Electric Road Systems a Feasibility Study Investigating a Possible Future of Road Transportation

Electric Road Systems A feasibility study investigating a possible future of road transportation

Archit Singh

Master of Science Thesis EGI_2016-090 MSC KTH Sustainable Energy Engineering Energy and Environment SE-100 44 STOCKHOLM

Master of Science Thesis EGI_2016-090 MSC

Electric Road Systems

A feasibility study investigating a possible

future of road transportation

Archit Singh

Approved Examiner Supervisor 2016-10-10 Hatef Madani Björn Hasselgren

Company Supervisor Contact person Gunnar Asplund Björn Hasselgren

Abstract The transportation sector is a vital part of today’s society and accounts for 20 % of our global total energy consumption. It is also one of the most greenhouse gas emission intensive sectors as almost 95 % of its energy originates from petroleum-based fuels. Due to the possible harmful nature of greenhouse gases, there is a need for a transition to more sustainable transportation alternatives. A possible alternative to the conventional petroleum-based road transportation is implementation of Electric Road Systems (ERS) in combination with electric vehicles (EVs). ERS are systems that enable dynamic power transfer to the EV's from the roads they are driving on. Consequently, by utilizing ERS in combination with EVs, both the cost and weight of the EV-batteries can be kept to a minimum and the requirement for stops for recharging can also be eliminated. This system further enables heavy vehicles to utilize battery solutions.

There are currently in principal three proven ERS technologies, namely, conductive power transfer through overhead lines, conductive power transfer from rails in the road and inductive power transfer through the road. The aim of this report is to evaluate and compare the potential of a full- scale implementation of these ERS technologies on a global and local (Sweden) level from predominantly, an economic and environmental perspective. Furthermore, the thesis also aims to explore how an expansion of ERS might look like until the year 2050 in Sweden using different scenarios. To answer these questions two main models (global and Swedish perspective) with accompanying submodels were produced in Excel.

The findings show that not all countries are viable for ERS from an economic standpoint, however, a large number of countries in the world do have good prospects for ERS implementation. Findings further indicated that small and/or developed countries are best suited for ERS implementation. From an economic and environmental perspective the conductive road was found to be the most attractive ERS technology followed by overhead conductive and inductive road ERS technologies. The expansion model developed demonstrates that a fast expansion and implementation of an ERS-based transportation sector is the best approach from an economical perspective where the conductive road technology results in largest cost savings until 2050. i

Examensarbete EGI_2016-090 MSC

Elektriska vägsystem

Genomförbarhetsstudie kring en möjlig

framtid för vägstransport

Archit Singh

Godkänt Examinator Handledare 2016-10-10 Hatef Madani Björn Hasselgren Företagshandledare Kontaktperson Gunnar Asplund Björn Hasselgren

Sammanfattning Transportsektorn är en viktig del av dagens samhälle och står för 20% av den totala globala energiförbrukningen. Det är också en av de sektorer med mest växthusgasutsläpp, där nästan 95% av energin härstammar från petroleumbaserade bränslen. På grund av växthusgasers potentiellt skadliga karaktär finns det ett behov för en övergång till mer hållbara transportmedel. En möjlig alternativ till den konventionella petroleumbaserade vägtransporten är implementering av elektriska vägsystem (ERS) i kombination med elfordon. Elektriska vägsystem är system som möjliggör dynamisk kraftöverföring till fordon från vägarna de kör på. Sålunda kan man genom att använda ERS i kombination med elbilar, minimera både kostnaden och vikten av batterierna samt även minska eller eliminera antalet stopp för omladdningar. Dessutom möjliggör detta system att även tunga fordon kan använda sig av batterilösningar.

Det finns för närvarande i princip tre beprövade ERS-tekniker, nämligen konduktiv kraftöverföring genom luftledningar, konduktiv kraftöverföring från räls i vägen och induktiv kraftöverföring genom vägen. Syftet med denna rapport är att utvärdera och jämföra potentialen för en fullskalig implementering av dessa ERS-teknik på en global och lokal (Sverige) nivå från, framförallt, ett ekonomiskt- och ekologiskt perspektiv. Rapporten syftar också till att undersöka, med hjälp av olika scenarier, hur en utbyggnad av ERS i Sverige skulle kunna se ut fram till år 2050. För att besvara dessa frågor producerades två huvudmodeller (global och lokal perspektiv) med kompletterande undermodeller i Excel.

De erhållna resultaten visar att ERS inte är lönsamt ur ett ekonomisk perspektiv i precis alla de undersökta länder, dock har ett stort antal länder i världen visat sig ha goda förutsättningar för ERS. Vidare visar resultaten att små och/eller utvecklade länder är bäst lämpade för ERS. Ur ett ekonomiskt- och ekologiskt perspektiv har konduktiv kraftöverföring från räls i väg tekniken visat sig vara den mest attraktiva, följt av konduktiv kraftöverföring genom luftledningar och induktiv kraftöverföring genom väg teknikerna. Expansionsmodellen som utvecklats visar att en snabb expansion och implementation av en ERS-baserad vägtransportsektor är det bästa alternativet, där tekniken för konduktiv kraftöverföring från räls i väg ger de största kostnadsbesparingar fram till 2050. ii

Acknowledgements I would like to express my upmost gratitude towards Elways and the department of Energy Technology at the Royal Institute of Technology for supporting and enabling this master thesis. Especially, I would like to thank my supervisor Gunnar Asplund at Elways who gave me the inspiration for the master thesis and has since helped and supported me meticulously throughout the process. I am also very grateful toward my supervisor Björn Hasselgren at the Royal Institute of Technology for being a great mentor, contributing with insightful inputs and active engagement during my thesis. Furthermore, I would like to give a special thanks to my examiner Hatef Madani for his support and guidance during the thesis. Last but not least, I would like to extend my appreciation to my fellow students Mårten Lundqvist, Martin Isacsson and Eric Schmidt for their perceptive feedbacks while proof reading the thesis report.

Archit Singh

Stockholm, August 2016

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Table of Contents ABSTRACT ...... I SAMMANFATTNING ...... II ACKNOWLEDGEMENTS ...... III LIST OF TABLES ...... VI LIST OF FIGURES ...... VII NOMENCLATURE AND ABBREVIATIONS ...... IX 1 INTRODUCTION...... 1

1.1 BACKGROUND AND PROBLEM DESCRIPTION ...... 1 1.2 PURPOSE...... 3 1.3 METHOD ...... 3 1.4 DELIMITATIONS ...... 4 1.5 ASSUMPTIONS...... 5 1.5.1 Model 1 – World ...... 7 1.5.2 Model 2 – Sweden ...... 8 2 FRAME OF REFERENCE ...... 10

2.1 ELECTRIC ROAD SYSTEMS ...... 10 2.1.1 Overhead Conductive Transmission Technology ...... 11 2.1.2 Conductive Power Transfer from Road ...... 14 2.1.3 Inductive Power Transfer from Road ...... 16 2.1.4 Stakeholders ...... 18 2.2 ELECTRIC VEHICLES ...... 20 2.2.1 Batteries ...... 22 2.2.2 Fast Chargers ...... 24 2.3 ROAD NETWORK IN THE WORLD ...... 25 2.4 FUELS ...... 27 2.4.1 Gasoline ...... 27 2.4.2 Diesel ...... 28 2.5 THE ELECTRICITY MARKET ...... 29 2.5.1 Global Electricity Mix ...... 30 2.5.2 Nordic Electricity Mix...... 31 3 MODELS ...... 33

3.1 MODEL 1 – WORLD ...... 34 3.1.1 Computing the Optimal Electrified Road Length ...... 34 3.1.2 Comparison – Petroleum-based Road Transport against ERS and EV Combination ...... 41 3.1.3 Comparison – Pure Battery Electric Car Fleet against ERS and Electric Car Combination ...... 43 3.2 MODEL 2 – SWEDEN ...... 45 3.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV Combination ...... 45 3.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination 47 3.2.3 Submodel 3 – Finding the Breakeven Point ...... 47 3.2.4 Submodel 4 – Expansion ...... 48 4 RESULTS ...... 54

4.1 PROSPECT OF ELECTRICAL ROAD SYSTEMS IN THE WORLD ...... 54 iv

4.1.1 Comparison – Petroleum-based Road Transport against ERS and EV Combination ...... 54 4.1.2 Comparison – Pure Battery Electric Car fleet against ERS and Electric Car Combination ...... 59 4.2 PROSPECT OF ELECTRICAL ROAD SYSTEM IN SWEDEN ...... 61 4.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV Combination ...... 61 4.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination 62 4.2.3 Submodel 3 – Finding the Breakeven Point ...... 64 4.2.4 Submodel 4 – Expansion ...... 66 5 DISCUSSION ...... 70

5.1 PROSPECT OF ELECTRIC ROAD SYSTEMS COMPARED TO THE CONVENTIONAL ROAD TRANSPORTATION SYSTEM ...... 70 5.1.1 The Global Perspective ...... 70 5.1.2 The Swedish Perspective...... 71 5.1.3 Validity and Limitations of the Model ...... 72 5.2 COMPARING A PURE BATTERY ELECTRIC CAR FLEET AGAINST AN ERS BASED FLEET ...... 73 5.2.1 Global and Swedish Perspective ...... 73 5.2.2 Validity and Limitations of the Model ...... 74 5.3 BREAKEVEN POINT AND EXPANSION ...... 75 5.3.1 Breakeven Point ...... 75 5.3.2 Expansion Scenarios ...... 76 5.3.3 Validity and Limitations of the Model ...... 76 5.4 DISCUSSING THE ELECTRIC ROAD SYSTEM TECHNOLOGIES ...... 77 5.5 IMPLICATIONS FOR GOVERNMENTAL POLICIES AND STRATEGIES ...... 79 6 CONCLUSIONS ...... 80 7 RECOMMENDATIONS FOR FUTURE WORK...... 82 8 REFERENCES ...... 83 9 APPENDIX ...... 89

9.1 APPENDIX A – ASSUMPTIONS...... 89 9.1.1 Overall Assumptions ...... 89 9.1.2 Model 1 – World ...... 89 9.1.3 Model 2 – Sweden ...... 90 9.2 APPENDIX B – COMPARISON BETWEEN PETROLEUM-BASED ROAD TRANSPORT VS ERS AND EV COMBINATION ...... 91 9.3 APPENDIX C – COMPARISON BETWEEN A PURE BATTERY ELECTRIC CAR FLEET VS ERS AND ELECTRIC CAR COMBINATION ...... 97

List of Tables Table 1. Yearly vehicle kilometer for all vehicles per road category during 2015...... 49 Table 2. Yearly vehicle kilometer for heavy-duty trucks per road category during 2015...... 49 Table 3. Expansion specifications for each scenario...... 53 Table 4. Most important global results for each ERS technology in countries where the yearly savings are positive, assuming 2015 battery pricing...... 55 Table 5. Most important global results for each ERS technology in countries where the yearly savings are positive, assuming future battery pricing...... 55 Table 6. The cost difference in percent between the two cases for all 184 countries assuming 2015 battery pricing...... 59 Table 7. The cost difference in percent between the two cases for all 184 countries assuming future battery pricing...... 59 Table 8. Number of countries where the ERS solution is cheaper and the cost difference between the two cases assuming 2015 battery pricing...... 59 Table 9. Number of countries where the ERS solution is cheaper and the cost difference between the two cases assuming future battery pricing...... 60 Table 10. The most important results obtained for the three ERS technologies in Sweden assuming 2015 battery pricing...... 61 Table 11. The most important results obtained for the three ERS technologies in Sweden assuming future battery pricing...... 61 Table 12. The cost difference between the two cases for the two examined ERS technologies assuming 2015 battery pricing...... 62 Table 13. The cost difference between the two cases for the two examined ERS technologies assuming future battery pricing...... 62 Table 14. The pros and cons of each technology attained in the study from an economic, environmental and aesthetic perspective...... 78 Table 15. Countries where the yearly savings from an ERS based transportation system are positive using the current battery price ((Conductive Road Case)...... 91 Table 16. Countries where the yearly savings from an ERS based transportation system are positive using the future battery cost (Conductive Road Case)...... 94 Table 17. A list with countries where the ERS plus battery combination is cheaper than a pure battery based passenger car fleet...... 97

List of Figures Figure 1. The main steps of the method used are shown in the figure. The writing of report was done continuously throughout the project...... 3 Figure 2. Principal design of an ERS. (Source: [10]) ...... 11 Figure 3. The picture illustrates a truck driving on an electrified road using overhead lines. (Source: Scania [22]) ...... 12 Figure 4. Example of an intelligent pantograph developed by Siemens. (Source: [23]) ...... 13 Figure 5. Siemens eHighway system test track in Groß Dölln, Germany. (Source: [24]) ...... 13 Figure 6. The picture shows how the electrical power is fed from the grid to the vehicle. (Source: [30]) ...... 15 Figure 7. Cross section of the road with a rail in each half and cables buried outside the roadway. (Source: [30]) ...... 15 Figure 8. The figure illustrates the principle behind the inductive power transfer from road technology. (Source [37]) ...... 16 Figure 9. This figure illustrates the basis of the primove Highway architecture. (Source [8]). .... 17 Figure 10. The major stakeholders in the ERS. (Source [8]) ...... 18 Figure 11. A Thomas Parker’s electric car from the 1880s. (Source: [39]) ...... 20 Figure 12. Worldwide number of electric vehicles in use from 2012 to 2015. (Source: [41]) ..... 21 Figure 13. Estimated Costs of EV Batteries through 2020. (Source: [54]) ...... 23 Figure 14. The charging profile of a Tesla Supercharger. (Source: [60]) ...... 24 Figure 15. The major road network in orange in United States...... 25 Figure 16. The major road network in orange in parts of Europe...... 26 Figure 17. The major road network in orange in Scandinavia...... 26 Figure 18. The world transportation consumption by fuel. (Source: [64]) ...... 27 Figure 19. The cost development of gasoline in Sweden excluding VAT from the year 1981 – 2015. (Source: [69]) ...... 28 Figure 20. The cost development of diesel excluding VAT in Sweden between the years 2001 to 2015. (Source: [69]) ...... 29 Figure 21. World total final energy usage by source for 2013. (Source: [73]) ...... 29 Figure 22. World electricity generation from all energy sources in 2014. (Source: [78]) ...... 30 Figure 23. Electricity prices in U.S. dollar cents per kilowatt hour excluding taxes. (Source: [79]) ...... 31 Figure 24. Power production by source in the Nordic region in 2013. (Source: [82]) ...... 32 Figure 25. An illustrative flow chart over the two models with accompanying submodels produced to investigate the thesis objectives...... 33 Figure 26. Example of how a sectioning of Germany into quadratic road sections with ERS installation can be conceptualized (Note that the meshed road sections would in reality only be on the land area)...... 35 Figure 27. Viewing one square in the quadratic grid-mesh...... 36 Figure 28. Explanation of why the circumferential length of a square in a large grid-mesh was approximated as 2x...... 36 Figure 29. Cost development as a function of length x for battery, ERS installation and the combination of these two factors...... 39 Figure 30. Vehicle kilometer as a function of road length...... 50 Figure 31. Traffic intensity as a function of road length...... 50 vii

Figure 32. Number of countries where the yearly profit is positive for different ERS technologies and battery cost scenarios...... 56 Figure 33. Percentage of total number of vehicles worldwide in countries where the yearly saving is positive for different ERS technologies and battery cost scenarios...... 56 Figure 34. Percentage of total number of heavy vehicles worldwide in countries where the yearly saving is positive for different ERS technologies and battery cost scenarios...... 57 Figure 35. Percent of global total GHG emissions reduced by implementing the different ERS technologies in countries where the yearly savings are positive for different and battery cost scenarios...... 57 Figure 36. The percentage of the countries in the world/continents that are suitable for conductive power transfer from road solution assuming the current battery pricing of 350 USD/kWh...... 58 Figure 37. The percentage of the countries in the world/continents that are suitable for conductive power transfer from road solution assuming the predicted future battery pricing of 120 USD/kWh...... 58 Figure 38. A summarization of all the cost differences presented in the previous tables ...... 60 Figure 39. A summarization of the yearly savings or losses for each ERS technology and battery cost scenario...... 62 Figure 40. The number of cars required until an ERS based system is cheaper for the two examined ERS technologies assuming 2015 battery pricing...... 63 Figure 41. The number of cars required until an ERS based system is cheaper for the two examined ERS technologies assuming future battery pricing...... 64 Figure 42. The frequency breakeven points for inductive and conductive ERS technologies...... 65 Figure 43. The frequency breakeven point for overhead conductive ERS technology...... 65 Figure 44. Accumulated result for the three different scenarios for conductive road ERS technology...... 66 Figure 45. Accumulated result for the three different scenarios for overhead conductive ERS technology...... 67 Figure 46. Accumulated result for the three different scenarios for inductive road ERS technology...... 67 Figure 47. Accumulated result obtained from Scenario 1 for all ERS technologies...... 68 Figure 48. The yearly development of GHG emission reduction for the three different scenarios for conductive and inductive ERS technology...... 69

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Nomenclature and Abbreviations

Notations

Symbol Description

∆ Quadratic grid-mesh size in kilometer x Length of a side in kilometer of a square in the quadratic grid-mesh k1 The cost of battery per kilometer and car

2 k2 Cost per car per km grid B(x) Function of battery cost by distance (km) ERS(x) Function of ERS installation cost by distance (km) f(x) Combined function of battery and ERS installation cost by distance (km)

Abbreviations

WTW Well-to-wheel GHG Greenhouse gas emissions

CO2 Carbon Dioxide EU European Union MK1 Miljöklass 1 EV Electric Vehicle DC Direct current ERS Electric Road System ICE Internal Combustion Engine

1 INTRODUCTION

This thesis is the final assignment for the master Sustainable Energy Engineering given under the program Energy and Environment at KTH Royal Institute of Technology in cooperation with Elways.

The thesis examines the prospect of Electric Road Systems, abbreviated as ERS, to be a solution to our dependency on fossil fuels and thus the future of transportation. ERS are technologies that enable continuous electricity transfer to vehicles in motion. In this report, the potential of different ERS technologies are examined from a technical, economic and environmental standpoint from a global and a Swedish perspective.

1.1 Background and Problem Description The global energy usage has seen a constant increase since the industrial revolution and the trend is likely to continue for a foreseeable future. According to the U.S. Energy Information Administration, the world energy consumption is estimated to increase by 56 % until 2040 [1]. Transportation is a vital part of today’s energy-intensive society, and accounts for 20 % [2] of our global total energy usage. Furthermore, as almost 95 % of this energy originates from petroleum- based fuels, this results in a huge emission of greenhouse gases (GHG). In fact, in 2014, the transport sector alone accounted for approximately 14% of the total GHG emissions [3]. This development has compelled governments around the world to start setting goals to mitigate the increasing global pollution. For example, the Swedish Government has published a statement proposing a goal to make the Swedish transportation sector entirely fossil fuel neutral by the year 2030 [4]. Similarly, the European Union aims to have at least 10 % of the energy used in the transportation sector come from renewable energy sources by 2020 [5].

Although some difference of opinion and disputes remain regarding how dangerous greenhouse gases actually are for the environment, most experts agree that the peak-oil scenario is approaching and will eventually make fossil fuels too expensive to extract and use. As a result, it is of utmost importance that we as smoothly and quickly as possible transition to more sustainable fuels. Vehicles powered by electricity using batteries, also known as electric vehicles (EV), are an alternative to traditional petroleum-powered vehicles. EVs have experienced tremendous progress during the last decennium due to a number of reasons, with the foremost reasons being the recent rapid development in the battery technology and the situation in the world where we are trying to progressively move away from fossil fuels. Even though electric vehicles might be a good potential alternative to petroleum-based vehicles, there still are a number of challenges that need to be addressed before EVs can become competitive enough to achieve the required breakthrough.

One of the major limitations for electrical vehicles are the capabilities for the current battery technology. Despite the fact that considerable development has been made in recent times within the field, the energy density of batteries is still substantially lower than the energy density of petroleum-based fuels [6]. Likewise, the charging time required is a major challenge in making electric vehicles more commercially attractive. Even with the latest fast charging technology, it 1 still takes up to 40 minutes to charge a battery with less range than what a full tank of diesel can provide [7]. Moreover, if we also want to electrify heavy vehicles (e.g. trucks and buses), it will not, in the foreseeable future, be possible to run these solely on batteries since the energy storage capacity and the output power of batteries is not enough for long-distance transportation of heavy vehicles [8]. Hence, the main obstacle towards electrifying heavy vehicles is the size and weight required for on-board storage of electrical energy. As an example, a road truck weighing 40 tons travelling 1,000 kilometers would require batteries weighing approximately 20 tons. The batteries would in this case take up far too much of the cargo space and would thus make the heavy vehicles highly unprofitable. To make the EV less reliant on the battery, especially for long distance heavy transport, and at the same time reduce the vehicle cost, a possible solution could be to transfer power to the vehicle from the road.

ERS are systems that enable dynamic power transfer to the vehicles from the roads they are driving on. In an ERS based road transportation system, the largest roads would be electrified so that the bulk distance travelled by vehicles would be done using external electric power. The remaining distance outside the ERS network could be performed by for example either using an internal combustion engine (ICE) or by using (small) on-board batteries optimized for shorter routes. By utilizing ERS, both the cost and weight of the batteries could be kept to a minimum and the requirement for stops for recharging would also be eliminated since it would be possible to recharge while driving [8]. There are a number of ways and technologies available that can be utilized in transferring power from the roads to the vehicle. However, there are currently in principal three ERS technologies that have shown potential and are considered in the industry. These are more specifically the conductive power transfer through overhead lines, conductive power transfer from rails in the road and inductive power transfer through the road [9].

As the ERS industry is relatively new, not many research reports have as of yet been published in this field. Furthermore, the reports that have been published are usually limited to examining only one ERS technology and look at the potential of this technology on a specific road case. For example, Viktoria Swedish ICT has in collaboration with different stakeholders compiled a detailed evaluation of both the inductive road technology and the overhead conductive road technology ( [10], [8]). However, both these reports assess the potential of the technologies from only a heavy vehicle transportation perspective and include cost estimates for a full deployment of a road section between Stockholm and Gothenburg. Similarly, the report published by Andersson and Edfeldt [11] compares the potential of ERS heavy-duty trucks with conventional and hybrid heavy-duty trucks from a haulage contractor companies’ perspective by examining different road cases [11]. A couple of reports that aim to summarize the ERS concept and evaluate its potential have been published ( [12], [13], [14]). Moreover, reports that examine different business models and payment methods for ERS have also been produced ( [15], [16]).

Consequently, it was found that not much research existed which looked at a full-scale implementation of the different ERS technologies from a global but also a Swedish perspective and which compared the ERS technologies between each other. This thesis aims to therefore evaluate and compare the potential of a full-scale implementation of different ERS technologies in different countries of the world from predominantly, an economic and environmental perspective.

1.2 Purpose The purpose of this study is to investigate whether it is possible to replace the majority of the transport sector's dependency on petroleum-based fuels by introducing ERS. More specifically, the purpose is to: 1) Investigate the possibility of a full-scale implementation, predominantly from an economic standpoint, for different ERS technologies around the world. Subsequently, to identify the top countries/regions with the best and the worst prospects. 2) Examine the potential for implementation of ERS in Sweden and compare the different technologies available from a primarily economic and environmental perspective. Furthermore, this thesis explores using different scenarios, how an expansion might look like until the year 2050.

1.3 Method The thesis method is essentially split into four distinct parts, namely, literature review, modelling, analysis of findings, and report writing. This is illustrated using different subparts in Figure 1 where the writing process was continuous throughout all the parts. The process was performed iteratively in order to facilitate improvements continuously.

Litterature Review - Reports - Articles - Interviews

- Internet

Modelling Analysis - Excel - Evaluation and - Economic and improvement of environmental model findings - Scenario analysis - Sensitivity analysis

Figure 1. The main steps of the method used are shown in the figure. The writing of report was done continuously throughout the project.

As in most projects, the first step was to study relevant articles and reports in order to understand and create a broad picture about ERS and the surrounding topics. Early on, the scope and appropriate limitations were discussed and decided on together with supervisors at KTH and 3

Elways. Reports and articles were then collected from various different sources which included administrative authorities, ERS companies, and stakeholders within the ERS field just to name a few. Collecting information from different stakeholders in the field was done in order to capture different views and aspects of ERS technology. A number of interviews, both in person or through a communication medium i.e. telephone, were performed with relevant stakeholders. Open questions were mainly used to ensure that the responses were formulated by the interviewee him- /herself which promoted unbiasedness. Naturally, the questions were varied depending on the field of expertise of the person being interviewed. Both qualitative and quantitative data was acquired from these interviews. However, literature in the form academic reports, articles, web pages of companies, administrative authorities, etc., were mainly used as sources of information for the data used in the modelling process.

To be able to answer the proposed inquiries, the thesis was split into evaluating the ERS field using two different conceptual models. These models aimed to analyze and compare the prospect of different ERS technologies from a technical, economic and environmental standpoint for a global and a Swedish perspective. The methodological approach applied for each model was different. Therefore, the modelling procedures used for the two models are presented in more details in Chapter 3. All modelling was performed in the spreadsheet program Microsoft Excel. This program was chosen because of a number of reasons, firstly, it was found to give a great overview over the modelling process. It also made following equations step by step simpler. Moreover, adding/changing inputs and altering equations could also be accomplished smoothly. For each model, specific input data was collected which was mostly compiled from literature and through making various assumptions (See chapter 1.5 for further details).

During the course of the thesis, work was carried out in a close contact with both the supervisor at KTH and the supervisor at Elways. Both have contributed with feedback, information and quality checks. This close collaboration was upheld through frequent meetings held approximately every second week.

1.4 Delimitations Some overall limitations of the thesis are presented in this section. Additionally, other limitations are presented continuously in the report when judged necessary. The major limitations are:  The economic calculations made in this thesis only consider the techno-economic direct costs and are thus only a partial cost of the actual complete economic expenditures. For example, the cost for society in terms of lost jobs in the fossil fuel related markets are not considered.

 The operational costs are only considered in the expansion model (submodel 4) and are hence neglected in the remaining models.

 Only 184 countries could be studied in the global perspective model due to insufficient data being available for the remaining countries.

 The different alternatives of payment systems for ERS have not been investigated in this thesis.

 The probability that the electricity cost will most likely increase in the future due to wider implementation of renewable energy sources and possible imposition of new taxes is not considered.

 No speculation over the future price trend was made for gasoline and diesel, hence, contemporary prices were used for future scenarios as well.

 In the calculations, the cost of expanding the electric grid to enable connections to the electrified roads has been neglected. Furthermore, the power grid and the electricity distribution technologies required for ERS are not considered.

 The installation price of the different ERS technologies is kept the same as the current anticipated prices when modelling the future.

 Electricity is the only energy source for vehicles that has been compared as an alternative to fossil fuels. Hence, other potential alternative fuels such as biofuels, hydrogen fuel cells, etc. are not considered.

 Battery storage is the only hybridization alternative considered for electric vehicles in combination with ERS.

 The current models do not take into consideration that when vehicles are travelling on the electrified roads, the efficiency becomes higher than when utilizing the battery as energy source. This is due to the fact that the electric motor in the EVs receive the energy directly from the ERS, thus the efficiency loss obtained from battery storage is bypassed. This would in reality result in a lower energy consumption per kilometer for the vehicles while travelling on electrified roads.

 Any form of energy regenerative systems in the vehicles and/or in the ERS have been ignored.

1.5 Assumptions In this report certain assumptions and simplifications were made to enable various calculations and models, which otherwise would have made the problem excessively complex. Since the correct assumptions are a vital part of a well-conducted scientific report, each assumption was heavily scrutinized and studied before it was adopted. The overall major assumptions made for this thesis are presented in this section. Moreover, for each model, more specific key assumptions are presented in subsections. Finally, in Appendix C, some additional assumptions that were judged to be too detailed for the main report body are presented.

 In these models, motor vehicles were split into four main vehicle types. Namely, passenger cars, light-duty trucks, heavy-duty trucks and buses.  It was assumed that all the current motor vehicles only use either diesel or gasoline as fuel, thus vehicles that already use alternative fuels are disregarded in the models.  It was assumed that each country will phase out all the fossil fuel based vehicles by replacing them with only electric vehicles that use batteries as energy source.

 Three different ERS technologies were examined in this thesis, namely Elways conductive road technology, Siemens overhead conductive technology and lastly, the inductive road technology of Bombardier.  It was assumed that the conductive road and inductive ERS could be used by all vehicle categories. However, the overhead conductive ERS was presumed to only be used by heavy-duty trucks and buses, denoted together as heavy vehicles.  It was assumed that the road network in all countries can be approximated as quadratic grid-meshes. This assumption is not that far from reality where many countries have a well- connected road system in a meshed manner as shown in the frame of reference chapter about the road network in the world.  The cost of installing inductive road, overhead conductive and conductive from road technologies was assumed to be 15 MSEK/kilometer, 6 MSEK/kilometer and 4 MSEK/kilometer respectively. The lowest predicted cost for each technology was used, as a construction at such a huge scale would probably lead to the lowest prices due to economies of scale. In the case of inductive road technology, the cost per kilometer was assumed to be lower than what was found through the literature study. This was chosen by the author to fathom the potential of the technology in a best case scenario.  Cost of the pickup arm for conductive road power transfer has been estimated to be roughly 4500 SEK. According to Gunnar Asplund, this is a reasonable assumption as the prices would decrease considerably if the pickup arm was mass produced due to economies of scales. The cost of the pantograph for overhead conductive is assumed to be 55 000 SEK and the inductive pickup system is assumed to have the same cost as the conductive road technology. Naturally, this will result in an underestimation of the real cost as the pickup arm for conductive road power transfer is expected to be cheapest out of the two options. However, this method will still show the situation under a best case scenario for the inductive road technology.  The average fuel consumption for all the categories of vehicles, taking into account both diesel and petrol driven vehicles, was computed to be 0.1 l/km. On the other hand, the average fuel consumption for heavy-duty trucks and buses was calculated as 0.38 l/km.

 Due to the fact that the cost of diesel and gasoline have seen a sharp decrease in pricing during the last year. It was judged necessary to use an average of the price over some years. The price used in this model was the average gasoline and diesel price excluding VAT for the last five years in Sweden, taking into account the vehicle categories, and was computed to be 5.8 SEK/l. Even though the Swedish gasoline and diesel price is a bit on the expensive side when considering a global perspective. It was concluded that the assumption still gave adequately accurate output for a global perspective. The diesel price used for the heavy transports was set as 5.6 SEK/l.  Additionally, the combined average emission from diesel and gasoline for all the vehicle categories was computed to be 2747 g CO2e/l. Whereas, the emission from diesel was set to be 2820 g CO2e/l.  For an average electric car, the energy usage used in this report was 0.16 kWh/km. This was estimated by comparing multiple factual sources. The average electricity usage

considering all the vehicle categories was estimated to be 0.24 kWh/km. Additionally, the average electricity usage for heavy vehicles was calculated to be 1.06 kWh/km.  The technical lifespan of ERS, batteries and pickup apparatuses are assumed to be 20, 8 and 15 years respectively.  To simplify calculations, the exchange rate for 1 USD was set to a constant value of 8 SEK. Similarly, 1 EUR was assumed to be have an exchange rate of 9 SEK.  Cost of battery in 2015 is assumed to be 350 USD/kWh and battery prices in the future (after 2020) as 120 USD/kWh.  The real interest rate has been assumed to be 4 % when using the fixed-rate mortgage method as used by Sweco and the Swedish Transport Administration [17].

1.5.1 Model 1 – World This model was developed with the aim to get a general idea about which countries and regions in the world that are the most attractive for ERS solutions. The main questions that the model targeted to answer were: If a full-scale implementation of ERS was performed globally in combination with electric vehicles, which countries would currently be the most appealing from primarily an economic standpoint. Also, comparing the cost of implementing a full-scale ERS for passenger cars against the cost of converting all passenger cars to pure battery electric cars, which alternative would be the most economically beneficial? To be able to answer these questions a number of additional assumptions were found necessary to be made and the most important of these are presented below. Specific assumptions for each submodel are reported under subheadings.

 It was assumed that the ERS solutions are already completely implemented in the countries and thus, the building phase was not considered. Instead a steady-state case was estimated where the yearly costs and savings were analyzed.  As the exact distribution for the different motor vehicle types could not be acquired for each country, the division of the four categories of vehicles for Sweden and their percentile of vehicle kilometer was assumed to be the same for all the countries in the world. Consequently, this resulted in the fact that the average distance travelled, fuel consumption, electricity consumption, fuel price and emission was assumed to be the same as for Sweden presented in Model 2. This assumption has been made to facilitate the computations as not enough reliable data could be acquired regarding the exact distribution of the different categories of vehicles in each country.  Only the land area of the studied countries was used where land area is defined as the sum of all surfaces delimited by international boundaries and/or coastlines, excluding inland water bodies (lakes, reservoirs, rivers).  To ensure that the EVs would have sufficiently big batteries and thus would not run out of charge, it was assumed that the batteries should be able to drive at least the length of a side of a quadratic grid-mesh, which are the electrified roads.  0.7 SEK/kWh, which is the rough median of the global electricity price, was used as the average global electricity cost as it was judged to be more accurate than the mean. Additionally, the average global electricity emission used in the model was 500 g CO2e/kWh.

Comparison – Petroleum-Based Road Transport against ERS and EV Combination  The cost of a conventional petroleum-based vehicle is estimated to be the same as for an electric vehicle without the battery and the pick arm apparatus that connects to the electrified roads. This has been computed by comparing the current vehicle prices.  As a result, the yearly cost of an ERS based transport sector is assumed to be the annual cost based on life expectancy of the electrified roads, the new batteries, the new pick up arms and the electricity consumption from all the electric vehicles.  The total yearly cost of the current petroleum-based transport system was assumed to be the cost of fuel used by the vehicles.

Comparison – Pure Battery Electric Car Fleet against ERS and Electric Car Combination  In this model, only the passenger cars were examined. Thus, only the conductive road and inductive road electric road technologies are considered.  It was assumed that the cost of electric passenger cars without the battery is always the same, regardless of the size of the battery.  The yearly cost of a pure battery electric car based fleet was assumed to be the yearly battery plus the infrastructure costs.  The cost of the two ERS technologies was assumed to be 1/5 of the normal price, due to the fact that the ERS would be dimensioned for only passenger cars and thus would require lower loads. This estimation was made by Elways [18].  The acceptable driving range that an electric car needs to travel to be a viable competitor against conventional vehicle was assumed to be a bit lower than the average driving distance for conventional vehicle, which is roughly 600 kilometers. Therefore, for this model, the minimum acceptable driving range for an electric passenger car was assumed to be 500 kilometers.  It was assumed that the only infrastructure investment for a pure battery electric car fleet would be fast chargers.  When it comes to the cost for a fast charger, it was found that the average cost of building a Tesla supercharging station with five Superchargers is 137 500 USD which translates to that each supercharger/fast charger would cost roughly 220 000 SEK.

1.5.2 Model 2 – Sweden The goal of this model was to further investigate how the prospect for the different ERS technologies is in Sweden. This was done by developing four different submodels that examined the problem from different perspectives. The first two models were the same as in Model 1, with chief difference that the input data was optimized for Sweden. The third submodel aimed to investigate the frequency of electric vehicles per day needed on an electrified road section that would result in a repayment of the investment. Finally, the last model intended to explore how an expansion and implementation of ERS could look like in the future. For each model, some assumptions were found necessary to be made and the most important ones are presented in this

8 chapter. Furthermore, the chief specific assumptions for the last two submodels are reported under the coming subheadings.  The values from 2015 were used for population, land area, total GHG emissions, yearly GDP and electricity consumption.  The cost of Nordic electricity mix for the year of 2013 has been assumed, which was 0.355 SEK/kWh.  The input for number of vehicles, including all the vehicle categories, in Sweden year 2015 was found to be roughly 5.9 million. The number of heavy vehicles in Sweden year 2015 was estimated to be about 114 500.

Submodel 3 – Finding the Breakeven Point  The daily investment cost per kilometer for conductive road, overhead conductive and inductive road electric road technologies were found to be 818 SEK/day, 1226 SEK/day and 3066 SEK/day respectively and was computed by using the fixed-rate mortgage method.

Submodel 4 – Expansion  The value for the total kilometer of electrified road needed for a full-implementation for the three different ERS technologies was assumed to be the values computed using Model 1 for future battery price.  For this model it was assumed that the most heavily trafficked road sections would be the first to be electrified as they would lead to the largest cost savings.  The model considered the time period from 2016 to 2050. It was assumed that a full implementation of ERS and a full electric-based transport sector would be attained until, at the latest, 2050.  The total yearly vehicle kilometer, new vehicles per year and the total number of vehicles are assumed to always be constant and have the same values as in 2015. These assumptions were found to be essential in producing the functions relating vehicle kilometer and vehicle frequency with road length.  The electricity and fuel prices were presumed to be constant, thus no prediction of the future cost development was made.  The average fuel consumption, average electricity consumption, average fuel and electricity emissions were assumed to be constant.  The cost for road maintenance was assumed to be 60 SEK/km/day for all ERS technologies. This cost was obtained from Elways [18], and is derived from the road maintenance cost of regular roads.  The cost of electrifying the roads for each technology was assumed to be 2.5 times more expensive than the normal cost for the first 100 km and 1.5 times more expensive for the first 1000 meters. This was done to mimic that the initial installments would be more expensive compared to later ones due to economy of scales.

2 FRAME OF REFERENCE

This chapter gives a broad picture of the current knowledge in the field of ERS and other relevant areas. The information presented in this chapter is the base for the modelling and is used as input parameters for the different models.

2.1 Electric Road Systems Electric Road Systems (ERS) are in principle electrified roads that are able to perform dynamic power transfer to the vehicles on the road. Normally an electric engine receives power from an external power source which has been integrated into the surface of the road. The transmission of the electrical power is made while the vehicles are in motion in a similar fashion as that for trolley buses, where the main difference is that ERS-vehicles can connect and disconnect from the power supply while in motion. Consequently, the roads with installed ERS can be used by both conventional fossil fuel based vehicles as well as ERS-vehicles. To make ERS-vehicles more flexible they are often equipped with batteries or smaller internal combustion engines (ICE) so that they also can be used on conventional roads [10].

The key actors in the industry believe that the ERS technology is technologically feasible and could be a potential solution in reducing society’s fossil fuel dependency and thus also the emissions in the transportation sector [10]. Though there are disagreements among experts over how long a full transition to ERS will take to be realized (anything between 10 to 50 years has been proposed), there is a consensus that this switch is possible to make. Naturally, the change is expected to take place progressively, starting with smaller demonstration projects and closed road systems to gradually incorporating major national and international highways. Several demonstration projects have already been initiated around the world to explore and evaluate the different ERS technologies and their full-scale commercial prospective [10]. Even though a deployment of ERS for road transportation will require huge investments in the infrastructure, a full-scale implementation could lead to significant advantages compared to the currently existing fossil fuel dependent transportation system.

One of these advantages obtained would be reduced operation costs due to ERS being more energy efficient and electricity being cheaper than fossil fuels. Additionally, the electric engines would reduce the noise generated, which would allow the heavier vehicles to run during off-traffic hours. Consequently, this would result in decreased congestion and a balancing of the energy demand. There is also a potential that as electric engines are simpler and lighter than traditional internal combustion engines, the vehicle maintenance costs would experience a reduction [8].

Figure 2. Principal design of an ERS. (Source: [10]) There are essentially three physical ways that a vehicle can be charged while being on the road, namely from above, the side or from below. Unfortunately, charging from the side is considered to be too dangerous for pedestrians and bicyclists due to the fact that an arm apparatus would need to stick out from the side of the vehicle, this approach is not considered as an alternative [19]. As a result, there are currently in principal three technologies of ERS that are considered in the industry, namely, conductive power transfer through overhead lines, conductive power transfer from rails in the road and inductive power transfer through the road [9]. In the coming chapters these technologies are presented in more detail.

2.1.1 Overhead Conductive Transmission Technology The overhead contact line technology has existed for many years and is conductive based; in fact it is the same technology that is used in today’s trains, trams and trolleybuses. Simplified, in such a system, electricity is continuously transferred from the overhead lines to the vehicle through a so called pantograph. A pantograph is a component that connects the overhead lines to the vehicle and can in such way transfer electricity between the overhead line and the vehicle. Along the roads there are electricity pylons that support the electricity wires. The systems using overhead contact lines today are commonly closed systems. They usually involve vehicles that travel along a pre- set path while continuously been connected to the overhead lines and in most cases also are connected to a rail in the ground [20].

However, when it comes to overhead contact lines for ERS-vehicle applications, it is of utmost importance that the vehicle should be able to connect to and disconnect from the overhead lines while moving. Once an ERS-vehicle is disconnected it would automatically switch to a secondary source for propulsion energy such as a hybrid-diesel engine or batteries [21]. At present, the technology with overhead contact lines is only supported for large vehicles such as trucks and hence cannot be used by passenger cars and other low vehicles [13]. This is mainly due to the fact that height regulations state that the electrified overhead lines on roads need to be above a certain height from the ground. For example, the Swedish National Electrical Safety Board claim that high voltage wires need to be located at least 6 meter and low-voltage 5.1 meter above the ground due to safety reasons. Therefore, using the overhead technology for passenger cars would require the cars to be equipped with a pickup arm of several meters which would not only be technologically challenging but a huge safety hazard [20].

Figure 3. The picture illustrates a truck driving on an electrified road using overhead lines. (Source: Scania [22]) Traditionally, when the overhead transmission technology is used for trams and trains, the rail is used to handle the return circuit. But as the ERS lack rails, a two-pole system is needed to be implemented so that both the power in-feed as well the out-feed can be managed. With this system it would also be possible to feed the regenerative braking power from the trucks back to the overhead line and into the energy grid so that it can be used by other vehicles in the system. The specially designed overhead contact lines are made in a way that ensures that secure energy supply can be performed for speeds up to 90 km/h [21].

Active Pantograph The overhead transmission line technology that will be used for the ERS will need to be dynamic and intelligent, as the vehicles travelling on the electrified roads will need to be able to perform a number of complex procedures. This involves for example overtaking other vehicles, passing under bridges and driving over non-electrified parts of the road network, exiting highway etc. Not to mention the fact that as the vehicles on an electrified road system would not be travelling on a fixed rail, there would be some lateral displacement of the vehicle on the road as it would be impossible to travel in a perfectly straight line. Naturally, it is thus of paramount importance to design a pantograph that is flexible and can intelligently handle different kind of traffic situations without problems [20]. Such an intelligent pantograph is required to enable continuous electricity transmission between the overhead line and the vehicle even during high travelling velocities and when the vehicle is somewhat laterally displaced compared to the transmission line. It also has to be able to handle vertical changes in the form of bumps or the road being raised due to frozen ground. Another important aspect of such a pantograph is the need of an automatic search system that can find the overhead lines when they are available. Several scanning technologies are available or are being developed to meet this need. It has been suggested that as a traditional pantograph cost somewhere between 30 000 – 80 000 SEK it is not unlikely to propose that more intelligent pantograph will have a price range between 120 000 – 240 000 SEK [20].

Even though several companies in the industry have developed overhead conductive technologies, in this thesis, only the Siemens eHighway overhead conductive concept is studied in detail. 12

Figure 4. Example of an intelligent pantograph developed by Siemens. (Source: [23])

Siemens – eHighway Siemens has developed the eHighway system that uses the overhead transmission line technology. It enables hybrid trucks to travel using electricity from overhead contact lines via an active pantograph, which makes it possible for the trucks to disconnect and connect at speeds up to 90 km/h. Due to the fact that direct transmission is used, it permits the system to have an 80-85 % well-to-wheel (WTW) efficiency, which is twice as high as that for conventional diesel engine. The eHighway system also makes it possible to regenerate the breaking energy which further increases the system WTW efficiency, lowers the emissions and the energy costs [23].

The active pantograph developed for the eHighway system is considered to be one of the key innovations. Apart from enabling the vehicles to connect and disconnect from the overhead lines, it furthermore has a specially designed sensor technology that permits the pantograph to automatically adjust its position to compensate for any lateral movement of the truck compared to the contact lines. This contrivance also minimizes the wear induced by the pantograph and thus possibly enables a longer lifecycle [23].

Figure 5. Siemens eHighway system test track in Groß Dölln, Germany. (Source: [24])

For the sections on the roads that are not electrified with overhead contact lines, the eHighway adapted trucks are able to switch to a hybrid drive. There are no restrictions with regards to the type of hybrid drive that can be used by the trucks. Serial and parallel concepts with internal combustion engines, battery solutions, fuel cells etc. are all possible to implement [25].

Since the technology is not yet in commercial use, it is difficult to estimate the investment costs for the technology. However, the consulting firm, Grontmij, stated in their report from 2012 for the Swedish Transport Administration and the Swedish Energy Agency that the cost of building such a system would be around 10 million SEK per kilometer. The Swedish Transport Administration made its own estimation in 2012 that the cost would be somewhere around 6-18 million SEK per kilometer, where electrification at a larger scale would result in costs in the lower end of the span [26].

2.1.2 Conductive Power Transfer from Road The conductive power transfer from the road is a newer technology compared to that of overhead contact lines. It was the French company Alstom with their conductive road transfer technology, Aesthetic Power Supply (APS), who opened the first tramway system in Bordeaux (2003) which used conductive road transfer technology. Not only did the APS technology prove that conductive road transfer is possible, but also exhibited that the technology was safe to use and more aesthetically pleasing according to some then compared to traditional overhead contact lines. Since then several cities around the world have incorporated the APS system [27]. In 2015 Alstom launched SRS, which is an innovative ground-based static charging system for both trams and electrical buses based on the proven APS technology. Alstom has furthermore been working in collaboration with Volvo on an ERS that allows continuous conductive power transfer from road for trucks [28]. The Swedish company Elways has also developed its own technology of conductive power transfer from road known as Elways. Unlike the ERS developed by Alstom and Volvo which is designed only for trucks, Elways has instead developed a system that can be utilized by all types of vehicles [18] . In this thesis, only the conductive road transfer technology developed by Elways is examined due to time and data restrictions and as such other companies’ conductive road transfer solution are not considered.

Elways In summary, Elways technology enables electricity transmission from the power grid to rails in the road. This means that there is a need for a current collector or pickup arm to connect the rails in the road to the vehicles. To increase the overall safety for humans and animals, the power supply rails is segmented, similarly to the APS system. For Elways technology, a distance of 50 meters is electrified at a time. This segment is fed with electricity from a low voltage AC cable with a voltage of 800 V which is positioned in the close vicinity of the road. The electricity input to the rails in the road is in turn made via a fast switch box located in the ground. This low voltage cable is then connected to a medium voltage cable (24 or 36 kV) which is placed next to the low voltage cable or further away from the road area. The transmission between the medium voltage cable and the low voltage cable occurs through a transformer station which is positioned every one or two kilometers. Lastly, the medium voltage cable is connected to the high voltage grid via a transformer station located every 50 kilometer or so [29].

Figure 6. The picture shows how the electrical power is fed from the grid to the vehicle. (Source: [30]) To minimize the risk of accidents due to the high voltages in the rails, Elways have positioned the conductors in trenches below the surface of the road. Hence, making it near impossible for humans or animals to reach the electrified part while walking over the rails. Furthermore, as the rails are only energized in segments of 50 meters at a time, the risk of getting electrocuted is reduced even further. The system also only supplies electricity to vehicles that are travelling over a certain velocity; this means that when a vehicle stops the current is likewise turned off. Another factor that can be a problem for ground based conductive rail systems is the weather. Elways have tested and proven that small objects such as stones, snow and rain can all be rinsed off the tracks automatically, using patented solutions, when the traffic intensity is sufficiently high [31]. Several tests have been performed in all weathers at Arlanda (outside Stockholm) test tracks which show that the system works satisfactorily [32]. A special add-on plough for a plough car has been proposed which can take care of the ice and snow in the rails during extreme winter conditions. Moreover, a heating system in the rails can also be implemented to eliminate this problem [31].

Figure 7. Cross section of the road with a rail in each half and cables buried outside the roadway. (Source: [30]) As with the overhead contact line technology, a key technology in conductive road transmission is the electricity pickup that is to be used by the vehicles. The pickup arm developed by Elways has a flexible mechanical construction that enables it to follow the rail even when the vehicle is not perfectly aligned to the rails. In addition, the pickup arm has a sensor that allows it to automatically connect or disconnect from the rail when a rail is available or while the vehicle is overtaking [33]. Transmission of power from the track to the car through the pickup has been performed for speeds up to 50 km/h. The pickup arm is estimated to cost around 5000-10 000 SEK and will be constructed to have a lifespan similar to that of vehicles. The system has been proven 15 to be able to conduct voltage and current that together correspond to 200 kW per track, which is the power necessary to charge trucks [34]. The lifespan of the contacts in the rails are estimated to have a technical lifespan of approximately 20 years depending on the traffic intensity [18]. Higher intensity would naturally lead to more frequent replacement of the rail.

Elways has stated that the cost of a large-scale expansion, more than 1000 kilometers, will cost around 4-5 million SEK per kilometers. It has also been estimated that the construction of the first 100 kilometers will cost 7 to 10 million SEK per kilometers. Additionally, the cost of road maintenance in the form of cleaning of the rails from dirt, water and snow has been predicted to be about 60 SEK per day and kilometers [29]. Gunnar Asplund has stated that the well-to-wheel efficiency of the Elways system will be somewhere between 85-95 % depending on the choice of the voltage and the quality of the electrical components [35].

2.1.3 Inductive Power Transfer from Road Inductive ERS use induction principles to transfer electricity wirelessly to moving vehicles, which means that no mechanical contact is required [36]. Simplified, the inductive power transfer technology utilizes the AC transformer principle. Figure 8 can be used to describe this principle. Typical AC transformers, which commonly are used in power distribution systems, have laminated iron core that lead the magnetic flux from a primary winding through a secondary winding with miniscule efficiency loss due to loss and leaking. This is displayed in picture A. However, to enable the inductive power transfer technology, the core is split into two separate parts as shown in picture B. Consequently, the inductive power transfer by road technology works by having the secondary winding of the transformer placed in the vehicle while the primary winding is elongated and installed into the road. This allows magnetic flux to be transferred between the road and the vehicle, thus enabling continuous power transfer as shown in picture C [8]. To allow the transmission, also this solution requires a type of pick-up arm, which corresponds to the second side of the transformer as exemplified in picture C.

Figure 8. The figure illustrates the principle behind the inductive power transfer from road technology. (Source [37])

Today, there are a number of different inductive electric road technologies that have been developed by enterprises all over the world. Korea is one of the countries that have invested most heavily into developing the inductive power transfer technology. The KAIST online electric vehicle project, also known as OLEV, is a well-known inductive road transfer technology that has been under development for many years. However, in this thesis, only the Bombardier’s primove Highway inductive technology has been considered and further details of this technology is presented in the following section.

Primove Highway – Bombardier The primove Highway system utilizes inductive charging and the primary windings are embedded into the road in segments of 20 meters. The windings are thereafter covered with asphalt to ensure that no cables or connections are exposed. The segments are only energized when a certified primove vehicle transmits proper code to the system. Substations with rectifier stations are used to connect the segments to the medium voltage power grid at regular intervals (Figure 9). Furthermore, DC power is distributed to the so called Wayside Power Converters Inverter which generate the required 20 kHz AC to energize each segment. If a rectifier station were to fail the system operation would not be affected. Yet, if two or more stations would shut-down, then in this case the voltage may be too low to sustain power transfer in that section but the system will still operate. The installation of primove windings in the road are performed by removing a 200 mm deep and 800 mm wide strip of asphalt. The primove winding is installed in a carrier to maintain the winding shape which thereafter is fixed to the roadbed and connected to the Wayside power converter. The installation is similar to installing snow melting cables [8].

Figure 9. This figure illustrates the basis of the primove Highway architecture. (Source [8]). It has been proven through various testing that power transfer above 150 kW can be performed for speeds of varying ranges (20-70 km/h). Additionally, it has been demonstrated that a misalignment of 100 to 150 mm has marginal impact on the power transmission between the road and the pickup system. A global efficiency of the primove Highway system has been estimated to be somewhere between 78-88 %. The primove system is designed to operate in all weather conditions and has

17 demonstrated to be inherently insensitive to weather conditions. It has also shown to meet the regulations and requirements regarding electromagnetic field emissions. Humans are unlikely to be exposed to high magnetic fields, which are situated close to the pickup under the vehicles, as the segments are only energized when a vehicle is in motion. Furthermore, shielding and receiver design are implemented in the vehicles to protect cargo and humans. The immediate area next to a primove Highway road has been found to have small magnetic fields which are very much below the standard exposure limits [8].

A total investment cost per kilometer for an inductive ERS between Stockholm and Gothenburg was computed to be roughly 30 million SEK per kilometer [8]. However, it can be expected that an implementation on a national scale might push the prices down due to economies of scale.

2.1.4 Stakeholders In a report published by VTI Viktoria, relevant stakeholders were identified for the ERS [8]. Figure 10, which is derived from the report, displays these major stakeholders.

Automotive firm

Road Power Construction Technology firm firm

ERS Electric utility Users

Petroleum Government firm and agencies

Figure 10. The major stakeholders in the ERS. (Source [8]) The automotive firms have to develop technical competence and a business model for vehicles that can be incorporated into the ERS, no matter which power transfer technology that becomes the standard. Petroleum firms will continue to be important but will need to handle the fact that they will most probably become secondary fuel suppliers. Thus, a need for reinvention and implementing complementing new businesses, such as fast charging and battery swapping stations will most likely be required. The Construction firm’s role will be to integrate the electric transfer technologies in the roads as well as the electric grid in a safe and durable manner. A new market for public-private partnerships could be a possibility.

Government and agencies will probably have to pay a vital role in facilitating the investments in ERS. The main motivation for them will be to reduce the environmental impact, oil imports and increase the energy efficiency. New national and international policies will most likely be required as loss in oil taxes and currency savings from oils imports will be removed. Also, export of ERS technologies could become a new market of income. Users could potentially lower their fuel costs due to increased energy efficiency and the comparably lower electricity costs (however, the electricity cost are expected to rise). Moreover, the user could utilize a system that is more environmental friendly. Road power technology firms could be companies providing the different power transfer technologies. The exact role in the ERS business model for these companies is yet to be identified. Lastly, the Power companies producing electricity will become the primary fuel supplier and must hence also develop and dimension the power grid to be able to handle the increase in the electricity consumption required. Furthermore, designing and delivering power stations connecting the electrified roads is a new potential business market in combination with increasing the sales of electricity for the power utility companies.

2.2 Electric vehicles Electric vehicles, abbreviated as EV, are vehicles that use electricity as their energy source. They are propelled by one or more electric motors and rechargeable battery packs or other energy storage devices are used to store the electricity [7]. Electrical cars are not a new invention, as a matter of fact, EV’s were first introduced more than a 100 years ago. During the early 1900s, electric cars accounted for around a third of all vehicles on the roads. Due to a number of reasons, mainly decreasing oil prices and Henry Ford’s mass-produced Model T which reduced the cost of a petroleum-based vehicle significantly. The result was that the electric vehicles more or less disappeared by 1935 [38]. Not much advancement was made to the EV technology the following 30 years due to gasoline being cheap and abundant. It was not until the gasoline shortages during the early 1970s that a new spark of interest was initiated and novel developments were made to the electric cars. However, the electric cars back then could never really compete with the gasoline- powered cars due to limited performance. Only having a top speeds of 70 km/h and a travelling range of just 65 kilometer before needing to be recharged [38].

Figure 11. A Thomas Parker’s electric car from the 1880s. (Source: [39]) The next rise in popularity for the electric vehicles came in the 1990s, this time it was the concern for the environment that drove the electric vehicles forward. Several new federal and state regulations in U.S regarding the environment such as the 1990 Clean Air Act Amendment and the 1992 Energy Policy Act, in combination with new transportation emissions regulation, initiated intense research in many parts of the world. Over the next decades several automobile makers started producing electric or hybrid cars and, since Tesla joined the market, huge advancement has been made in the electric vehicle field [38]. Mainly due to advancements in new battery technology, the plug-in electric vehicle’s range has seen a remarkable increase. The battery research and development has helped cut electric vehicle battery costs by 50 percent in the last four years, while simultaneously improving the vehicle batteries’ performance (meaning their power, energy and durability).

This in turn has led to lower costs for electric vehicles, making them a more affordable option for consumers. Today, there are over 23 plug-in electric and 36 hybrid models available in all sizes and shapes from a range of different automobile companies [38]. As of 2015, the total number of electric vehicles on the road worldwide was 740 000, having increased tremendously the last couple of years (Figure 12). Furthermore, registrations of new electric cars increased by 70% 20 between 2014 and 2015, with over 550 000 vehicles being sold worldwide in 2015. In seven countries, namely Norway, the Netherlands, Sweden, Denmark, France, China and the United Kingdom, the market shares of electric cars rose above 1%. In Norway the market share reached an astounding 23% and in the Netherlands almost 10 % during 2015. China has emerged as the main electric vehicle market; its booming electric car sales were larger in 2015 compared to even the United States [40].

Figure 12. Worldwide number of electric vehicles in use from 2012 to 2015. (Source: [41]) Even though the initial investment might be more for an electric vehicle compared to a conventional vehicle, a recent study made by the Electric Power Research Institute (EPRI) shows that the lifetime costs of ownership for an electric vehicle are competitive with conventional gasoline and hybrid vehicles [42].

Bloomberg New Energy Finance have stated in a recently published study that the electric cars will by 2022 cost the same as their internal combustion counterparts, which will be the point of liftoff for EVs. They have also predicted that by 2040 electric vehicles will stand for 35 % of the global new car sales [43]. The average cost for consumers for the electric cars released during 2015 to early 2016 is about 400 000 SEK, where the median is roughly 300 00 SEK [44].

As the electric cars use electric motors, their tank to wheel efficiency is about 70 %, compared to conventional gasoline vehicles that only use 15 % - 17 % of the energy stored in the gasoline ( [45], [46]). The driving range of an EV is still considerable less than conventional vehicles and lies around 100 – 200 kilometer compared to roughly 600 kilometer for a gasoline car [47]. However, a few Tesla models have a range of up to 300 to 480 km [48] but the prices of these models increase tremendously due to the bigger batteries that are required and usually cost in the range of one million SEK. Yet, Tesla has announced that the new model 3 which is scheduled to be released sometime in 2017 will only cost around 300 000 SEK and will boast a driving range

21 of at least 350 kilometers [49]. Similarly to how the energy consumption for petrol and diesel engine vehicles are given in liter per kilometer, for electric cars the energy usage is given in kilowatt-hours per kilometer. Although the energy consumption depends a lot on factors such as car model and manufacturer, by comparing different sources, an average energy consumption of 0.16 kWh/km was obtained for passenger cars ( [50], [51], [52]). The recharging time to fully recharge a battery pack can take between 4 to 8 hours, while “fast charging” to 80 % of the battery capacity normally takes 40 minutes.

According to the Bloomberg New Energy Finance (BNEF), for the EVs to achieve widespread adoption, one of four things need to happen, specifically:

1. Governments need to offer incentives to lower the costs. 2. Manufacturers need to accept extremely low profit margins. 3. Customers need to be willing to pay more to drive electric vehicles. 4. The cost of batteries needs to come down.

Even though the first three points have already partly been happening in the EV market, this possibly cannot be sustained in the long run. Luckily, the costs of batteries have been falling rapidly and are thus heading in the desired direction [53].

2.2.1 Batteries Batteries can be used to power the propulsion on EVs and are one of the key technologies in electric vehicles. The drastic development in battery technology during the last decade which has improved the battery performance in power, energy and durability, is one of the major reasons for EVs increase in popularity [38]. In fact, the battery cost has more than halved between the years 2008 to 2012 and as the batteries account for a third of the cost of building an electric car, this is a significant development [53]. According to the U.S. Department of Energy (U.S. DOE), the cost of batteries (excluding warranty cost or profit and assuming a production volume of at least 100 000 batteries annually) have decreased from 1000 USD/kWh in 2008 to 485 USD/kWh in 2012 [54]. Estimation for Internal Combustion Engine (ICE) parity target, according to IEA, is 300 USD/kWh (Figure 13).

Figure 13. Estimated Costs of EV Batteries through 2020. (Source: [54]) In 2015, the price for lithium-ion battery packs is reaching 350 USD/kWh as per a study published by BNEF. They also project that long-range electric cars will cost less than 22 000 USD expressed in 2016 dollars and the battery costs will be well below 120 USD/kWh by 2030 to further fall in the future with new chemistries becoming available. Major goals made by Batteries and Energy Storage subprogram for 2022 include decreasing the battery cost to 125 USD/kWh and increasing the density from 100 Wh/kg to 250 Wh/kg [55]. Tesla Motors and General Motors have also stated that they believe that the battery prices might reach around 100 USD/kWh until 2020-2021 [56].

When it comes to the specific energy, meaning how much energy a battery can hold in weight (W/kg), batteries are still far behind fossil fuels in this aspect. One kilogram of battery delivers about 120 W, compared to one liter of gasoline that produces roughly 100 times that, about 120 kW of energy. In spite of the fact that the electric motors have a much better efficiency compared to ICE, the energy storage capability of a battery is still a fraction of that of fossil fuels [57].

Another important aspect for EV batteries is their useful lifespan. It has been widely accepted by many drivers and prior literature that the EV batteries should be retired after the battery has lost 20 % of its energy storage or power delivery capability. However, a study performed by the Berkeley Lab found that the travel needs of drivers continue to be met well beyond these levels of battery degradation [58]. Most EV batteries are guaranteed for 8-10 years or 160 000 km, but upholding the performance is a challenge as batteries are sensitive to cold and heat, thus degrading faster in these climates.

2.2.2 Fast Chargers As mentioned earlier, one of the major limitations with batteries is the recharging time. Using standard chargers, charging can take up to 4 to 8 hours. This is way too long for batteries to be a viable competitor against petroleum-based fuels. Due to this fact, the EV and battery manufacturers have developed so called fast chargers that can charge the batteries many times faster. The faster charging time can be achieved by increasing the output capacity, which for fast chargers can lie anywhere between 50-150 kW [59]. However, this also requires automakers to produce cars that can make use of the higher output capacities.

Currently, Tesla’s version of a fast charger, called Supercharger, is considered to be the fastest charging station in the market. Superchargers consist of multiple chargers that work in parallel and can thus deliver up to 120 kW of DC power directly to the battery. The car’s onboard computer is used to constantly monitor the charging process to ensure that peak performance can be maintained. By applying this method, Tesla’s fast charger is able to charge 80 % of a 90 kWh large battery in roughly 40 minutes (Figure 14). However, it is important to note that the actual charging rate can be affected by a number of external factors, such as ambient temperatures, utility grid restrictions and charging traffic, to name a few [60].

Figure 14. The charging profile of a Tesla Supercharger. (Source: [60]) The cost for building a supercharging station for Tesla is approximately between $100,000 and $175,000 depending on the station. A significant portion of the costs originate from modifications that need to be made at the site to support the Superchargers. The construction of Superchargers is considerably more expensive than putting up standard chargers, this is due to the fact that Superchargers deliver around five times the power of standard charging stations in the same amount of time. As a result they are more demanding in terms of infrastructure changes. A property owner who chooses to acquire a supercharging station is not required to stake any monetary commitment, but is required to offer up between four or five spaces. This means that there are normally between 4-5 Superchargers per supercharging station [61]

2.3 Road Network in the World Roads have always been a vital part of human society and have existed in different forms for thousands of years. Since the invention and employment of motor vehicles, the road network has undergone a massive expansion and a growing number of roads are being asphalted [62]. Today, approximately, 64 million kilometer of road network has been laid down in the world [63]. In most developed countries, the road network is well-connected due to its importance for the transportation sector. Using Google Maps and the Swedish Transport Administration‘s own maps, pictures of the road network for major roads could be acquired for different regions of the world.

The major road network in parts of Europe, United States and Scandinavia are illustrated in Figure 15, Figure 16 and Figure 17. From the pictures it can be observed that the global road network on average is well-linked. Furthermore, as these illustrations only show the major roads, in reality if other roads were also displayed, the road mesh would in that case be even finer. After studying a significant number of road maps from various regions of the world, it was concluded that estimating the global road network as being quadratically meshed is an acceptable assumption that will not decrease the accuracy of the results notably.

Figure 15. The major road network in orange in United States.

Figure 16. The major road network in orange in parts of Europe.

Figure 17. The major road network in orange in Scandinavia.

2.4 Fuels The global transportation energy consumption is dominated by two fuels, namely, motor gasoline (including ethanol blends) and diesel (including biodiesel blends). This is illustrated in Figure 18 which shows the world’s transportation consumption for all transportation categories. Gasoline is mainly used for light-duty vehicles such as private cars to transport people whereas diesel is more commonly used for heavy-duty vehicles such as trucks [64]. Petroleum fuels, which include gasoline and diesel fuel, are made from crude oil and liquids from natural gas processing [65]. In this chapter, some information about these fuels is presented.

Figure 18. The world transportation consumption by fuel. (Source: [64])

2.4.1 Gasoline Gasoline, also known as petrol, is the transportation fuel that is used by most vehicles in the world (Figure 18). The exact composition and the prices for gasoline vary widely around the world due to difference in various taxes and subsidies for gasoline [66]. For this thesis, it has been assumed that all gasoline vehicles in the world use Swedish unleaded 95 Gasoline. All unleaded 95 gasoline have 5 volume % of ethanol which has very good combustion characteristics and is therefore ideally suitable as a renewable component in gasoline [67]. The fuel has an energy content of 43.2 MJ/kg and a well-to-wheel life-cycle greenhouse-gas emission of 2700 g CO2e/l [68]. According to Svenska Petroleum och Biodrivmedel Institutet (SPBI), the average price of gasoline excluding VAT was 5.11 SEK/l in 2015 [69] and the European commission for February 2016 showed a weighted average price for the European countries to be roughly 3.50 SEK/l without taxes [70]. However, as the gasoline price has reduced significantly the last year, the gasoline prices during the last five years in Sweden were considered instead. From this, the average gasoline price excluding VAT for the last five years was computed to be 5.9 SEK/l.

Cost of Gasoline without VAT 7

SEK/L 3

YEAR

Figure 19. The cost development of gasoline in Sweden excluding VAT from the year 1981 – 2015. (Source: [69])

2.4.2 Diesel Diesel, as mentioned earlier, is the second biggest fuel source that dominates the road transportation sector. Even though it is mainly used for heavy-duty vehicles, a lot of light-duty vehicle such as passenger cars are also known to use it. Due to the same reasons mentioned for gasoline, it is assumed for this thesis that all diesel-driven vehicles in the world use the Swedish MK1 diesel [71]. This diesel has an energy content of 42.9 MJ/kg and a well-to-wheel life-cycle GHG of 2820 g CO2e/l [72]. For the year of 2015 the cost of diesel excluding taxes was approximately 4.6 SEK/l [69] and 3.5 SEK/l was the weighted average for the European countries in February 2016 [70]. Similarly to gasoline prices however, the diesel prices have also decreased significantly the last year. To get a better picture, the diesel prices during the last five years were considered and thus, an average price of 5.6 SEK/l excluding VAT was obtained.

Cost of Diesel without VAT 8

4 SEK/L 3

YEAR

Price w/o. VAT

Figure 20. The cost development of diesel excluding VAT in Sweden between the years 2001 to 2015. (Source: [69])

2.5 The Electricity Market In 2013, the world’s total final energy usage was 108 171 TWh [73] where electricity accounted for 18 % of the usage as seen in Figure 21. Consequently, electricity is highly coveted due to its high exergy value and easy to trade flexibility, making it a big business. In many instances, electricity is generated by a power company that in the end does not deliver to the end-users. Instead, the produced electricity is repeatedly bought and re-sold a number of times before finally being used [74]. This is the case for a so called wholesale electricity market which a majority of the world’s countries operate under [75].

Figure 21. World total final energy usage by source for 2013. (Source: [73])

2.5.1 Global Electricity Mix As illustrated by Figure 22, the world’s electricity production for 2014 was 22 433 TWh. By far the largest energy sources for the electricity were fossil-fuels which corresponded to 66 % of the total electricity mix. Naturally, due to the high percentage of fossil fuels used, the GHG-emissions for electricity production are quite high when looking at a global perspective. According to the International Energy Agency, the average world CO2e emission for the years 2005-2007 for electricity production was about 504 g CO2e/kWh [76]. Using the data acquired from IPCC studies regarding the life cycle global warming potential of selected electricity sources [77], a naive model was made by the author which also gave similar result of approximately 500 g CO2e/kWh for the world’s electricity production.

Figure 22. World electricity generation from all energy sources in 2014. (Source: [78])

When it comes to the electricity prices, they can vary widely between the countries but also within a country itself depending on different factors such as infrastructure and geography (Figure 23). It is mainly the selection of fuels used to generate electricity that decides the cost of the electricity prices. This is certainly true for Italy, which at the moment has the highest electricity cost in the world. Due to the fact that Italy choose not to build any nuclear power plants, their electricity generation mix consists primarily of fossil fuel sources which are more expensive compared to nuclear or hydroelectric power plants. On the flipside, Sweden enjoys some of the cheapest electricity in the world due to having mainly nuclear and hydro power as their primary electricity generation sources [79]. Using the data from Figure 23, a rough world average for the electricity price per kilowatt hour excluding taxes was calculated to be 0.8 SEK/kWh and the median as 0.7 SEK/kWh.

Figure 23. Electricity prices in U.S. dollar cents per kilowatt hour excluding taxes. (Source: [79])

2.5.2 Nordic Electricity Mix In 1996, the Swedish electricity market underwent a deregulation that divided the market into three different parts. These three parts are electricity production, distribution and electricity sales. The electricity that is produced is sold, most commonly through the power market place Nord Pool where Sweden, Norway, Finland and Denmark are involved, to electricity sales companies who in turn re-sell the electricity to the end consumers. Large customers, such as big industries, sometimes buy electricity directly from an electricity production company. Moreover, the electricity distribution companies are responsible for providing the power grids for a certain geographical area which results in them having monopoly over said area. This is therefore the part of the electricity market that is still regulated in Sweden, the purpose of deregulating the electricity market was to increase the competition in the electricity market, giving the customers more choices while consequently decreasing the prices [80].

The electricity trading between the countries involved in Nord Pool, occurs one day in advance. Thus, the prices for the coming day are set one day prior. The electricity producers bid on the price that they are willing to sell their electricity for and the electricity sales companies in turn bid on the prices they are willing to buy for. Where the supply and demand meets, the price is set, similarly to any other commodity market [81]. The so called marginal pricing is used when prices are set for the electricity produced per hour. This means that the price for all electricity produced for a particular hour is the price at the highest accepted producing bid. Consequently, this results in the fact that the electricity produced during that hour will be paid at the cost to produce power from the most expensive power source. Hence, power sources with low marginal costs, which are used for base load e.g. nuclear and hydro power, are used first, whereas power sources with high marginal cost such as oil- and gas-driven turbines are used only when the power demand is very

31 high. The price of electricity as a result differs over the span of a trading day for the electricity companies [81].

Figure 24. Power production by source in the Nordic region in 2013. (Source: [82])

The electricity production in the Nordic region is dominated by hydropower which stands for 53 % of the total power generation followed by nuclear power at 23 % (Figure 24). As only 12 % of the electricity production stems from fossil fuels the Nordic electricity mix produces low greenhouse gas emissions. In the year of 2013 the Nordic countries together use 380.5 TWh, where Sweden consumed 137 TWh. Naturally, the exact share of the electricity mix fluctuates between different years depending on various factors such as weather conditions, water availability and availability of the energy sources [81]. However, for this thesis, the Nordic electricity mix for the year of 2013 has been assumed. The average spot price for electricity in Nord Pool was 38.1 euro/MWh which roughly corresponds to 0.355 SEK/kWh. The GHG emissions for Nordic electricity mix is roughly 90 g CO2e/kWh [83] and for European electric mix, it is approximately 400 g CO2e/kWh [84].

3 MODELS

Two models were produced in order to investigate the prospect of different electric road technologies from a global and a Swedish perspective. This was accomplished through comparing the three main ERS technologies against each other, against the conventional petroleum-based vehicle fleet and against a pure electric passenger car fleet. Several submodels were hence required to be created for each model to produce these comparisons. Additionally, a model simulating an expansion of ERS in Sweden using different scenarios was also produced.

In Figure 25, the two models and their accompanying submodels are presented in an illustrative manner. The figure also displays the thesis objectives that the different models intended to answer. As can be seen, Model 1 and its submodels aimed to investigate the first thesis objective (as presented in chapter 1.2) from a global perspective. Similarly, submodel 1 and 2 in Model 2 intended to study the same thesis objective but from a Swedish perspective. For all these model, two battery cost scenarios were scrutinized, the current battery pricing and a projected future battery pricing. It is important to note that the only input changed between the scenarios was the battery pricing. Lastly, submodel 3 and 4 were produced to explore the second purpose question which aimed to examine a possible ERS expansion in Sweden.

Figure 25. An illustrative flow chart over the two models with accompanying submodels produced to investigate the thesis objectives. In the following sections, the main building blocks for each of the models introduced and the results they yield are described in detail.

3.1 Model 1 – World The first model was produced to investigate two different questions. Firstly, it was to identify countries and regions in the world where implementing, on a full-scale, different ERS technologies in combination with electric vehicles would result in savings compared to the current petroleum- based transportation system. For this model, one of the most important assumptions made was that the cost of a conventional petroleum-based vehicle was estimated to be equal to an electric vehicle without the battery and the pick-up arm apparatus which connects to the electrified roads. As a result, in a steady state case where the ERS solutions are already completely implemented in the countries, the cost of an electric vehicle excluding the battery and the pickup arm apparatus and the cost of a conventional vehicle could be neglected from the cost calculations. Hence, the yearly cost of an ERS was the annual cost of the electrified road investment, the new batteries, the new pick-up arms and the electricity usage from all the electric vehicles. Similarly, the yearly cost for the conventional vehicles in the current petroleum-based transport system would thus only be the cost of fuel used by the vehicles. By comparing these costs, it was possible to conclude whether an implementation of the different technologies of ERS would result in savings or not for all the studied countries.

The second question that the model aimed to investigate was to compare the cost of implementing a full-scale ERS for passenger cars against the cost of converting all passenger cars to pure battery electric cars. Also for this model, an assumption was made that the cost of an electric passenger car without the battery is the same regardless of the size of the battery. The yearly cost of an ERS was in this case the annual cost of the electrified road investment, the new batteries and the new pick-up arms. The yearly cost of a pure battery electric car based transportation sector would then be the cost for new batteries plus the annual infrastructure costs.

The thought process behind these models is explained in more detail in the impending sections. However, to model both questions the first step was to find the optimal length of electrified road needed in each country.

3.1.1 Computing the Optimal Electrified Road Length The basic idea behind the methodology to find the optimal electrified road length was to divide the land area of the studied countries into quadratic grid-meshes. The meshes symbolize the electrified roads and the space in-between implies areas for battery drive. A naive example of how such a segmentation could look like for Germany is illustrated in Figure 26. As can be seen, the red grid-meshes represent the road areas that need to be electrified and the blue gridlines inside represent roads where the vehicles will use batteries as their energy source. In this model, it was assumed that the road system of each country is meshed in a quadratic manner as stated in the assumption chapter. Hence, the fundamental idea was that the main factors that affect how much of the road that needs to be electrified is the cost of batteries compared to the cost of constructing ERS. Consequently, the optimal size of the grid-meshes, which is represented by ∆ in the figure, could be found by optimizing these two factors.

Figure 26. Example of how a sectioning of Germany into quadratic road sections with ERS installation can be conceptualized (Note that the meshed road sections would in reality only be on the land area). The method of optimizing the size of the quadratic grid ∆ used for this model was mainly developed by Gunnar Asplund from Elways and was further modified in this thesis. It is based on the fact that the bigger the size of the electrified road grid-mesh is, the cheaper the ERS installation will be in the country as the length of electrified road needed would decrease. However, this will also lead to a need for bigger batteries, due to a longer driving range requirement, which in turn would result in increased battery costs. Consequently, by balancing the length of the electrified road and the battery size required, we could thus obtain the optimal grid-size ∆ for each country. In the following paragraphs, this method is explained further.

If we consider only one square in the quadratic grid-mesh and assume that the side of the square is 푥, as illustrated in Figure 27, it can be concluded that the area of this square would hence be 푥2.

From this, using the land area of an arbitrary country, the number of squares 푛푠푞푢푎푟푒 with length 푥 that fit in that country could then be expressed using Equation (1).

퐿푎푛푑 퐴푟푒푎 (1) 푛 = 푠푞푢푎푟푒 푥2

Figure 27. Viewing one square in the quadratic grid-mesh. Similarly, the circumference of the square, which in reality would be the length of the electrified road, can be expressed as 4푥. This is true when considering only one square. However, when considering several squares together, it is quite clear that the circumference of each square would be less than 4푥 as they overlap. It was found, through experimenting, that when an area is meshed very finely into quadratic grid-meshes. The circumferential length of the squares can be approximated as 2푥 instead of 4푥. This conclusion can be clarified with the help of Figure 28. In the figure, a grid of squares was produced by adding only two sides of a square at a time. At first, doing this for only one square led to a big error as half the sides of the grid were not considered. But as one continued to go towards bigger 푛, the sides of the grid not considered became increasingly smaller compared to the rest of the grid. Hence, it was derived from the table that the sides neglected by this method increased linearly whereas the sides considered increased quadratically. As such, it was found to be a fair assumption to disregard the outer sides of the grid- mesh as 푛 → ∞ and approximate the length of a square in a large grid as 2푥. This was further supported by the fact that in reality the borders of countries, which correspond to the neglected sides, usually do not have roads.

Figure 28. Explanation of why the circumferential length of a square in a large grid-mesh was approximated as 2x.

Consequently, using this information the length (L) of 푛푠푞푢푎푟푒, which relates to the total length of the electrified road system, could be obtained by applying Equation (2).

퐿푎푛푑 퐴푟푒푎 2 ∙ 퐿푎푛푑 퐴푟푒푎 (2) 퐿(푥) = 2푥 ∙ 푛 = 2푥 ∙ = 푠푞푢푎푟푒 푥2 푥

From this, as the cost of constructing the different ERS technologies per kilometers was known, the cost of constructing an ERS technology could thus be expressed as a function of 푥 as shown in Equation (3) and (6).

2 ∙ 퐿푎푛푑 퐴푟푒푎 (3) 퐸푅푆(푥) = 퐶표푠푡 퐸푅푆 ∙ 퐿(푥) → 퐸푅푆(푥) = 퐶표푠푡 퐸푅푆 ∙ 푥

Furthermore, by computing the car density in a country the cost of installing an ERS technology could instead be expressed in cubic kilometer of road cost per vehicle (SEK/km2/vehicle) using the known inputs as shown in Equation (4) and (5).

푁푉푒ℎ𝑖푐푙푒푠 (4) 푉푒ℎ𝑖푐푙푒 퐷푒푛푠𝑖푡푦 = 퐿푎푛푑 퐴푟푒푎

2 퐿푎푛푑 퐴푟푒푎 (5) 퐶표푠푡 푝푒푟 푘푚 푔푟𝑖푑 푝푒푟 푣푒ℎ𝑖푐푙푒 (푘2) = 2 ∙ 퐶표푠푡 퐸푅푆 ∙ 푁푉푒ℎ𝑖푐푙푒푠

2 The cost per km per vehicle was then defined as 푘2 and the function ERS(x) was simplified to the following:

푘2 (6) 퐸푅푆(푥) = 푥

Going back to Figure 27, the battery cost could similarly be expressed as a function of 푥. This was achieved by considering an electric vehicle inside the electrified road mesh. From this it was 1 concluded that the theoretical maximum travelling range required from a battery would be 푥. 2 However, to ensure that the battery charge would not be depleted mid-drive, an extra driving range factor was added and could be chosen as preferred. Consequently, as the battery cost in SEK/km was known and the average electricity consumption of electric vehicles in kWh/km could be computed using the method described later in Model 2. The cost of battery per kilometer and car

(SEK/km/car), denoted as 푘1, could thus be computed using Equation (7).

푘1 = 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푉 ∙ 퐶표푠푡 퐵푎푡푡푒푟푦 ∙ 퐸푥푡푟푎 푅푎푛푔푒 (7)

However, as the ERS building costs and the battery costs needed to be expressed in terms of yearly costs for the coming calculations, there were a number of steps that were required to be taken. To begin with, the economic lifespan had to be estimated.

The economic lifespan of a product is difficult to predict as it depends on a number of unpredictable factors, this is especially true for a product that does not actually exist on a commercial scale yet. Thus, in this report, the economic lifespan was computed using the technical lifespan of the products and the fixed-rate mortgage method. The fixed-rate mortgage method takes into consideration both the interest and the amortization of an investment. By applying the fixed- rate mortgage equation in combination with the technical lifespan on an arbitrary cost, the economic lifespan was obtained as shown in the subsequent steps.

The fixed-rate mortgage equation can be expressed as:

푟(1 + 푟)푛 (8) 푃푎푦푏푎푐푘 = 퐿 ∙ (1 + 푟)푛 − 1 Where, L = Loan amount from the beginning r = Interest in decimal form, either annual or monthly rate n = Number of payment occasion

Converting this equation to apply to our models resulted in Equation (9).

퐼푛푡푒푟푒푠푡 ∙ (1 + 퐼푛푡푒푟푒푠푡 )푇푒푐ℎ푛𝑖푐푎푙 퐿𝑖푓푒푠푝푎푛 (9) 퐶표푠푡 = 퐴푏푟𝑖푡푎푟푦 퐶표푠푡 ∙ 푦푒푎푟푙푦 (1 + 퐼푛푡푒푟푒푠푡 )푇푒푐ℎ푛𝑖푐푎푙 퐿𝑖푓푒푠푝푎푛 − 1

From this the economic lifespan was obtained to be:

퐴푟푏𝑖푡푎푟푦 퐶표푠푡 (10) 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 = 퐶표푠푡푦푒푎푟푙푦

Once the economic lifespan was computed for the different ERS technologies, battery and pickup arm, these were then related to the economic lifespan of ERS. Doing this, the total cost for battery and pick-up arm could be computed for a period that corresponded to the ERS economic lifespan. Coefficients that correlated the battery and the pick-up arms economic lifespan to the ERS economic lifespan were derived as shown in Equation (11) and (12).

퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆 (11) 퐶표푒푓푓𝑖푐𝑖푒푛푡 = 퐵푎푡푡푒푟푦 푡표 퐸푅푆 퐿𝑖푓푒푠푝푎푛 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐵푎푡푡푒푟푦

퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆 (12) 퐶표푒푓푓𝑖푐𝑖푒푛푡 = 푃𝑖푐푘푢푝 푎푟푚 푡표 퐸푅푆 퐿𝑖푓푒푠푝푎푛 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 푃𝑖푐푘푢푝 퐴푟푚

Coming back to defining the battery equation, the battery lifespan coefficient was combined with

푘1 to take into account that the battery economic lifespan is shorter than the ERS lifespan. Thus, the batteries would be required to be changed more frequently than ERS. This resulted in a new definition for 푘1 and the “Cost battery” factor as demonstrated in Equation (13) and (14).

퐶표푠푡 퐵푎푡푡푒푟푦퐸푅푆 푙𝑖푓푒푠푝푎푛 = 퐶표푠푡 퐵푎푡푡푒푟푦 ∙ 퐶표푒푓푓𝑖푐𝑖푒푛푡퐵푎푡푡푒푟푦 푡표 퐸푅푆 퐿𝑖푓푒푠푝푎푛 (13)

푘1 = 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푉 ∙ 퐶표푠푡 퐵푎푡푡푒푟푦퐸푅푆 푙𝑖푓푒푠푝푎푛 ∙ 퐸푥푡푟푎 푅푎푛푔푒 (14)

Finally, the equation for the battery cost as a function of 푥 could be derived as shown below.

퐵(푥) = 푘1푥 (15)

It is important to note however, that the cost of battery is not entirely linear in reality as the difference in cost of producing a small size and a big size battery is not linear. However, for the sake of simplification and due to the fact that the price of a battery is usually given in SEK/kWh in literature, a linearization of the problem was judged not only to be acceptable but also necessary.

In the next step, by merging Equations (15) and (6), a function was obtained that displayed how the combined cost of ERS installation and batteries varied with increasing 푥 (Equation (16)). Thus, the fundamental function that the modelling is based on was obtained. In Figure 29, the cost development as a function of 푥 is illustrated in an imaginary manner for batteries, ERS installation and the combination of both these factors. The combined function, denoted as 푓(푥), was found to have a minimum where the length 푥 of the electrified roads and the battery size needed was optimized in a manner that led to an overall lowest cost.

푘2 (16) 푓(푥) = 푘 푥 + 1 푥

Figure 29. Cost development as a function of length x for battery, ERS installation and the combination of these two factors. The modelling function was derived and thus the value of 푥 where the combined cost was at its minimum was computed. The procedure is presented below:

푘2 (17) 푓′(푥) = 푘 − 1 푥2

Inserting zero instead of 푓′(푥), the value of 푥 which corresponds to the minimum cost could be found as shown in Equation (18).

(18) 푘2 2 푘2 푘2 0 = 푘1 − 2 → 푥 = → 푥 = ±√ 푥 푘1 푘1

The negative solution was discarded, as it was known that costs always are positive, this finally resulted in that the optimization equation was derived.

(19) 푘2 푥표푝푡𝑖푚푎푙 = √ 푘1

Once the 푥표푝푡𝑖푚푎푙 was computed, the rest of the parameters required to answer the modelling questions could then be calculated. The computation steps for the most important parameters are presented below.

The battery size necessary for the optimal grid-mesh was computed as shown in Equation (20), where extra driving range was added due to the fact mentioned previously. Likewise, the total cost of batteries for each vehicle in the country for a period that corresponds to the economic lifespan of ERS was then calculated using Equation (21).

퐵푎푡푡푒푟푦 푆𝑖푧푒 = 푥푂푝푡𝑖푚푎푙 ∙ 퐸푥푡푟푎 푅푎푛푔푒 ∙ 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푉 (20)

푇표푡푎푙 퐵푎푡푡푒푟푦 퐶표푠푡 = 퐵푎푡푡푒푟푦 푆𝑖푧푒 ∙ 푁푉푒ℎ𝑖푐푙푒푠 ∙ 퐶표푠푡 퐵푎푡푡푒푟푦퐸푅푆 푙𝑖푓푒푠푝푎푛 (21)

The total length 퐿(푥푂푝푡𝑖푚푎푙) and the cost of the grid 퐸푅푆(푥푂푝푡𝑖푚푎푙) needed could thus be calculated using the following equations for each country. Additionally, the size of the grid was compared with the existing real road network in each country.

퐿푎푛푑 퐴푟푒푎 (22) 퐿(푥푂푝푡𝑖푚푎푙) = 2 ∙ 푥푂푝푡𝑖푚푎푙

푇표푡푎푙 퐶표푠푡 표푓 퐺푟𝑖푑 = 퐿(푥푂푝푡𝑖푚푎푙) ∙ 퐶표푠푡 퐸푅푆 (23)

퐿(푥푂푝푡𝑖푚푎푙) (24) % 표푓 퐸푥𝑖푠푡𝑖푛푔 푅표푎푑 푁푒푡푤표푟푘 = 퐸푥𝑖푠푡𝑖푛푔 푅표푎푑 푁푒푡푤표푟푘 퐿푒푛푔푡ℎ

Furthermore, using the cost of a pickup arm that varies between the different ERS technologies. The cost of installing a pickup arm in each vehicle for a period that corresponds to the economic lifespan of ERS was computed through using Equation (25).

푇표푡푎푙 퐶표푠푡 표푓 푃𝑖푐푘푢푝 퐴푟푚푠 = 푁푉푒ℎ𝑖푐푙푒푠 ∙ 퐶표푠푡 푃𝑖푐푘푢푝 퐴푟푚 (25)

The yearly costs were the most interesting as they were used to answer the modelling questions, the total investment costs of battery, pick arm and grid were divided by the economic lifespan of ERS (Equations (26), (27) and (28)).

푇표푡푎푙 퐶표푠푡 표푓 퐺푟𝑖푑 (26) 푌푒푎푟푙푦 퐺푟𝑖푑 퐶표푠푡 = 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆

푇표푡푎푙 퐵푎푡푡푒푟푦 퐶표푠푡 (27) 푌푒푎푟푙푦 퐵푎푡푡푒푟푦 퐶표푠푡 = 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆

푇표푡푎푙 퐶표푠푡 표푓 푃𝑖푐푘푢푝 퐴푟푚푠 (28) 푌푒푎푟푙푦 푃𝑖푐푘푢푝 퐴푟푚 퐶표푠푡 = 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆

3.1.2 Comparison – Petroleum-based Road Transport against ERS and EV Combination Once the optimal grid-mesh size was computed, the annual costs for an ERS based road transport sector could be produced. The parameters required to find out which countries in the world that would save money by implementing different technologies of ERS could then be computed. As mentioned previously, due to the assumption that the cost of a conventional petroleum-based vehicle is the same as the cost of an electric vehicle without the battery and the pickup arm apparatus, the yearly savings or losses of an ERS based transport sector compared to the current petroleum-based system could be calculated. This was achieved by computing the difference between the annual ERS system plus the electricity consumption costs from the EVs and the petroleum fuel costs in the current system. The procedure is explained in more detail in the coming paragraphs.

To begin with, the annual electricity cost of a hundred percent electric based transportation sector was computed by applying Equation (29) and (30). Where a weighted average distance travelled and average electricity consumption taking into account the different vehicle categories was computed using the method explained in Model 2.

퐴푛푛푢푎푙 퐸푙. 푈푠푎푔푒 = 퐷𝑖푠푡푎푛푐푒 푇푟푎푣푒푙푙푒푑 ∙ 퐴푣푒푟푎푔푒 퐸푙. 푈푠푎푔푒 ∙ 푁푉푒ℎ𝑖푐푙푒푠 (29)

퐴푛푛푢푎푙 퐸푙. 퐶표푠푡 = 퐴푛푛푢푎푙 퐸푙. 푈푠푎푔푒 ∙ 퐴푣푒푟푎푔푒 퐸푙푒푐푡푟𝑖푐푡푦 퐶표푠푡 (30)

To get a general idea about how much more electricity that would be needed to be produced in each country compared to now, Equation (31) was used.

퐴푛푛푢푎푙 퐸푙. 푈푠푎푔푒 (31) % 표푓 퐸푙푒푐푡푟𝑖푐푡푦 푃푟표푑푢푐푡𝑖표푛 = 퐷표푚푒푠푡𝑖푐 퐸푙푒푐푡푟𝑖푐𝑖푡푦 푃푟표푑푢푐푡𝑖표푛

Similarly, the annual fuel cost was computed for a hundred percent fossil-fuel based vehicle fleet using Equation (32) and (33). The fuel price used was also a weighted average.

퐴푛푛푢푎푙 퐹푢푒푙 푈푠푎푔푒 = 퐷𝑖푠푡푎푛푐푒 푇푟푎푣푒푙푙푒푑 ∙ 퐴푣푒푟푎푔푒 퐹푢푒푙 푈푠푎푔푒 ∙ 푁푉푒ℎ𝑖푐푙푒푠 (32)

퐴푛푛푢푎푙 퐹푢푒푙 퐶표푠푡 = 퐴푛푛푢푎푙 퐹푢푒푙 푈푠푎푔푒 ∙ 퐹푢푒푙 푃푟𝑖푐푒 (33)

Finally, by subtracting the annual fuel costs and the combined annual costs of electricity, batteries, pick-up arms and the electrified roads, the yearly savings/losses that an ERS would result in for a country was obtained.

퐴푛푛푢푎푙 퐸푅푆 퐶표푠푡 = 퐴푛푛푢푎푙 퐸푙. 퐶표푠푡 + 푌푒푎푟푙푦 퐺푟𝑖푑 퐶표푠푡 + 푌푒푎푟푙푦 푉푒ℎ𝑖푐푙푒 퐶표푠푡 (34)

Where,

푌푒푎푟푙푦 푉푒ℎ𝑖푐푙푒 퐶표푠푡 = 푌푒푎푟푙푦 퐵푎푡푡푒푟푦 퐶표푠푡 + 푌푒푎푟푙푦 푃𝑖푐푘푢푝 퐴푟푚 퐶표푠푡

Thus we get,

푌푒푎푟푙푦 푆푎푣𝑖푛푔푠 = 퐴푛푛푢푎푙 퐹푢푒푙 퐶표푠푡 − 퐴푛푛푢푎푙 퐸푅푆 퐶표푠푡 (35)

From this, the countries where implementing ERS would result in savings could hence be identified and sorted. From this the total number of vehicles in these countries could be computed and related to the total number of vehicles in the world using Equation (36).

푁푉푒ℎ𝑖푐푙푒푠 (36) % 표푓 푇표푡푎푙 푉푒ℎ𝑖푐푙푒푠 = 푁푢푚푏푒푟 표푓 퐶푎푟푠 𝑖푛 퐶표푢푛푡푟𝑖푒푠 푤ℎ푒푟푒 푌푒푎푟푙푦 푆푎푣𝑖푛푔푠 > 0

Similarly, the yearly GHG emissions that can be reduced if the ERS solution were to be implemented was summed for each country where the yearly savings are positive. In addition, the reduction in yearly GHG emission was then compared to the total yearly GHG emissions in the countries/globally by implementing Equation (37).

퐺퐻퐺 푒푚𝑖푠푠𝑖표푛 푟푒푑푢푐푒푑 (37) % 푅푒푑푢푐푡𝑖표푛 표푓 푇표푡푎푙 퐺퐻퐺 푒푚𝑖푠푠𝑖표푛 = 푇표푡푎푙 퐺퐻퐺 푒푚𝑖푠푠𝑖표푛

To get an idea about how much the total ERS investment cost would be related to the studied country’s GDP, Equation (38) was applied.

푇표푡푎푙 퐸푅푆 퐶표푠푡 (38) % 표푓 푌푒푎푟푙푦 퐺퐷푃 = 푌푒푎푟푙푦 퐺퐷푃

This whole modelling procedure was then performed for each of the three studied ERS technologies with two different pricing for batteries, namely the current battery price and the predicted future battery price.

3.1.3 Comparison – Pure Battery Electric Car Fleet against ERS and Electric Car Combination In this part of the model, the question to be answered was whether the cost of having a 100% pure battery electric passenger car fleet was more expensive than implementing the combination of ERS with electric passenger cars. It should be noted that for this comparison, only the inductive road and conductive road technology were studied as the overhead conductive technology is not applicable for passenger cars. In the same way as for the comparison between the petroleum-based transport sector and ERS, the first step was to compute the optimal grid-size. However, as only the passenger cars were to be compared in this part of the model, there were a couple of changes that were implemented. To begin with, the ERS was to be used only by passenger cars. It would be dimensioned for only electric cars, which in turn would lead to cheaper construction costs. It was estimated that the cost per kilometer for an ERS that was dimensioned for only passenger cars would be only 1/5 of the standard price. Thus, the standard costs of the different ERS technologies were reduced using Equation (39).

1 (39) 퐶표푠푡 퐸푅푆 = ∙ 퐶표푠푡 퐸푅푆 푂푛푙푦퐶푎푟푠 5

Additionally, as the input data collected regarding the number of vehicles in each country was for all categories of motor vehicles, the number of vehicles in each country was multiplied by a percentage coefficient (from Sweden) to obtain the number of passenger cars.

푁퐶푎푟푠 = 푁푉푒ℎ𝑖푐푙푒푠 ∙ 푃푒푟푐푒푛푡푎푔푒 퐶푎푟푠 (40)

After performing these changes, the rest of the calculations to obtain the yearly ERS costs were the same as described previously.

Once the yearly ERS costs were computed, the yearly cost of a pure battery electric based car fleet was also computed. This was done, to start with, by defining the acceptable minimum driving range that would be required from the battery electric cars so that they could compete against conventional passenger cars. From this, the size of the battery could thus be calculated as shown in Equation (41). Consequently, the cost of all the batteries that would be needed for a time period that coheres to the ERS economical lifespan could then be equated by applying Equation (42) for each country. Lastly, by using Equation (43), the yearly cost of batteries could be attained.

퐸푉 퐵푎푡푡푒푟푦 푆𝑖푧푒 = 퐴푐푐푒푝푡푎푏푙푒 퐷푟𝑖푣𝑖푛푔 푅푎푛푔푒 ∙ 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푉 (41)

푇표푡푎푙 퐸푉 퐵푎푡푡푒푟푦 퐶표푠푡 = 퐸푉 퐵푎푡푡푒푟푦 푆𝑖푧푒 ∙ 퐶표푠푡 퐵푎푡푡푒푟푦 ∙ 푁퐶푎푟푠 (42)

푇표푡푎푙 퐸푉 퐵푎푡푡푒푟푦 퐶표푠푡 (43) 푌푒푎푟푙푦 퐸푉 퐵푎푡푡푒푟푦 퐶표푠푡 = 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆

To somehow also include the infrastructure costs that will occur in a complete electric car based transportation sector, the cost of installing fast chargers, using the assumption for cost and frequency per car presented in Appendix C, was computed using Equation (44). Furthermore, it was assumed that the cost of an electric car without the battery is the same for both of the scenarios. 43

The total yearly cost of a pure battery electric based car fleet was obtained through the summation of the yearly battery costs and the yearly infrastructure costs (Equation (45) and (46).

푁퐶푎푟푠 (44) 푇표푡푎푙 퐼푛푓푟푎푠푡푟푢푐푡푢푟푒 퐶표푠푡 = ∙ 퐶표푠푡 퐶ℎ푎푟푔푒푟 퐶푎푟 푝푒푟 푐ℎ푎푟푔푒푟

푇표푡푎푙 퐼푛푓푟푎푠푡푟푢푐푡푢푟푒 퐶표푠푡 (45) 푌푒푎푟푙푦 퐼푛푓푟푎푠푡푟푢푐푡푢푟푒 퐶표푠푡 = 퐸푐표푛표푚𝑖푐 퐿𝑖푓푒푠푝푎푛 퐸푅푆

푌푒푎푟푙푦 푃푢푟푒 퐸푉 퐶표푠푡 = 푌푒푎푟푙푦 퐼푛푓푟푎푠푡푟푢푐푡푢푟푒 퐶표푠푡 + 푌푒푎푟푙푦 퐸푉 퐵푎푡푡푒푟푦 퐶표푠푡 (46)

In conclusion, the difference in cost between the two options was hence found by applying Equation (47) for the different countries. From this the countries where having an ERS based transport sector was more cost effective than a pure battery electric based sector were identified and analyzed.

퐴푛푛푢푎푙 퐸푅푆 퐶표푠푡 (47) % 퐶표푠푡 퐷𝑖푓푓푒푟푒푛푐푒 = 푌푒푎푟푙푦 푃푢푟푒 퐸푉 퐶표푠푡

Same as before, this entire modelling procedure was thereafter implemented for each of the two studied ERS technologies with two different pricing of battery, namely the current battery price and the predicted future battery price.

3.2 Model 2 – Sweden The aim of this model was to take a closer look at the potential of the different ERS technologies in Sweden using various modelling approaches. There were, in short, four different sub-models that were produced which scrutinized the prospect of ERS from different perspectives. These submodels are explained in more details in the following sections.

3.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV Combination The first sub-model was largely based on Model 1, but was optimized using input data for Sweden. Most of the optimized input data was Sweden specific information gathered from literature and Internet which could be applied directly, for example data regarding the fuel cost, electricity cost, the number of motor vehicles etc. However, some of the input data was required to be calculated before it could be used in the model. One of the most important input data that was computed was the total vehicle kilometer per year that each of the four vehicle types stood for. This information was paramount in calculating a number of important factors such as the average distance travelled, average electricity consumption of the vehicles etc. which are used both in Model 1 and Model 2.

The first step in computing the total vehicle kilometer per year was to find and summarize the total number of vehicles for each group. Furthermore, the average fuel consumption and average yearly distance travelled for each vehicle type was obtained by manipulating the different statistic data collected from Statistics Sweden. A simplified account of how the average fuel consumption and average yearly distance travelled was acquired for each vehicle type is summarized below.

For the passenger cars, the average distance travelled was simply found by looking at data tables provided by Statistics Sweden, as was the fuel consumption (l/km) for diesel and gasoline passenger cars. By using the statistics over the gasoline and diesel consumption per inhabitant, the total consumption and thus the percent division between gasoline and diesel was acquired (Equation (48) and (49)). From this, the average fuel consumption in liter per kilometer, taking into account both gasoline and diesel cars, was obtained through using Equation (50).

퐷𝑖푒푠푒푙 푈푠푒 퐺푎푠표푙𝑖푛푒 푈푠푒 (48) 푇표푡푎푙 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 = 푃표푝푢푙푎푡𝑖표푛 ∙ + 푃표푝푢푙푎푡𝑖표푛 ∙ 푃표푝푢푙푎푡𝑖표푛 푃표푝푢푙푎푡𝑖표푛

퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 푏푦 퐹푢푒푙 푇푦푝푒 (49) % 퐹푢푒푙 푇푦푝푒 = 푇표푡푎푙 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛

푙 푙 (50) 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 퐶푎푟푠 = % ∙ 퐷𝑖푒푠푒푙 + % ∙ 퐺푎푠표푙𝑖푛푒 퐴푣푒푟푎푔푒 퐷𝑖푒푠푒푙 푘푚 퐺푎푠표푙𝑖푛푒 푘푚

The process for computing these factors for light-duty trucks and heavy-duty trucks was a little different. The average distance travelled for each type could be obtained by manipulating the compiled statistic data. But, as the compiled data was split into several weight classes (See Model 2 Excel file), an overall average for light-duty trucks and heavy-duty trucks was calculated. This was done by finding information regarding the fuel consumption for different sized trucks, and where information was missing, relevant assumptions were made. Using these estimates by truck 45 size and the collected data regarding the vehicle kilometer per year for a number of truck sizes, the average fuel consumption for light-duty trucks and heavy-duty trucks was equated. The Equations (51) and (52) were applied separately for the truck sizes that fell under light-duty trucks and which were defined as heavy-duty trucks.

(51) 푇표푡푎푙 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 = ∑ 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛퐵푦 푠𝑖푧푒 ∙ 푉푒ℎ𝑖푐푙푒 푘푚퐵푦 푠𝑖푧푒

∑ 푉푒ℎ𝑖푐푙푒 푘푚퐵푦 푠𝑖푧푒 (52) 퐴푣푒푟푎푔푒 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 = 푇표푡푎푙 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛

Lastly, the average distance travelled and average fuel consumption for buses were found in the compiled statistic data (See Model 2 Excel file). Once these inputs were obtained for all the four vehicle types, the total vehicle kilometer per year could hence be computed. This was done through using Equation (53).

푉푒ℎ𝑖푐푙푒 퐾푚푦푒푎푟푙푦 = 푁푉푒ℎ𝑖푐푙푒푠 ∙ 퐷𝑖푠푡푎푛푐푒 푇푟푎푣푒푙푙푒푑퐴푣푒푟푎푔푒 ∙ 퐹푢푒푙 푈푠푎푔푒퐴푣푒푟푎푔푒 (53)

Once the yearly vehicle kilometer that each vehicle category contributed to was computed, the percentage of each category of the total vehicle kilometer in Sweden was obtained (Equation (54)).

푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟 푏푦 퐶푎푡푒푔표푟푦 (54) % 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟 푏푦 퐶푎푡푒푔표푟푦 = 푇표푡푎푙 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟

These values were afterwards an integral part in computing the weighted overall averages for a number of important inputs. The average fuel consumption, average distance travelled, average fuel price, average fuel emissions were all computed using the percentage fraction of vehicle kilometer by vehicle category. The weighted averages were compiled using the same analogy as for the average fuel consumption as shown in Equation (55). This procedure was performed to find overall weighted averages for all the vehicle categories as well as for only heavy vehicles. This was performed to represent the vehicle types that each ERS technology can utilize.

푊푒𝑖푔ℎ푡푒푑 퐴푣푒푟푎푔푒 = ∑ 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 ∙ % 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟 푏푦 퐶푎푡푒푔표푟푦 (55)

Another important input that was computed in this part of the model, and which was used both in Model 1 and Model 2, was the average electricity consumption for each of the vehicle categories. The average electricity consumption for passenger cars was obtained from statistic data compiled through literature survey. However, due to the fact that not many pure electric based vehicles exist on a commercial scale for the other categories, a different approach was taken in order to find their average electricity consumptions. The calculated fuel consumption for each vehicle type in combination with the fuel energy content, density and the vehicle well-to-wheel efficiency was used to compute the energy consumption per kilometer. This procedure is summarized by Equation (56) and (57).

푀퐽 퐹푢푒푙 퐸푛푒푟푔푦 퐶표푛푡푒푛푡 ( ) (56) 푀퐽 푘푔 퐹푢푒푙 퐸푛푒푟푔푦 퐶표푛푡푒푛푡 ( ) = 푙 푘푔 퐹푢푒푙 퐷푒푛푠𝑖푡푦 ( ) 푚3

푀퐽 (57) 퐸푛푒푟푔푦 퐶표푛푠푢푚푝푡𝑖표푛 ( ) = 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 ∙ 퐸푛푒푟푔푦 퐶표푛푡푒푛푡 ∙ 퐸푓푓𝑖푐𝑖푒푛푐푦 푘푚

Once the energy consumption per kilometer was computed, the energy needed for electric drive was equated using an approximated well-to-wheel efficiency for electric vehicles. Lastly, the electricity consumption for each vehicle type was obtained as shown below where 0.28 was used to convert MJ/km to kWh/km.

푀퐽 (58) 푀퐽 퐸푛푒푟푔푦 퐶표푛푠푢푚푝푡𝑖표푛 ( ) 퐸푛푒푟푔푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푙푒푐푡푟𝑖푐 퐷푟𝑖푣푒 ( ) = 푘푚 푘푚 퐸푓푓𝑖푐𝑖푒푛푐푦퐸푉푠

푀퐽 (59) 퐾푊ℎ 퐸푛푒푟푔푦 퐶표푛푠푢푚푝푡𝑖표푛 퐸푙푒푐푡푟𝑖푐 퐷푟𝑖푣푒 ( ) 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 ( ) = 푘푚 푘푚 0.28

The weighted average for the electricity consumption was computed in the same manner explained previously. Once these inputs were computed and other inputs were optimized for Sweden using different information sources, the rest of the procedure was exactly like Model 1.

3.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination The second model was also built on Model 1, but on the second part, which compared an ERS based passenger car fleet against a pure battery electric passenger car fleet. This comparison was applied on Sweden with optimized input data. An addition to the model was however made which sought to answer the question: How many electric cars would there need to be on the roads before having an ERS based transportation fleet would be cheaper than having a pure battery electric car fleet.

Hence, this addition in the model aimed to find the point when an ERS would become cheaper and the number of electric cars this point would correspond to. This procedure was performed quite simply by applying the modelling approach described in Model 1 for increasing number of passenger cars. From this, the cost of ERS and the cost of pure electric cars were acquired for a range of different number of cars. These costs were subsequently plotted against the number of cars, which finally gave the point of intersection between the two cost estimates. This method was done for the ERS technologies that support electric cars for different battery pricing.

3.2.3 Submodel 3 – Finding the Breakeven Point This submodel was developed with the goal of investigating the frequency of electric vehicles per day needed on an electrified road section that would result in a repayment of the investment. To be able to answer this question, a one kilometer electrified road length was analyzed. Using the fixed-rate mortgage method, the daily cost for a kilometer long section of electrified road was computed by applying Equation (60) and (61). 47

퐼푛푡푒푟푒푠푡 ∙ (1 + 퐼푛푡푒푟푒푠푡 )푇푒푐ℎ푛𝑖푐푎푙 퐿𝑖푓푒푠푝푎푛 (60) 퐶표푠푡 = 퐶표푠푡 퐸푅푆 ∙ 푦푒푎푟푙푦 (1 + 퐼푛푡푒푟푒푠푡 )푇푒푐ℎ푛𝑖푐푎푙 퐿𝑖푓푒푠푝푎푛 − 1

퐶표푠푡푦푒푎푟푙푦 (61) 퐶표푠푡 = 푑푎𝑖푙푦 360

Once the daily cost was computed, a range of frequency of vehicles per day was produced. For each different frequency, the fuel consumption cost and the electric consumption cost were computed as shown in the following equations.

푉푒ℎ𝑖푐푙푒푠 (62) 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 퐶표푠푡 = ∙ 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 ∙ 퐹푢푒푙 퐶표푠푡 푑푎푦

푉푒ℎ𝑖푐푙푒 (63) 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐶표푠푡 = ∙ 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 ∙ 퐸푙. 퐶표푠푡 푑푎푦

From this the economic savings from an electric based vehicle fleet compared to the current petroleum-based car fleet on this road could be calculated using Equation (64).

푆푎푣𝑖푛푔푠 = 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 퐶표푠푡 − 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푛푠푢푚푝푡𝑖표푛 퐶표푠푡 (64)

This was subsequently done for increasing frequency of vehicle per day until the breakeven point where the savings equaled the ERS investment costs was found. The procedure was repeated for the two ERS technologies, and lastly, figures were created to present the findings.

3.2.4 Submodel 4 – Expansion The purpose of developing this submodel was to simulate, using various scenarios, how an expansion of the different ERS technologies could look like in Sweden until the year 2050. To be able to approach and model such a vast problem, there were a couple of important assumptions that were found necessary to be made. To start with, the model was developed in such a way that it only regarded the expansion from an ERS perspective. To clarify, only the vehicle road work that was performed on the electrified roads was considered in the calculations and the savings from using battery drive on the normal roads were excluded. Similarly, only the cost related to constructing and maintaining the ERS was included in the expenditure, as such, the investment cost of batteries and pickup arm was not considered. Using this method, the accumulated savings, investment costs and thus also the result could be obtained for the electrified roads to see if they would lead to overall cost savings.

Due to the fact that Sweden has ambitious goals of having a fossil-free transport sector in the near future, it was presumed that the transport sector would be fully electric-based until 2050 at the latest. Furthermore, the total yearly vehicle kilometer, new vehicles per year and the total number of vehicles were assumed to be constant as explained in the assumption chapter. From this and using the data acquired from Swedish Transport Administration regarding the vehicle kilometer, equations that related the vehicle kilometer and traffic intensity as a function of road length could be produced.

The data regarding the vehicle kilometer per road category for all vehicle and only heavy trucks, as shown in Table 1 and Table 2, was acquired from the Swedish Transport Administration. Subsequently, the accumulated road length and vehicle kilometer could be computed.

Table 1. Yearly vehicle kilometer for all vehicles per road category during 2015. All vehicles (km) (km) (Mkm) (Mkm) Road length Accumulated Vehicle kilometer Accumulated vehicle road length kilometer European roads 6,700 6,700 23,000 23,000 National roads 8,900 15,600 14,000 37,000 Primary county roads 10,800 26,400 9,000 46,000

Other county roads 72,100 98,500 12,000 58,000 Total 98,500 58,000

Table 2. Yearly vehicle kilometer for heavy-duty trucks per road category during 2015. Heavy-duty trucks (km) (km) (Mkm) (Mkm) Million km Road length Accumulated Vehicle kilometer Accumulated road length Vehicle kilometer

European roads 6,700 6,700 3,170 3,170 National roads 8,900 15,600 1,701 4,871 Primary county roads 10,800 26,400 825 5,696 Other county roads 72,100 98,500 834 6,530 Total 98,500 6,530

The accumulated road length and vehicle kilometer from the tables provided points that related vehicle kilometer to the road length. However, as a smooth curve was needed that correlated the vehicle kilometer per each road kilometer, a formula was produced through curve fitting that gave the correct values. Sequentially, a formula that related the traffic intensity, viz. vehicles per day, to the road kilometer could also be computed. These curve fitted figures are shown below and they show that majority of the vehicle kilometer and thus also the highest traffic intensity is on the major roads in Sweden. A fundamental assumption that was undertaken as a natural implication from the vehicle kilometer function was that the most traffic intensive roads would be electrified at first. The reasoning behind this was quite straightforward, as these roads would be where the biggest savings could be made due to the high traffic intensity. The same function for vehicle kilometer and traffic intensity by road length were used for heavy vehicles, however, they were calibrated to fit the data for heavy-duty trucks.

Vehicle kilometer as a function of road length

Accumulated distance travelled by vehicles [Trafikverket] 70

20 BILLION VEHICLE KILOMETERBILLIONVEHICLE

ROAD LENGTH IN THOUSAND KILOMETER

Figure 30. Vehicle kilometer as a function of road length.

Traffic intensity as a function of road length Traffic intensity 1,000,000

100,000

10,000

1,000 VEHCILES/DAY 100

ROAD LENGTH IN THOUSAND KILOMETER Figure 31. Traffic intensity as a function of road length.

Once a function of the vehicle kilometer related to the road length was computed, the rest of the submodel could be built around it. It is important to note, that each of the calculations described below were performed for each year in sequence. To begin with, the total expansion needed for each technology was set as the total kilometer electrified road needed in Sweden as computed in submodel 1 in Equation (65) for future battery price. A variable input for the yearly kilometer of road that would be electrified was implemented which could be altered through changing the annual percentile growth rate and max kilometer electrification per year. All of these inputs were changed depending on which scenario that was analyzed, and more on the specifics of each scenario will be explained later.

2050 (65) 푇표푡푎푙 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 = ∑ 푌푒푎푟푙푦 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 퐸푥푝푎푛푠𝑖표푛 2016

Using the formula for vehicle kilometer produced earlier, the yearly accumulated vehicle kilometer that was executed on the electrified roads could then be equated by adding the yearly vehicle kilometer executed on the new electrified road sections. This procedure is shown in Equation (66) and (67).

푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟푁푒푤 푟표푎푑 = 푓(푌푒푎푟푙푦 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 퐸푥푝푎푛푠𝑖표푛) (66)

2050 (67) 퐴푐푐푢푚푢푙푎푡푒푑 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟 = ∑ 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟푁푒푤 푟표푎푑 2016

From this, the vehicle kilometer that was performed by electric vehicles could then be equated through a number of steps. To begin with, a variable for the percentage of new vehicles sold per year that would be EVs was defined. This variable was increased yearly by different percentile values, depending on the scenario, until 100 % of the sales of new vehicles were electric vehicles. Consequently, the accumulated number of electric vehicles that were added each year could be computed. These steps are shown in Equation (68) and (69).

푁푒푤 퐸푉푦푒푎푟푙푦 = 푁푒푤 푉푒ℎ𝑖푐푙푒푠푦푒푎푟푙푦 ∙ % 퐸푉 표푓 푁푒푤 푉푒ℎ𝑖푐푙푒푠푦푒푎푟푙푦 (68)

2050 (69) 퐴푐푐푢푚푢푙푎푡푒푑 퐸푉푠 = ∑ 푁푒푤 퐸푉푦푒푎푟푙푦 2016

By each year using the value for accumulated EVs, the percent of the vehicle kilometer performed on the electrified roads that EVs contributed to could then be computed.

퐴푐푐푢푚푢푙푎푡푒푑 퐸푉푠 ∙ 퐴푣푒푟푎푔푒 퐷𝑖푠푡푎푛푐푒 푇푟푎푣푒푙푙푒푑 % 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 = 푇표푡푎푙 푁푉푒ℎ𝑖푐푙푒푠 ∙ 퐴푣푒푟푎푔푒 퐷𝑖푠푡푎푛푐푒 푇푟푎푣푒푙푙푒푑

퐴푐푐푢푚푢푙푎푡푒푑 퐸푉푠 (70) → % 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 = 푇표푡푎푙 푁푉푒ℎ𝑖푐푙푒푠

Lastly, the vehicle kilometer travelled each year by the electric vehicles can be computed using Equation (71).

푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 = % 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 ∙ 퐴푐푐푢푚푢푙푎푡푒푑 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟 (71)

Once all these inputs were defined, the yearly and thus also the accumulated expenditure, savings and results could be derived. Beginning with the expenditures, these were assumed to be the cost for the electrified roads, road renovation and road maintenance. The yearly electrified road investment was computed by simply multiplying with the yearly expansion (Equation (72)). The yearly road renovations were presumed to occur on the old roads once their technical lifespan was reached. It was calculated in the same way as the road investment. Lastly, the road maintenance was equated using a cost input for the daily cost per kilometer road length through Equation (74).

푅표푎푑 퐼푛푣푒푠푡푒푚푒푛푡푦푒푎푟푙푦 = 푌푒푎푟푙푦 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 퐸푥푝푎푛푠𝑖표푛 ∙ 퐶표푠푡 퐸푅푆 (72)

푅표푎푑 푅푒푛표푣푎푡𝑖표푛푦푒푎푟푙푦 = 푌푒푎푟푙푦 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 푅푒푛표푣푎푡𝑖표푛 ∙ 퐶표푠푡 퐸푅푆 (73)

퐶표푠푡푦푒푎푟푙푦 (74) 푅표푎푑 푀푎𝑖푛푡푒푛푎푛푐푒 = ∙ 푌푒푎푟푙푦 퐸푙푒푐푡푟𝑖푓𝑖푒푑 푅표푎푑 퐸푥푝푎푛푠𝑖표푛 푦푒푎푟푙푦 푘푚

The accumulated expenditure was thereafter computed by adding these three costs for each year. Next the yearly and accumulated savings were computed by subtracting the cost of fuel that would have been used compared to the electricity used by the EVs on the electrified roads. This process can be seen in Equation (75), (76), (77) and (78).

퐹푢푒푙 퐶표푠푡푦푒푎푟푙푦 = 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 ∙ 퐹푢푒푙 퐶표푛푠푢푚푝푡𝑖표푛 ∙ 퐹푢푒푙 푃푟𝑖푐푒 (75)

퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푠푡푦푒푎푟푙푦 = 푉푒ℎ𝑖푐푙푒 퐾𝑖푙표푚푒푡푒푟퐸푉 ∙ 퐸푙. 퐶표푛푠푢푚푝푡𝑖표푛 ∙ 퐸푙. 푃푟𝑖푐푒 (76)

푆푎푣𝑖푛푔푠푦푒푎푟푙푦 = 퐹푢푒푙 퐶표푠푡푦푒푎푟푙푦 − 퐸푙푒푐푡푟𝑖푐𝑖푡푦 퐶표푠푡푦푒푎푟푙푦 (77)

2050 (78) 퐴푐푐푢푚푢푙푎푡푒푑 푆푎푣𝑖푛푔푠 = ∑ 푆푎푣𝑖푛푔푠푦푒푎푟푙푦 2016

Finally, the yearly and accumulated result was produced by subtracting the expenditure from the savings. In Equation (79), the process is shown for only the accumulated result which is done each year.

퐴푐푐푢푚푢푙푎푡푒푑 푅푒푠푢푙푡 = 퐴푐푐푢푚푢푙푎푡푒푑 푆푎푣𝑖푛푔푠 − 퐴푐푐푢푚푢푙푎푡푒푑 퐸푥푝푒푛푑𝑖푡푢푟푒 (79)

The accumulated values can thereafter be plotted against each year to get a figure that shows the cost development. Additionally, the emission reduction result was also computed, and the method

52 used was analogues to the Equations (75), (76), (77). However, the emission reduction calculations used the values for emissions instead of costs in the equations.

The Scenarios As discussed briefly earlier, the expansion model was implemented on different scenarios for each of the three studied ERS technologies. The scenarios were developed by the author and aimed to reflect different possible futures which were split into three potential futures. These three scenarios are explained in detail below.

Scenario 1. In this scenario, the government is convinced that ERS is the future of the transportation sector. As a result, there is high governmental engagement and influence for a fast expansion and implementation of ERS. Also, increasing oil prices due to shortage, in combination with governmental incentives have resulted in a rapid growth of electrical vehicles in Sweden.

Scenario 2. For this scenario, the government is not completely convinced that ERS is the solution for Sweden’s road transportation sector. Due to this fact, there is medium governmental engagement and influence for an expansion and implementation of ERS. Additionally, oil prices continue to be low and combined with weak governmental support, has resulted in a moderate increase in the yearly sales of electric vehicles. Scenario 3. Lastly, in this scenario there is low governmental commitment and influence leading to a slow expansion and implementation of ERS. Also, the continuing low oil prices and low governmental engagement have resulted in a very slow penetration of electric vehicles in the market.

From these scenarios, some specifications could thus be produced and implemented into the expansion model. These specifications for each scenario are summarized in Table 3. It is important to note that these specification and predictions have been developed by the author and are thus very much speculative. The thinking behind the scenarios is to merely give a notion about how a potential future expansion of ERS might look like. Table 3. Expansion specifications for each scenario. Specifications ERS expansion EV expansion Scenario 1 – Fast 2016-2027, 11 years - Until 2024, 100 % of new vehicles sold are EVs. - All motor vehicles are EVs until 2035.

Scenario 2 - Medium 2016-2036, 20 years - Until 2030, 100 % of new vehicles sold are EVs. - All motor vehicles are EVs until 2039.

Scenario 3 - Slow 2016-2049, 33 years - Until 2043, 100 % of new vehicles sold are EVs. - All motor vehicles are EVs until 2048.

4 RESULTS

In this chapter the results obtained from the models described are presented. In the same way as the models, the results are split into two main parts. That is, a section that presents the prospect of ERS from a global perspective and one that investigates the potential more closely for Sweden.

4.1 Prospect of Electrical Road Systems in the World 184 countries were analyzed and evaluated to see whether ERS solutions could be a viable option for them from primarily an economic standpoint. The modelling was split into two main scenarios where the prospect of the three current ERS technologies was investigated. Firstly, the ERS technologies in combination with electric vehicles were compared against the current petroleum- based transportation system to assess the potential economic and environmental gains. Secondly, the investment cost of replacing conventional passenger cars with 100 % electric based passenger cars, including the infrastructure modifications, was computed. This was then compared against the investment cost of ERS solutions in combination with electric cars to assess which alternative would be the best from an economic perspective. These two comparisons are presented in more detail in the following sections.

4.1.1 Comparison – Petroleum-based Road Transport against ERS and EV Combination Using the optimization procedure described in the methodology section, the optimal length of the electrified roads and battery size needed was computed for each country. This procedure was reiterated for each technology for two different battery cost scenarios. Subsequently, the cost of installing a full-scale ERS was compared and analyzed with the cost of the current petroleum fuel based system.

To identify the regions/countries in the world that are the most suited for ERS installations, the countries where the yearly savings were found to be positive for an ERS based transport system were sorted. This was done for each ERS technology and battery cost scenario. Consequently, some global overall factors could then be computed for these countries, the most important of these are presented in Table 4 and Table 5. In Appendix B, a list with each individual country where the yearly savings were found to be positive for the conductive road technology can be found. This list demonstrates how the computing of the main factors was performed for each ERS technology and battery cost scenario.

Table 4. Most important global results for each ERS technology in countries where the yearly savings are positive, assuming 2015 battery pricing. Scenario Countries Yearly Electrified Battery Percent Percent of Percent of Percent Percent Percent Savings Road Size of Total Yearly of GHG of Total of Total 2015 Existing Electricity BNP Emission Vehicles Heavy Road Production (Total Reduced Globally Vehicles Network ERS Globally Cost) # MSEK/year km kWh % % % % % % Conductive 87 2,500,000 4,300,000 4 19.9% 17.3% 2.7% 4.9% 79% 79% Road Overhead 57 200,000 610,000 8 4.3% 6.4% 0.2% 0.7% 1% 55% Conductive Inductive 48 900,000 480,000 4 7.5% 22.7% 3.1% 1.9% 30% 30% Road

Table 5. Most important global results for each ERS technology in countries where the yearly savings are positive, assuming future battery pricing. Scenario Countries Yearly Electrified Battery Percent Percent of Percent of Percent Percent Percent Future Savings Road Size of Total Yearly of GHG of Total of Total Existing Electricity BNP Emission Vehicles Heavy Road Production (Total ERS Reduced Globally Vehicles Network Cost) Globally # MSEK/year km kWh % % % % % % Conductive 116 3,700,000 3,600,000 8 11.3% 16.7% 1.7% 5.6% 91% 91% Road Overhead 86 500,000 630,000 17 2.9% 5.2% 0.2% 1.0% 2% 78% Conductive Inductive 76 2,100,000 1,210,000 11 6.0% 17.3% 3.0% 4.6% 75% 75% Road

To easily illustrate how the cost of different ERS technologies and the batteries affect the number of countries that are suitable for ERS, Figure 32 has been produced. It displays that the number of countries that are viable for ERS implementation increase with decreasing ERS installation and battery costs.

Number of countries where the yearly saving is positive for different ERS technologies and battery cost 140 116 120

100 87 86 76 80 57 60 48

40 Number of countries of Number

0 Conductive Road Overhead Conductive Inductive Road

Scenario 2015 Scenario Future

Figure 32. Number of countries where the yearly profit is positive for different ERS technologies and battery cost scenarios. Similarly, Figure 33 has been produced to illustrate how big percentage of the global vehicle fleet the countries where the yearly savings are positive account for. Whereas in Figure 34, the same has been done, but for only heavy vehicles. It also demonstrates that the percentage of the total global vehicle increase with decreasing ERS installation and battery costs.

Percentage of total global vehicles in countries where the yearly saving is positive 100% 91% 90% 79% 80% 75% 70% 60%

50% global vehicles global 40%

total 30%

30% %of 20% 10% 1% 2% 0% Conductive Road Overhead Conductive Inductive Road

Scenario 2015 Scenario Future

Figure 33. Percentage of total number of vehicles worldwide in countries where the yearly saving is positive for different ERS technologies and battery cost scenarios.

Percentage of total heavy vehicles in countries where the yearly saving is positive 100% 91% 90% 79% 78% 80% 75% 70% 60% 55% 50% 40% 30%

30% % of total global vehicles global %total of 20% 10% 0% Conductive Road Overhead Conductive Inductive Road

Scenario 2015 Scenario Future

Figure 34. Percentage of total number of heavy vehicles worldwide in countries where the yearly saving is positive for different ERS technologies and battery cost scenarios. To also include an environmental perspective, Figure 35 was produced. It illustrates how big reduction in the global total GHG emissions could be achieved by implementing ERS in countries with positive yearly savings.

Percent of total global GHG emissions reduced by countries where the ERS yearly saving are positive 6.0% 5.6%

4.9% 5.0% 4.6%

4.0%

3.0%

1.9% 2.0% 1.0% % GHG emission reduction emission % GHG 1.0% 0.7%

0.0% Conductive Road Overhead Conductive Inductive Road

Scenario 2015 Scenario Future

Figure 35. Percent of global total GHG emissions reduced by implementing the different ERS technologies in countries where the yearly savings are positive for different and battery cost scenarios.

To get an overview over which regions and countries that are suitable for ERS, Figure 36 was produced. It shows the percentage of the countries in the world that are suitable for conductive ERS solution assuming the current battery pricing. It also presents the country percentage for each individual continent. It should be noted that for this comparison, all the recognized countries in the world were counted in.

Figure 36. The percentage of the countries in the world/continents that are suitable for conductive power transfer from road solution assuming the current battery pricing of 350 USD/kWh. In the same way, Figure 37 illustrates the percentage of the countries/continents in the world that are suitable for conductive power transfer from road assuming the predicted future battery pricing.

Figure 37. The percentage of the countries in the world/continents that are suitable for conductive power transfer from road solution assuming the predicted future battery pricing of 120 USD/kWh.

4.1.2 Comparison – Pure Battery Electric Car fleet against ERS and Electric Car Combination In similar fashion as for the comparison between ERS and petroleum-based fuels. The process described in the modelling chapter was applied to produce a comparison between a future where electric passenger cars use only batteries for propulsion and one where both ERS and batteries are used in combination. This comparison was made for the conductive and inductive road technologies using two battery cost scenarios, namely, the current and future predicted battery pricing. The comparison was done by calculating how much each future would cost and thereafter computing the cost difference in percent between the two. In Table 6 and Table 7 this has been done for all the 184 countries by computing a global cost. In this comparison all countries are included, regardless of whether the ERS plus battery combination is found less expensive or not.

Table 6. The cost difference in percent between the two cases for all 184 countries assuming 2015 battery pricing. Scenario Countries Percent of Total Cars Yearly ERS + Yearly Pure EV + Cost Difference 2015 in the World EV Cost Infrastructure Cost

# % MSEK/Year MSEK/Year % Conductive 184 100% 2,600,000 37,200,000 6.9% Road Inductive 184 100% 4,600,000 37,200,000 12.2% Road

Table 7. The cost difference in percent between the two cases for all 184 countries assuming future battery pricing. Scenario Countries Percent of Total Cars Yearly ERS + Yearly Pure EV + Cost Difference Future in the World EV Cost Infrastructure Cost

# % MSEK/Year MSEK/Year % Conductive 184 100% 1,700,000 13,300,000 12.9% Road Inductive 184 100% 2,900,000 13,300,000 21.8% Road

In the next step, the countries where the cost of ERS in combination with batteries was found to be lower than a pure battery electric passenger car case, were summarized. Subsequently, the overall yearly cost in these countries could be computed to find the price difference and other interesting factors as shown in Table 8 and Table 9. In Appendix C, a complete summarized list for each country can be found for conductive ERS technology with 2015 battery pricing which exemplifies how the calculations were performed for each ERS technology and battery pricing.

Table 8. Number of countries where the ERS solution is cheaper and the cost difference between the two cases assuming 2015 battery pricing. Scenario Countries Percent of Total Cars Yearly ERS + Yearly Pure EV + Percent Cost 2015 in the World EV Cost Infrastructure Cost Difference

# % MSEK/Year MSEK/Year % Conductive 181 99.99% 2,100,000 37,200,000 5.6% Road Inductive 173 99.97% 3,600,000 37,200,000 9.7% Road

Table 9. Number of countries where the ERS solution is cheaper and the cost difference between the two cases assuming future battery pricing. Scenario Countries Percent of Total Cars Yearly ERS + Yearly Pure EV + Percent Cost Future in the World EV Cost Infrastructure Cost Difference

# % MSEK/Year MSEK/Year % Conductive 177 99.98% 1,400,000 13,300,000 10.8% Road Inductive 164 99.87% 2,300,000 13,300,000 17.5% Road

Lastly, an illustrative summary has been made of the cost differences for each scenario presented in the tables. The figure shows that an ERS based car fleet would cost less compared to a pure battery electric car fleet for all the studied scenarios.

Cost difference between the two futures for different ERS technologies and battery pricing 25% 21.8%

20% 17.5%

15% 12.9% 12.2% 10.8% 9.7% 10% 6.9% %difference Price 5.6% 5%

0% Conductive Road Overhead Conductive

Scenario 2015 Scenario 2015 - All countries Scenario Future Scenario Future - All countries

Figure 38. A summarization of all the cost differences presented in the previous tables

4.2 Prospect of Electrical Road System in Sweden In this model, the potential of an ERS based transportation system in Sweden was analyzed. Several submodels were produced that studied the problem from different perspectives. The findings obtained from the submodels are presented in the following subsections.

4.2.1 Submodel 1 – Comparing a Petroleum-based Road Transport System against ERS and EV Combination This submodel was in essence based on the Model 1 comparison between a petroleum-based transportation system and an ERS based one. However, the biggest difference was that the input data used in this submodel was optimized for Sweden. Consequently, the yearly savings/losses of an ERS based transportation system could be computed for each ERS technology and battery price scenario for Sweden. The most important results computed are shown in Table 10 and Table 11 for each technology and battery cost scenario.

Table 10. The most important results obtained for the three ERS technologies in Sweden assuming 2015 battery pricing. Scenario Yearly Electrified Battery Total Total Percent Percent of Percent of Percent 2015 Savings Road Size Cost of ERS of Total Yearly BNP Reduced of Road Cost Existing Electricity (Total ERS Transport Road Production Cost) Emissions Network MSEK/year km kWh MSEK MSEK % % % % Conductive 12,400 40,200 5 161,000 354,000 19% 13% 9% 92% Road Overhead -420 9,700 90 60,000 124,000 5% 4% 3% 23% Conductive Inductive -9,760 20,800 9 311,000 655,000 10% 13% 16% 92% Road

Table 11. The most important results obtained for the three ERS technologies in Sweden assuming future battery pricing. Scenario Yearly Electrified Battery Total Total Percent Percent of Percent of Percent Future Savings Road Size Cost of ERS of Total Yearly BNP Reduced of Road Cost Existing Electricity (Total ERS Transport Road Production Cost) Emissions Network MSEK/year km kWh MSEK MSEK % % % % Conductive 21,900 24,000 8 96,000 225,000 11% 13% 5% 92% Road Overhead 3,000 5,800 150 35,000 77,000 3% 4% 2% 23% Conductive Inductive 8,700 12,400 16 186,000 405,000 6% 13% 10% 92% Road

The yearly saving or loss for each technology and battery pricing has thereafter been summarized in a illustrative manner in Figure 39.

The yearly savings/loss for each ERS technology and battery cost scenario 25,000 21,900

20,000

15,000 12,400

10,000 8,700

5,000 3,000 -420

MSEK/Year 0

-5,000

-10,000 -9,760 -15,000 Conductive Road Overhead Conductive Inductive Road

Scenario 2015 Scenario Future

Figure 39. A summarization of the yearly savings or losses for each ERS technology and battery cost scenario.

4.2.2 Submodel 2 – Comparing a Pure Battery Electric Car Fleet against ERS and Electric Car Combination Similarly, this submodel was built on the part of Model 1 that compared a fully expanded pure battery electric car fleet against a transportation system that utilizes both ERS and electric cars in combination. The yearly cost for each case could hence be computed and compared with each other. In Table 12 and Table 13, the cost for each case is presented for the two studied ERS technologies and battery pricing.

Table 12. The cost difference between the two cases for the two examined ERS technologies assuming 2015 battery pricing. Scenario 2015 Yearly ERS + EV Yearly Pure EV + Percent Cost Difference Cost Infrastructure Cost MSEK/Year MSEK/Year % Conductive Road 10,305 173,923 6% Inductive Road 17,977 173,923 10%

Table 13. The cost difference between the two cases for the two examined ERS technologies assuming future battery pricing. Scenario Future Yearly ERS + EV Yearly Pure EV + Percent Cost Difference Cost Infrastructure Cost MSEK/Year MSEK/Year % Conductive Road 7,008 62,285 11% Inductive Road 11,593 62,285 19%

Furthermore, this model aimed to find the point when an ERS based transportation system would become cheaper than a pure electric car based one, and the number of electric cars this point would

62 correspond to. This was performed for both the conductive road and inductive road ERS technologies using the two different battery cost scenarios, as demonstrated in Figure 40. The maximum number of pure electric cars before an ERS based transportation system becomes less expensive assuming 2015 battery pricing was found to be approximately 15 000 and 45 000 cars for conductive road and inductive road technologies respectively. Likewise, Figure 41 exhibits the maximum number of electric cars, assuming future battery pricing, to be roughly 35 000 cars for conductive technology and 130 000 cars for inductive technology.

Pure EV vs ERS Combination Case - 2015 Yearly ERS + EV Cost - Conductive Yearly Pure EV + Infrastructure Cost Yearly ERS + EV Cost - Inductive 18,000

16,000

14,000

12,000

10,000

8,000

6,000 45,000 Cars

INVESTMENT (MSEK/YEAR) INVESTMENT 4,000

2,000

0 15,000 Cars

NUMBER OF ELECTRIC CARS

Figure 40. The number of cars required until an ERS based system is cheaper for the two examined ERS technologies assuming 2015 battery pricing.

Pure EV vs ERS Combination Case - Future Yearly ERS + EV cost - Conductive Yearly Pure EV + Infrastructure Cost Yearly ERS + EV Cost - Inductive 7,000

6,000

5,000

4,000 130,000 Cars 3,000

2,000 INVESTMENT (MSEK/YEAR) INVESTMENT 1,000 35,000 Cars 0

NUMBER OF ELECTRIC CARS

Figure 41. The number of cars required until an ERS based system is cheaper for the two examined ERS technologies assuming future battery pricing.

4.2.3 Submodel 3 – Finding the Breakeven Point This submodel was developed to find the frequency of electric vehicles required on an electrified road section to repay the initial investment cost (operation costs are not included) for different ERS technologies. The frequency breakeven point was hence computed for each technology and is illustrated in Figure 42 and Figure 43. As illustrated in the figures, it was found that a minimum frequency of 1650 and 6150 EVs per day is required for conductive and inductive ERS technologies respectively. Whereas, for overhead conductive technology, a minimum frequency of 670 heavy electric vehicles per day was computed.

Breakeven Points for Inductive and Conductive Road Technologies Conductive Road Cost Savings Inductive Road Cost 4000

3500

3000

2500 6150 EVs/day 2000

SEK/DAY 1500

1000

500 1650 EVs/day 0

ELECTRIC VEHICLES/DAY

Figure 42. The frequency breakeven points for inductive and conductive ERS technologies.

Breakeven Point for Overhead Conductive Technology

Conductive Overhead Cost Savings 1800

1600

1400

1200

1000 670 HEVs/day

800 SEK/DAY 600

400

200

HEAVY ELECTRIC VEHICLES/DAY

Figure 43. The frequency breakeven point for overhead conductive ERS technology.

4.2.4 Submodel 4 – Expansion The final submodel was produced to simulate how an expansion of the different ERS technologies could look like in Sweden until the year 2050. The investment costs and savings were only viewed from an electrified road perspective as explained in the MODELS chapter. Three different expansion scenarios of Electrified Road Systems and electric vehicles were scrutinized and implemented on the model, where Scenario 1 simulated a fast expansion, Scenario 2 a medium fast expansion and Scenario 3 a slow expansion. In the following figures the accumulated results, which were found by subtracting the accumulated savings from the accumulated expenditures, are presented for each ERS technology and scenario.

Accumulated Result for all Scenarios - Conductive Road Accumulated Result - Scenario 1 (Fast) Accumulated Result - Scenario 2 (Medium) Accumulated Result - Scenario 3 (Slow) 350,000

300,000

250,000

200,000

150,000

100,000 MSEK/YEAR 50,000

-50,000

-100,000

Figure 44. Accumulated result for the three different scenarios for conductive road ERS technology.

Accumulated Result for all Scenarios - Overhead Cond. Accumulated Result - Scenario 1 (Fast) Accumulated Result - Scenario 2 (Medium) Accumulated Result - Scenario 3 (Slow) 50,000

40,000

30,000

20,000

10,000

0 MSEK/YEAR -10,000

-20,000

-30,000

-40,000

Figure 45. Accumulated result for the three different scenarios for overhead conductive ERS technology.

Accumulated Result for all Scenarios - Inductive Road Accumulated Result - Scenario 1 (Fast) Accumulated Result - Scenario 2 (Medium) Accumulated Result - Scenario 3 (Slow) 0

-20,000

-40,000

-60,000

-80,000

-100,000 MSEK/YEAR -120,000

-140,000

-160,000

-180,000

Figure 46. Accumulated result for the three different scenarios for inductive road ERS technology.

As it was found that the largest saving until year 2050 was obtained from Scenario 1 for all the ERS technologies. The accumulated result from all the technologies has thus been summarized in Figure 47 for Scenario 1 to display these results side by side.

Accumulated Result for Scenario 1 - All Technologies

Conductive Road ERS Overhead Conductive ERS Inductive Road ERS 400,000

300,000

200,000

100,000 MSEK/YEAR 0

-100,000

-200,000

Figure 47. Accumulated result obtained from Scenario 1 for all ERS technologies. Lastly, the reduction of GHG emissions per year was also plotted out for the three scenarios. Figure 48 demonstrates how the yearly percentile emission reduction obtained by shifting to an electric based transportation system would appear for the conductive and inductive road technologies with the different scenarios. Similarly, the emission reduction for each scenario for the overhead conductive ERS would have the same plot shape. However, the difference being that the maximum percentile GHG emission reduction from the transportation sector would in this case be 23 % as opposed to the 92 % shown in the figure. This, of course, is due to the fact that the overhead conductive technology can only be utilized by heavy vehicles.

% GHG emission reduction from transportation sector Reduction in Emissions - Scenario 1 (Fast) Reduction in Emissions - Scenario 2 (Medium) Reduction in Emissions - Scenario 3 (Slow) 100%

90%

80%

70%

60%

50%

40%

30% % EMISSION REDUCTION %EMISSION 20%

10%

Figure 48. The yearly development of GHG emission reduction for the three different scenarios for conductive and inductive ERS technology.

5 DISCUSSION

In this chapter, a discussion of the results and the conclusions that have been drawn during this thesis is presented. The conclusions are based on the reasoning and arguments invoked during the analysis and aim to answer the thesis questions presented in the introductory chapter.

5.1 Prospect of Electric Road Systems Compared to the Conventional Road Transportation System The results obtained in this thesis for the economic comparison between an ERS based transportation structure against the current conventional road transportation system are scrutinized and analyzed in this section for a global and a Swedish perspective.

5.1.1 The Global Perspective Perhaps the most significant finding revealed by the first model is that an ERS based transportation system is, in fact, not suitable for every country in the world. This result, albeit being rather intuitive, still displays that ERS call for certain requirements in a country to be feasible. However, that been said, looking at Figure 33 it can be seen that a large number of countries in the world were indeed found to have good prospects for ERS from an economic standpoint. The technology which was found to result in savings in most number of countries, compared to the current petroleum-based system, is the conductive road ERS technology followed by overhead conductive and inductive road ERS technologies. Furthermore, the figure illustrates that the number of prospective countries for each technology increases with decreasing battery cost. This is a logical outcome as a lower cost of battery will unsurprisingly also decrease the overall investment cost for an ERS based transportation system thus making it suitable for even more countries.

However, an interesting deduction that can be derived from viewing the world map over prospective countries (Figure 36 and Figure 37) is that most of these countries are either small and/or developed countries. This can be explained by considering the fact that the higher the car density is in a country, the cheaper the implementation of an ERS becomes. This is directly caused by the fact that low car densities lead to larger optimal grid-sizes (∆) in the countries, which in turn results in a need for larger batteries. These parameters together often result in that the yearly cost of the ERS solutions becomes more expensive than the yearly cost of the current road transportation system. Consequently, from this it can be said that rich and developed countries, who typically have high car density, are usually good candidates for ERS. This is further supported by viewing Figure 33 and Figure 34, which illustrates that even though not more than 2/3 of the countries at best are found suitable for ERS, the majority of the total global vehicle fleet can be found in these countries. Hence, the region of the world with by far the most number of countries suitable for ERS was found to be Europe and the region with least suitable countries was found to be Africa.

Furthermore, these discoveries point to an interesting conclusion. As the number of vehicles in the world most likely will keep increasing in the future, this could result in the fact that even more countries will eventually become attractive for ERS solutions. Additionally, as oil prices realistically will see an increase the future, this might also contribute to further the appeal for ERS. However, this will most likely be negated by increasing electricity prices due to wider 70 implementation of renewable energy sources and possible imposition of taxes. The battery development is another important factor that could affect ERS positively if battery prices continue to decrease as dramatically as predicted. Thus considering these facts, it is safe to assume that the ERS technologies will continue to become progressively more attractive.

Parameters that often are mentioned as problematic by sceptics are the huge investment costs, the increase in electricity production required and the length of the conventional road that needs to be electrified. Even though the investment costs for an ERS based transportation system are considerable, when related to other major infrastructure investments such as rail road expansions, they are found to be comparable. Furthermore, Table 4 and Table 5, display that the average percent of the yearly BNP that an ERS implementation requires in suitable countries, is around a few percent. While being a substantial amount of money, it is still not an unrealistic amount in light of similar major infrastructure projects. Similarly, the length of road necessary to electrify for the different ERS technologies is but a portion of the total road network available in the countries. Lastly, it can be seen in the tables that in the worst case, approximately 1/5 of the current electricity production will be required to power a full-scale ERS transportation sector. This might seem like a lot at first, but when considering that the energy required from the entire road transportation is included in this increase, the required electricity production growth could perhaps be motivated.

To also include the environmental perspective, Figure 35 demonstrates that a considerable part of the total GHG emissions in suitable ERS countries can be reduced by implementing the ERS solutions. Naturally, the emission reduced from implementing the overhead conductive ERS technology is considerably lower than conductive and inductive road technologies. This is due to the fact that only heavy vehicles, which constitute only a small portion of the total vehicle fleet, can utilize the overhead solution.

5.1.2 The Swedish Perspective The results obtained for this comparison for Sweden in Figure 39 display that only the conductive road technology will give positive yearly savings if considering the current battery pricing. Yearly savings of approximately 12 billion SEK can be attained which point to that there are business opportunities as well as benefits for the society to be gained. The main reason why the inductive and the overhead conductive technologies experience no profit is simply due to the fact that the battery pricing and the cost of electrifying the roads are is too expensive. Thus, the savings that are acquired by using electricity instead of petroleum-based fuels are insufficient. However, in the case where future battery pricing is used, the results are very different. For this case, all three ERS technologies are projected to result in savings, but still the largest savings are made with a conductive based ERS. One thing to note, however, is that as the overhead conductive transfer technology can only be utilized by large/heavy vehicles, the total savings that can be made are therefore considerably smaller than for conductive and inductive road systems. This is also true for the other parameters computed in Table 10 and Table 11 on page 60.

Reflecting on these parameters, it is projected that the total investment cost for a full-scale implementation of an ERS ranges between 2-16 % of the yearly Swedish GDP depending on the ERS technology used and battery price scenario assumed. There is no doubt that this will require considerable investments. However, when bearing in mind the economic and environmental gains, 71 decrease in energy usage and reduction in the oil-dependency that will be achieved, and in addition further considering the Swedish goal of having a fossil-fuel free transportation sector until 2030, the initial investment costs can thus be motivated as a necessary evil. Moreover, parameters presenting the percent electricity production increase required and total road needed to be electrified also show rather large numbers. Yet, bearing in mind the potential gains that can be acquired, they are perhaps not unrealistic and could be achievable. Thus, it can be concluded that with current gasoline/diesel prices in this report, all three ERS technologies are economically viable if the battery prices come down to around 120 USD/kWh. But with current battery prices, only conductive road technology seems to be a viable solution.

5.1.3 Validity and Limitations of the Model The model developed for the comparison is a simplification of reality and is thus built on major assumptions. However, any model developed that is required to mimic a real life system almost always necessitates simplifications. It may be possible to nit-pick each and every assumption made for this model to try and improve the models reliability towards the real life system. But, in the end, one has to also develop a model that is easily understandable and which converts a complex system into a manageable one. Thus, it is judged that the questions that the model was developed to answer, which mainly was to get a notion of a) whether the different ERS technologies even are viable solutions from predominantly an economic standpoint and b) which countries in the world that have the worst/best prospect, were answered reasonably well. Furthermore, as mentioned in the delimitation section, this model does in no way calculate the real total economic costs associated to a full-scale ERS expansion and should not be assumed to do so. It merely aims to compare the degree of difference in costs between the conventional transportation system and a potential ERS based transportation system.

That being said however, there are a couple of major assumptions that could have been improved further to increase the validity of the results. To begin with, one of the fundamental assumptions that the model is built on is that the road network in all countries has been approximated as being meshed quadratically and equally over the entire country land area. Even though it has been shown in the frame of reference sections that many countries do indeed have very quadratically sectioned road networks, this is not true for every country. Especially the not as developed countries and countries with large land area usually have regions with dense road network and on the other hand regions with little to no road network. For example countries such as China, Russia and Australia have large regions that are completely uninhabited. Hence, it would make more sense to make a model that only considers the inhabited land areas. This would most probably lead to even more countries becoming suitable for ERS as less land area will be required to be electrified.

Another aspect that the current model does not consider is that the density of the road network, in most countries, is not constant over the entire land area. Taking Sweden as an example, the road network in the south is substantially denser than in the north. This would of course mean that the vehicles used in the north would need much larger batteries due to there being less road available to electrify. Hence, it would therefore perhaps make sense to implement dynamic grid-sizing in the model which varies depending on the road network density. But, this would make the model vastly more complex and the end result would possibly not be vastly different compared to now.

Lastly, the additional costs added to enable vehicles to operate in ERS is assumed to simply be the battery plus the pickup arm apparatus. In reality, however, the cost of for example the required control and sensor system might be significant, especially for larger vehicles, and should be incorporated. Furthermore, the costs of the different pickup arm apparatuses for the overhead conductive and inductive ERS technologies were obtained through assumptions. Also here, the real cost might be much higher, particularly for the inductive technology, and could result in greater overall costs. All in all, the limitations with the assumptions discussed in this section were judged to be necessary due to time and data limitation and acceptable for the overall validity of the model.

5.2 Comparing a Pure Battery Electric Car Fleet against an ERS based fleet The results in this thesis for the economic comparison between a pure battery electric car fleet against an ERS in combination with an electric car fleet is scrutinized and analyzed in this section for a global and a Swedish perspective.

5.2.1 Global and Swedish Perspective One of the major arguments used against an employment of ERS for the passenger car fleet is that the battery technology by itself is sufficient enough as the energy source. Consequently, it is argued that there is no need of implementing costly ERS as adequate driving range, power output and acceptable recharge time can be attained from batteries alone. These arguments are also often used by people who think that ERS are only going to be feasible for heavy vehicles. Taking these arguments into consideration, the model developed for this comparison had the intention to see the difference in the costs between the two systems and thus, to identify which future model that would be the most economic.

Looking at the results summarized in Table 6 to Table 9 for the global perspective, it is quite apparent that the ERS option, considering a full-scale implementation, is significantly less expensive than having pure battery based electric passenger cars. Depending on which ERS technology and battery cost scenario that is considered, the number of countries where ERS is found cheaper varies. However, even when considering the most expensive ERS technology (inductive road) and future battery pricing, it can be seen that the ERS solution is less expensive in almost 90 % of the studied countries. Furthermore, the cost of a pure battery electric car fleet is about 17 times higher in the most costly case and 4.5 times in the least costly case depending on the battery scenario and ERS technology regarded.

Similar results were obtained for Sweden, as shown in Table 12 and Table 13. They also display that the ERS solution is roughly between 5 to 16 times less costly than the pure electric car scenario reliant on the battery cost and ERS technology considered. Furthermore, Figure 40 and Figure 41 illustrate the number of electric cars required before an ERS based transportation system becomes cheaper than a pure battery electric one. It is quite apparent that not many electric cars are needed in the transportation system before the ERS option becomes less expensive. Even though, the required number of cars increases with more expensive ERS installation costs and decreasing battery prices. When considering the entire passenger fleet, the number of cars required is still only a fraction of the total amount. For this model, the acceptable range of an electric car battery was 73 assumed to be 500 kilometer. Some might argue that this is way too much and that studies have shown that most users can manage with much less. Nevertheless, even when applying a shorter acceptable range, the cost of a pure battery electric car fleet would still be many times more expensive.

In view of the findings attained by this model, it is safe to conclude that the best alternative out of the two is the ERS in combination with electric passenger cars, at least, if viewed from a purely economic perspective. So is it reasonable to predict that the future passenger car fleet will surely utilize ERS as it is by far the least expensive option? This question, cannot of course be answered with certainty as it depends on a number of unpredictable factors. Looking from an economical perspective, the answer to this question would most undoubtedly be yes. However, as there are numerous cases throughout history where the most technical sound and/or the cheapest solution has not ended up being the winner in a market, there is a possibility that this might also happen to an ERS based passenger car fleet.

Another aspect that could potentially work against an ERS solution is the fact that in order for the ERS to work efficiently, a large number of stakeholders have to be coordinated and organized. This might be an obstacle as the current transportation system is much more flexible and stakeholders such as the automotive manufacturers might not be easily convinced to adapt to the new system. Rationally, it would make more sense for the automotive firms to continue with a system that does not require a huge amount of synchronization with other markets. Manufacturing pure battery electric cars could due to this fact be a simpler and more logical future for the automotive firms.

This speaks in favor of seeing this as it perhaps being down to the governments in each country to facilitate and coordinate the various stakeholders in developing and implementing an ERS which includes passenger cars. Without a governing agency managing and driving the development forward, it will be difficult to implement ERS solutions for passenger cars due to the complexity and size of the system required.

5.2.2 Validity and Limitations of the Model Also this model, as discussed for the previous one, is naturally affected by certain simplifications. Apart from the major limitations discussed for the previous model, which also apply here, there are a couple of specific assumptions for this model that need to be discussed. To start with, one major assumption made was that the cost of an electric car without the battery is the same regardless of the size and weight of the battery that will be used in the vehicle. This, of course, is not the case in reality. For the pure battery electric car case, which requires a battery with a range of up to 500 kilometers, this would result in a battery weighing roughly 800 kilograms. This extra weight would require the structural construction of the electric cars to be more robust and sturdy, thus leading to increased manufacturing costs. Moreover, the additional weight would lead to increased rolling resistance due to friction and would thus require higher effect per kilometer from the batteries to operate. This would very likely result in increased overall costs as well. Consequently, this means that the actual cost of a pure battery electric based car fleet would be higher than the cost that has been computed by the model.

Similarly, when approximating the infrastructure costs for the pure electric car based transportation system, certain educated guesses were made. It was hypothesized that the fast charging stations were the only infrastructure investments that would be required. Hence, other potential infrastructure investments such as battery swapping stations and normal charging stations were ignored. The number of fast chargers needed was assumed to be the same as the gas pumps required today. In reality, it is rational to expect that there will be a need for more fast charging stations compared to the number of gas pumps. This is due to a number of reasons. Firstly, the time needed to charge a battery sufficiently with fast charging technology is roughly 30-40 minutes. This is considerably longer than the time needed for refilling gas in a conventional vehicle and will therefore lead to a requirement for more fast chargers. Similarly, the driving range of electric cars is usually less than the average driving range of a conventional car, this would result in a need for more frequent charging, leading to a requirement for a greater number of fast chargers compared to the gas pumps today.

On the contrary, electric cars have the advantage that they can be charged anywhere with a wall socket. This could mean that many normal users would usually charge their cars at home during downtime, and thus nearly never utilize the fast charging stations. Such a scenario would instead lead to a case where less fast charging stations would be required compared to the current number of gas pumps. Whichever case is the correct one, there is no doubt that further research needs to be carried out in this area. Either way, it was found that the costs caused by infrastructure investment were very small compared to the battery investment costs which stood for almost the entire total cost of a pure battery electric based transportation system. Thus, it can be concluded that computing the correct number of fast chargers required is insignificant for the end results.

Even though there are obviously a couple of major problems and limitations with the model developed. It is concluded, nonetheless, that the comparison of the two transportation system scenarios was performed satisfactorily. The most probable result of improving the model would still be that the ERS and electric car combination would be found to be the most economic option.

5.3 Breakeven point and Expansion In this section, the findings acquired from the models developed to find the investment breakeven point and simulate the different expansion scenarios are discussed and analyzed.

5.3.1 Breakeven Point The findings attained for the investment breakeven point for the three ERS technologies are presented in Figure 42 and Figure 43. The figures show quite clearly, as expected, that the minimum frequency of vehicles per day required for the conductive road technology is substantially less than for the inductive road technology. This simply means that a lot more kilometer of road can be electrified in the conductive case with profit compared to the inductive case. By considering Figure 31, this fact can be understood. The figure illustrates that the traffic intensity as a function of road length can be estimated as being a hyperbole shaped curve. Consequently, this means that the higher the vehicle frequency required, the less of the road network which results in cost savings there is to be electrified. Thus, it can be assumed, that the most logical approach when electrifying the road network would naturally be to start with the most

75 traffic intensive road sections. As they would lead to the largest cost savings which could thus be used to finance ERS installations on less traffic intensive roads.

The findings show quite plainly that not all the roads with ERS will produce cost savings. Even less so, if the cost of the ERS technology is increased. As a consequence, the road sections with high traffic intensity will be required to subsidies the construction and the operation of roads with low traffic intensity. However, this is not a unique discovery. There are more than a few examples in our society where regions with high cost savings facilitate the expansion and operation of said technology in regions with low or no profits. An example of this can be found by viewing the expansion of the mobile network in Sweden.

5.3.2 Expansion Scenarios Viewing the results accumulated for the different scenarios and the three technologies in Figure 44 to Figure 48, they show that Scenario 1, which simulates a fast expansion, is the best alternative from both an environmental and economic standpoint when considering a time period until 2050. Even though the initial investment in both the expansion of ERS and electric vehicles is the highest in this scenario, the largest return can also be yielded until 2050. Looking at the results for each individual technology, starting with the conductive road technology, it can be seen that profit can be made from all three scenarios. This result is primarily due to the low ERS investment cost and the fact that all motor vehicles can utilize the technology. Secondly, for the overhead conductive ERS, the results display that only the first two scenarios lead to cost savings. But for the slow expansion scenario, no profit can be made until year 2050. Here, the relatively low investment costs combined with the vehicle size restriction can be the explanations for the findings.

Finally, for the inductive road ERS technology, all scenarios were found to result in losses for the entirety of the studied time period. The conclusion that can be drawn from this is that the current investment cost for this technology is simply too high. At least, this is the case, when viewing the expansion from an electrified road perspective. In Figure 47, the accumulated results for each ERS technology acquired from Scenario 1 shows quite clearly the differences between the technologies. As expected, the largest cost savings are to be made from an expansion of the conductive road technology. However, even though the cost savings from the overhead conductive technology appear to be vastly smaller. When considering the fact that only heavy vehicles can use this technology, the savings attained are undeniably significant in this case.

To sum up, the major conclusions that can be draw from this model is that a fast expansion and implementation of an ERS based transportation sector is the best tactic. The substantial initial investments are rather quickly refunded so that considerable savings can be made until year 2050.

5.3.3 Validity and Limitations of the Model The expansion model, unsurprisingly, also is affected by numerous simplifications and limitations. Many of the inputs used were assumed to be constant over time, even though that was not always the case. For example, the yearly vehicle kilometer travelled will most probably increase, as the total number of motor vehicles in Sweden will continue to grow. This is also true for the number of new vehicles per year. Making other assumed constant values such as the fuel and electricity prices more dynamic would perhaps increase the accuracy of the model. To summarize, it can be said that the major limitation of the model is possibly that too many inputs were assumed to be 76 constant when in reality they should be more dynamic. However, it was found that creating a more dynamic expansion model would be very complex and time consuming. Additionally, as the goal of the simulations is just to get a notion of which scenario will have the best outcome, it was determined that a more complex model would not necessarily lead to better results. Subsequently, a judgement was made that the current model provides sufficiently accurate results for the expansion simulations.

5.4 Discussing the Electric Road System Technologies Now that the findings from each model have been discussed and analyzed for the three ERS technologies, it is time to compare the three technologies and try to define an answer to the question: Which ERS technology is the best alternative?

The results in all the models indicate that from an economical and environmental perspective, the conductive road technology seems to be the best option. The low investment costs of the conductive ERS coupled with the fact that it can be universally used by all motor vehicles result in a great combination. Even though the inductive technology share these advantages, the steep investment cost is an obstacle. The overhead conductive transfer technology on the other hand, has a fairly low investment cost but does however, restrict which type of motor vehicles that can utilize the system. This factor makes the technology less appealing compared to the conductive road technology, at least, if regarded from an overall economic and environmental perspective. Yet, it is important to note that the overhead conductive technology is specifically developed to be used by heavy vehicles, its goals and market segment focus is thus different from the other two technologies. Consequently, this could very well mean that the overhead road technology could be preferred by the heavy vehicle industry as the economic gains are still substantial and it is possible that the technology will only need to be implemented on certain road sections where the intensity of heavy vehicles is high, and not everywhere as assumed in this study.

There are naturally certain real life aspects that the models cannot capture. To begin with, as discussed previously, the synchronization and cooperation required between the different stakeholders for an ERS to work properly is considerable. Thus, there is no doubt that substantial influence and supervision will be required from the governments to obtain a successful implementation. If not obtained, there is a large probability that the passenger car manufacturers will not opt to choose the ERS solution, but would rather choose the, for them, simpler solution of manufacturing pure battery electric cars.

This option, however, is not possible for heavy vehicles such as trucks and buses due to the limitation with the current battery technology. Hence, it can be predicted from this that the heavy vehicle market will be the driving force in the development and implementation of ERS. As a result, this could lead to the fact that the ERS technology chosen will be the one which is best suited for heavy vehicles, whether it be conductive road, overhead conductive or inductive road technology. Subsequently, even though the computations in the model indicate quite clearly that a universally motor vehicle including technology is the strongest option economically and environmentally, the end result (if not enough governmental engagement is attained) might thus be that a technology which excludes certain vehicle categories might be implemented due to market forces.

Analyzing the technical info compiled for each technology, it is quite apparent that they all have both disadvantages and advantages. The maturity of the overhead technology makes it particularly appealing from an investment point-of-view, as the technology has previously proven to work and thus would not require substantial risks to implement. Yet, there are still new technological developments that even the overhead conductive system has needed to cultivate to obtain a workable ERS. The sceptics of this technology argue that the fact that only a small part of the vehicle fleet can utilize this technology is a major drawback as it puts a restriction on the usability of the ERS. Furthermore, the obvious risks, such as extreme weather, associated with high-hanging cables is another argument used. Even though the companies producing overhead conductive technologies have proven that these risks can be handled, sceptics might still argue over the functionality of exposed electricity cables. Especially when in parallel, the Swedish power companies are working to dig down all the currently exposed electric lines due to safety and practicality concerns. One more argument against the overhead system is that it is often found to be unpleasing from an aesthetic point of view.

Both the conductive and inductive road technologies are comparatively new and have thus far only been demonstrated to work under test-like conditions. This is particularly true for the conductive road technology due to it being a rather novel technology. However, the conductive road has in this study shown to be the least expensive ERS system that caters to all vehicle categories. The major drawback of the system is that the rail trenches might be affected by weather conditions, which sceptics point out as a potential obstacle. Similarly, the ever present risk of electrocution from the rails is another drawback recognized. But these drawbacks have been demonstrated as being surmountable, at least on an experimental level on test-tracks. Taking a closer look at the inductive road technology, it has the advantage of being the most aesthetically pleasing out of the three and also being almost fully immune to weather conditions. The technology does, however, require substantially more in investment costs and road renovations compared to the other ERS technologies. More technical specifications such as global efficiency, pick-up arm technology, system lifespan, etc. were found to be relatively similar and/or based on estimations.

To summarize the arguments against and for the three technologies discussed in this section, Table 14 has been produced which displays the pros and cons from an economic, environmental and aesthetic standpoint. Table 14. The pros and cons of each technology from an economic, environmental and aesthetic perspective.

Conductive Road Overhead Conductive Inductive Road Economic Perspective ++ + -

Environmental Perspective ++ + ++

Aesthetic Perspective + - ++

In conclusion, it can be said that all three technologies have pros and cons, where they all have shown on an experimental level, that the problems associated with the individual technologies can be dealt with. Thus, before a more definitive answer concerning which ERS technology is the best from a technical perspective can be made, more information from the larger demonstration projects currently underway, needs to be acquired. 78

5.5 Implications for Governmental Policies and Strategies Most countries in the world want to reduce their transportation sector dependency on fossil fuels, this is particularly true for the European countries and Sweden. As mentioned earlier, Sweden has set a rather bold goal of achieving a fossil fuel neutral transportation sector until the year 2030. ERS in combination with electric vehicles have been shown, in this report, to be a possible solution to this conundrum. However, as discussed in the previous sections, a full-scale implementation of ERS would be difficult due to path dependencies of our current transportation sector and the complexity of achieving a well-functioning cooperation between the various ERS stakeholders. Thus, it can be concluded that active governmental commitment and guidance might be required in achieving an effective implementation and operation of ERS. It is indicated that perhaps the market forces by themselves will not be enough in overcoming these difficulties to attain an ideal ERS, especially if the passenger cars are to be included. The inconvenience of being dependent on other stakeholders for their cars to function properly might deter passenger car manufacturers from an ERS path if not appropriate incentives and policies are offered by the governments.

Furthermore, the findings point toward that the best expansion scenario might be a rapid expansion approach. This not only requires massive cooperation between the different ERS stakeholders but also require huge initial investments. Even though these in part can be acquired through private investors, it is rather obvious that an extensive governmental commitment will be essential. Additionally, as the electric vehicle growth will also need to increase substantially, the dropping battery prices might not be enough initially to drive this rapid growth and relevant governmental subsidies might be required. These reasoning’s are supported by the findings published by Bloomberg New Energy Finance regarding the governments roll being essential in achieving a widespread adoption of EVs as shown on page 21. It is judged that it might not be a huge leap to assume that this might also apply for a prevalent adoption of ERS in combination with EVs.

Before an implementation on a full-scale can be realized however, it is of course important that a standardization of the chosen ERS technology is made on a preferably global scale but at the very least on a European scale. It is important that the flexibility of being able to travel over borders without any constraints is conserved, since our current society is dependent on this fact to function properly. A standardization will furthermore simplify the collaboration between the stakeholders as each could develop solutions that fit together with the system as a whole instead of being required to develop various different technical solutions. As a result, it is important that the governments use the ongoing demo projects for the different ERS technologies to decide on one technology, which is found to be the best from a technical, economic and environmental perspective, before initiating a full-scale expansion.

Lastly, countries that are the forerunners in the development of the ERS could potentially acquire huge exportation opportunities in the future. Sweden is currently one of the world leaders in the ERS development and thus has a good opportunity to become the leading exporter of ERS related knowledge and technology. This huge economical market prospect is just another incentive out of many that motivate a high governmental engagement in the ERS field.

6 CONCLUSIONS

It is important to note that the conclusions found in this report are based on the specific assumptions and limitations made for the models and thus, other inputs could possibly lead to different findings and conclusions.

It was found that an ERS based transportation system is not economically viable in every country in the world when compared to the current conventional road transportation system. However, a large number of countries in the world were indeed found to have good prospects for ERS from an economical and environmental perspective, where the conductive road was found to be the most attractive ERS technology followed by overhead conductive and inductive road ERS technologies. It is worth mentioning that the current model considers the entire land area of each country when computing the economic viability. Subsequently, even though implementing ERS in certain countries might not be economically sound when considering the whole land area, parts of the countries could still result in cost savings which unfortunately the current model cannot exhibit.

Findings further indicated to the fact that small and/or developed countries are best suited for ERS implementation, thus the most attractive region for ERS was found to be Europe and the least attractive to be Africa. It was found that future developments such as increasing number of vehicles, decreasing battery prices and oil prices will most likely result in more countries eventually becoming suitable for ERS.

For Sweden the findings show that only the conductive road transfer technology results in saving with current battery prices. However, with future battery pricing, all the ERS technologies are found to be viable with conductive road still resulting in the largest profit. Almost a 90 % reduction in emission from the transportation sector could potentially be achieved by implementing conductive road and inductive road ERS technologies. Considerably less reduction is achieved from the overhead conductive technology (23 %) due to the vehicle usage restrictions.

The comparisons between a pure battery electric car fleet and an ERS based electric car fleet show rather clearly that the ERS approach is the cheapest option from both a global and a Swedish perspective. However, governmental influence and incentive is most probably required for the passenger car industry to embrace an ERS approach as the current market is heading towards a pure battery electric car future.

The breakeven model shows quite clearly that not all the roads with ERS will produce cost savings. As a consequence, the road sections with high traffic intensity will be required to subsidies the construction and the operation of roads with low traffic intensity. Hence, logically the expansion of ERS should start with electrifying the most traffic intensive road sections.

The expansion model points towards that a fast expansion and implementation of an ERS based transportation sector is possibly the best approach. The substantial initial investments are rather quickly refunded so that considerable cost savings can be made until year 2050. Also for the expansion scenario, the conductive road ERS technology was found to be the most attractive in this study.

The results acquired in all the models indicate that from an economical and environmental perspective, the conductive road technology is the best option. Furthermore, it can be concluded that the heavy vehicle market will be the driving force in the development and implementation of ERS as the current battery technology is insufficient.

It is reasoned that an active governmental commitment and guidance might be required in achieving an effective implementation and operation of ERS as the market forces by themselves might possibly not be enough. Additionally, a standardization of the chosen ERS technology is important before a full-scale implementation can be initiated.

7 RECOMMENDATIONS FOR FUTURE WORK

The modelling approach developed in this thesis could potentially be further improved by implementing dynamic grid-sizing which would vary depending on the road network density. Additionally, using the inhabited land area instead of total land area for each country might lead to more accurate results.

The reliability and validity of the expansion model could be further improved by converting the assumed constant inputs such as fuel prices and electricity prices into forecasted dynamic inputs. A number of scenarios where the dynamic inputs could be varied depending on different predicted futures could hence be produced.

The current models do not take into consideration that when vehicles are travelling on the electrified roads, the efficiency becomes higher than when using battery drive. As this results in a lower energy consumption per kilometer for the vehicles, this would mean that also the real overall electricity consumption would be lower in a full-scale ERS than computed in this model. Finding a way to implement this improvement could lead to more precise results.

Energy regenerative systems that recover for example the energy from braking can be implemented in certain ERS technologies. This would of course lead to a decrease in the overall energy consumption and potentially also result in bigger savings. It would therefore be interesting to develop a model that included various regenerative systems for the different ERS technologies.

In this thesis, only battery storage is considered as a hybridization alternative for vehicles in combination with ERS. However, other hybrid solutions where the vehicles utilize an ICE or hydrogen fuel cells can also be used in combination with ERS. A comparison between these different alternatives could be a possible area to further explore.

Similarly, it would be interesting to not only compare the ERS technologies between each other but also compare them against other potential alternative future road transportation systems based on for example biofuels, hydrogen fuel cells etc. to identify the best solution.

This thesis demonstrates that there is economical profit to be made by implementing full-scale ERS. However, there needs to be more research concerning how these cost savings will be divided between the different stakeholders and the payment method that will be implemented for the users.

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9 APPENDIX

9.1 Appendix A – Assumptions

9.1.1 Overall Assumptions  The tank-to-wheel efficiency of petroleum-based vehicles was assumed to be between 16- 20 % depending on the vehicle category. However, the tank-to-wheel efficiency of electric vehicles was assumed to be approximately 70 % for all the vehicles categories.

 It was assumed that heavy vehicles accounted for approximately 6.5 % of the global yearly vehicle kilometer.

 The average mileage for all vehicle categories was computed to be approximately 13 000 kilometer. The average distance travelled for only heavy vehicles was found to be roughly 43 360 kilometer.

 In this model, it has been assumed that all light duty trucks, heavy duty trucks and buses only use Swedish MK1 diesel as fuel.

 The energy density of batteries in 2015 was assumed to be 0.1 kWh/kg and in the future as 0.25 kWh/kg.

9.1.2 Model 1 – World  The total number of road motor vehicles was used as indata for each country. This includes automobiles, SUVs, trucks, vans, buses, commercial vehicles and freight motor road vehicles, where motorcycles and other two-wheelers were excluded. However, in this thesis the total number of vehicles was split into four main vehicle types. Namely, passenger cars, light-duty trucks, heavy-duty trucks and buses.

 A payback method was used to compute the payback time of the investments. This method was deemed to be adequate in getting a general idea of how long time it would take until the investment would pay-off.

 The acceptable driving range that an electric car needs to travel to be a viable competitor against conventional vehicle was assumed to be a bit lower than the average driving distance for conventional vehicle, which is roughly 600 kilometers. Therefore, for this model, the minimum acceptable driving range for an electric passenger car was assumed to be 500 kilometers.

 The annual cost of an ERS based car fleet was assumed to be the same as in the previous comparison model.

 To approximate the infrastructure costs that would occur in a completely electric based transportation sector, the cost of installing sufficient number of fast chargers instead of gas pumps was calculated. To do this, firstly, the number of fuel stations in Sweden was found to be 2670 [85]. From this, it was assumed that each of these stations on average hosted 5 89

gas pumps, this resulted in the fact that for each pump there are about 400 cars. Even though in reality there might be a need for more fast chargers than the number of gas pumps today. Due to reasons such as the fact that it takes longer time to refill batteries and their smaller travelling range would lead to a need to charge more frequently. However, for this model it was judged to be sufficient to assume that the same number of fast chargers as gas pumps in Sweden today would be required. This will at least give an indication of the minimum price for the installation of fast chargers during a best case scenario.

 The computed value for the number of cars per fast charger for Sweden was assumed to be the same in all the studied countries.

9.1.3 Model 2 – Sweden  The total road network length used was 214 600 kilometer, and included only asphalted roads.

 The highest value for the traffic intensity was chosen to be 160 000 vehicles/day, which is the value for Sweden’s most heavily used road segment, namely Essingeleden

9.2 Appendix B – Comparison between Petroleum-based Road Transport vs ERS and EV Combination

Table 15. Countries where the yearly savings from an ERS based transportation system are positive using the current battery price ((Conductive Road Case)..

Country Optimal Battery Total % of Cost of grid Car Annual Annual Yearly % Of % Of Total GHG % of Total % of grid- Size Length of Existing per year investment Electricity Fuel Cost Saving Electricity yearly Emission GHG number of Total mesh grid road per year Cost production BNP reduced Emission cars in these Cars in size network reduced countries the World km kWh km MSEK/Year MSEK/Year MSEK/year MSEK/year MSEK/Year % % tons % % World 15.2 3.6 4,324,717 20% 1,272,881 1,675,948 2,134,232 7,549,938 2,466,877 17.3% 2.7% 2,061,746,747 7.3% 995,878,405 78.52%

Monaco 0.6 0.1 7 8.5% 2 15 68 242 157 - 66,002 - 31,881 Singapore 2.2 0.5 623 18.2% 183 527 1,819 6,435 3,906 5.4% 0.3% 1,757,211 2.5% 848,779 Bahrain 2.5 0.6 616 14.9% 181 485 1,608 5,687 3,413 17.7% 1.7% 1,552,911 5.6% 750,097 Malta 2.5 0.6 250 8.1% 74 194 638 2,255 1,350 44.4% 3.1% 615,923 20.5% 297,507 Hong Kong 3.4 0.8 635 30.4% 187 416 1,212 4,288 2,473 4.4% 0.2% 1,171,077 - 565,661 Barbados 4.4 1.0 195 12.2% 58 112 286 1,013 558 40.8% 6.2% 276,730 7.8% 133,668 Netherlands 4.8 1.1 14,214 10.2% 4,184 7,812 19,213 67,968 36,758 28.9% 2.5% 18,560,671 8.5% 8,965,297 Lebanon 4.9 1.1 4,205 60.3% 1,238 2,289 5,570 19,702 10,606 51.6% 7.1% 5,380,364 22.1% 2,598,858 South Korea 5.1 1.2 38,271 36.0% 11,264 20,462 48,705 172,296 91,864 13.4% 2.9% 47,050,768 7.1% 22,726,770 Taiwan 5.1 1.2 12,712 30.7% 3,742 6,791 16,145 57,112 30,435 9.2% 1.6% 15,596,210 5.6% 7,533,383 Guam 5.2 1.2 209 20.0% 62 111 259 917 486 21.4% 6.0% 250,467 - 120,982 Belgium 5.4 1.3 11,313 7.3% 3,330 5,903 13,623 48,193 25,337 22.9% 3.2% 13,160,576 10.1% 6,356,908 Japan 5.4 1.3 134,172 11.0% 39,490 69,553 159,183 563,118 294,891 21.4% 4.0% 153,776,941 12.2% 74,278,344 Liechtenstein 5.6 1.3 58 15.2% 17 30 67 237 123 - 2.5% 64,599 - 31,203 Luxembourg 6.0 1.4 857 29.6% 252 425 915 3,237 1,645 59.1% 2.0% 884,001 7.3% 426,996 Israel 6.5 1.5 6,297 33.9% 1,853 3,040 6,285 22,235 11,056 14.3% 3.0% 6,071,927 7.0% 2,932,902 U.K. 6.6 1.5 73,730 18.7% 21,701 35,378 72,420 256,189 126,690 29.0% 3.6% 69,960,303 250.8% 33,792,683 Italy 6.6 1.5 89,313 18.3% 26,287 42,794 87,403 309,193 152,709 43.3% 5.4% 84,434,873 17.0% 40,784,285 Germany 6.6 1.6 104,876 16.3% 30,868 50,069 101,670 359,661 177,054 23.7% 3.6% 98,216,595 10.9% 47,441,222 Switzerland 7.1 1.7 11,300 15.8% 3,326 5,269 10,290 36,401 17,516 20.0% 3.0% 9,940,324 18.4% 4,801,440 Kuwait 7.1 1.7 5,002 75.7% 1,472 2,327 4,526 16,010 7,685 10.3% 2.2% 4,371,946 2.3% 2,111,766 Mauritius 7.4 1.7 549 25.6% 162 252 479 1,695 802 26.1% 2.9% 462,822 7.8% 223,555 Qatar 7.6 1.8 3,065 31.2% 902 1,395 2,612 9,242 4,332 9.7% 1.2% 2,523,687 3.4% 1,219,008 Trinidad and Tobago 8.0 1.9 1,282 15.4% 377 572 1,033 3,653 1,671 18.7% 3.6% 997,534 2.2% 481,835 Nauru 8.8 2.1 5 16.0% 1 2 3 12 5 16.7% 3.9% 3,378 3.8% 1,632 Poland 9.0 2.1 67,294 15.9% 19,807 28,866 47,970 169,697 73,054 41.7% 7.5% 46,341,208 12.3% 22,384,033 France 9.4 2.2 116,915 11.4% 34,411 49,539 80,104 283,371 119,316 20.6% 5.4% 77,383,281 15.1% 37,378,179 Cyprus 9.4 2.2 1,961 9.8% 577 831 1,341 4,745 1,996 41.9% 8.6% 1,295,892 14.6% 625,950 Czech Republic 9.5 2.2 16,210 12.4% 4,771 6,842 10,964 38,784 16,208 18.0% 6.0% 10,591,153 7.8% 5,115,808 San Marino 9.6 2.2 13 4.4% 4 5 9 31 13 - 0.8% 8,354 - 4,035 Denmark 9.7 2.3 8,779 11.9% 2,584 3,690 5,854 20,708 8,581 24.2% 4.1% 5,655,093 9.6% 2,731,560 91

Portugal 9.9 2.3 18,532 22.4% 5,455 7,740 12,102 42,810 17,513 32.8% 7.8% 11,690,517 15.9% 5,646,830 Austria 9.9 2.3 16,578 13.3% 4,879 6,908 10,744 38,006 15,475 22.7% 5.0% 10,378,799 12.2% 5,013,235 Spain 10.5 2.5 95,201 13.9% 28,020 39,076 58,541 207,090 81,453 29.3% 7.0% 56,552,400 16.1% 27,316,310 Slovenia 10.7 2.5 3,768 8.6% 1,109 1,538 2,271 8,032 3,115 22.0% 7.1% 2,193,489 11.2% 1,059,513 Saint Lucia 10.9 2.6 112 9.2% 33 45 66 235 90 31.2% 6.5% 64,054 5.8% 30,940 Jamaica 11.1 2.6 1,948 8.8% 573 787 1,129 3,996 1,506 34.0% 9.4% 1,091,104 9.7% 527,032 Antigua and Barbuda 11.2 2.6 79 6.8% 23 32 46 162 61 62.2% 4.5% 44,159 4.2% 21,330 Saint Kitts and Nevis 11.2 2.6 47 12.2% 14 19 27 95 36 30.7% 4.1% 25,938 7.2% 12,529 Greece 11.6 2.7 22,570 19.3% 6,643 9,016 12,566 44,454 16,229 30.8% 9.4% 12,139,601 11.1% 5,863,749 Brunei 12.0 2.8 875 28.9% 258 346 469 1,658 586 19.1% 3.1% 452,823 2.2% 218,726 Slovakia 12.1 2.8 7,951 20.4% 2,340 3,140 4,235 14,983 5,267 21.2% 5.8% 4,091,510 9.4% 1,976,308 Grenada 12.6 3.0 55 4.9% 16 21 28 99 34 26.7% 4.6% 27,108 1.4% 13,094 Hungary 12.6 3.0 14,209 7.1% 4,182 5,553 7,261 25,688 8,691 34.2% 6.4% 7,014,842 10.4% 3,388,355 Seychelles 12.7 3.0 72 14.1% 21 28 37 129 44 - 3.3% 35,353 4.6% 17,077 Ireland 13.1 3.1 10,524 10.9% 3,098 4,076 5,183 18,333 5,977 29.2% 4.9% 5,006,512 8.2% 2,418,278 Maldives 13.2 3.1 45 51.5% 13 18 22 79 25 18.8% 1.1% 21,437 1.8% 10,355 United Arab Emirates 13.2 3.1 12,698 311.2% 3,737 4,911 6,216 21,990 7,125 8.0% 2.3% 6,004,974 0.1% 2,900,562 Malaysia 13.3 3.1 49,255 34.1% 14,497 18,990 23,791 84,161 26,883 25.8% 7.0% 22,982,854 8.1% 11,101,328 Croatia 14.5 3.4 7,730 26.3% 2,275 2,925 3,441 12,172 3,531 48.4% 9.8% 3,323,835 12.1% 1,605,500 U.S. 14.5 3.4 1,262,903 19.2% 371,707 477,833 561,938 1,987,878 576,401 18.7% 8.0% 542,852,359 142.5% 262,212,099 Dominican Republic 14.6 3.4 6,618 33.6% 1,948 2,499 2,921 10,333 2,965 31.9% 5.1% 2,821,838 9.0% 1,363,022 El Salvador 14.7 3.5 2,821 25.9% 830 1,064 1,238 4,380 1,247 29.5% 6.1% 1,196,132 9.4% 577,763 Thailand 14.8 3.5 69,057 38.4% 20,325 26,007 30,085 106,426 30,009 26.1% 7.1% 29,063,002 8.4% 14,038,201 Serbia 14.9 3.5 10,395 23.5% 3,059 3,908 4,495 15,901 4,438 17.9% 12.2% 4,342,254 7.2% 2,097,424 Romania 15.1 3.5 30,494 36.2% 8,975 11,437 13,036 46,117 12,668 31.4% 8.4% 12,593,590 9.8% 6,083,038 Turkey 15.2 3.6 101,533 23.8% 29,884 38,037 43,171 152,718 41,626 25.8% 7.3% 41,704,503 10.9% 20,144,382 Tonga 15.2 3.6 94 13.8% 28 35 40 141 38 105.5% 20.5% 38,514 10.1% 18,603 Bulgaria 15.3 3.6 14,185 35.3% 4,175 5,304 5,978 21,147 5,690 19.4% 12.1% 5,774,908 9.4% 2,789,434 Lithuania 15.4 3.6 8,156 9.7% 2,401 3,046 3,420 12,100 3,232 102.2% 11.3% 3,304,201 15.3% 1,596,017 Sri Lanka 15.7 3.7 8,244 7.2% 2,427 3,067 3,390 11,991 3,108 40.4% 4.0% 3,274,400 7.9% 1,581,622 Kiribati 17.1 4.0 95 14.2% 28 35 36 127 28 568.2% 62.7% 34,580 43.2% 16,703 Moldova 17.7 4.2 3,723 39.8% 1,096 1,352 1,358 4,805 999 35.6% 23.4% 1,312,157 10.4% 633,806 China 17.8 4.2 1,047,508 23.5% 308,310 379,923 379,188 1,341,395 273,974 9.3% 6.0% 366,309,862 3.8% 176,937,386 Mexico 18.2 4.3 213,832 56.4% 62,937 77,254 75,808 268,175 52,177 36.9% 10.7% 73,233,659 10.7% 35,373,801 Ukraine 18.8 4.4 61,497 36.3% 18,100 22,074 21,039 74,427 13,214 15.5% 20.4% 20,324,684 3.5% 9,817,362 Belarus 18.9 4.4 21,519 22.7% 6,334 7,723 7,356 26,021 4,609 33.7% 14.2% 7,105,843 7.3% 3,432,311 Costa Rica 18.9 4.4 5,403 13.8% 1,590 1,938 1,842 6,518 1,147 26.6% 8.1% 1,779,876 11.7% 859,728 Bosnia and Herzegovina 19.5 4.6 5,262 23.0% 1,549 1,878 1,744 6,168 998 17.1% 14.8% 1,684,497 6.2% 813,657 Dominica 19.5 4.6 77 5.1% 23 28 26 90 15 45.5% 10.4% 24,640 10.7% 11,902 Estonia 20.2 4.8 4,191 7.2% 1,233 1,486 1,335 4,724 670 16.4% 12.2% 1,290,061 6.0% 623,134 Jordan 20.4 4.8 8,688 120.6% 2,557 3,075 2,740 9,692 1,320 28.4% 11.5% 2,646,619 10.3% 1,278,387 Montenegro 20.5 4.8 1,315 16.9% 387 465 415 1,467 199 23.2% 14.7% 400,527 11.4% 193,465 Azerbaijan 21.2 5.0 7,793 14.7% 2,294 2,741 2,369 8,379 976 14.6% 4.9% 2,288,210 3.9% 1,105,266 Albania 21.4 5.0 2,561 14.2% 754 900 772 2,730 305 28.4% 8.7% 745,422 10.5% 360,059 Sweden 22.0 5.2 37,387 6.5% 11,004 13,077 10,979 38,838 3,778 9.8% 8.8% 10,605,966 17.1% 5,122,963 New Zealand 22.3 5.3 23,987 25.5% 7,060 8,368 6,927 24,504 2,149 22.9% 15.8% 6,691,456 9.4% 3,232,151 Comoros 22.5 5.3 199 22.6% 59 69 57 202 17 407.7% 17.9% 55,142 19.7% 26,635 92

Armenia 23.6 5.5 2,394 30.7% 705 828 655 2,317 129 13.2% 10.3% 632,741 8.9% 305,631 Guatemala 23.8 5.6 8,988 78.2% 2,645 3,104 2,430 8,595 416 41.5% 7.8% 2,347,202 6.8% 1,133,761 Iran 24.0 5.6 127,685 59.7% 37,581 44,060 34,308 121,364 5,415 18.6% 10.0% 33,142,338 4.7% 16,008,629 Philippines 24.2 5.7 24,661 11.3% 7,258 8,500 6,574 23,255 923 12.4% 3.6% 6,350,591 4.3% 3,067,504 Norway 24.2 5.7 25,108 26.7% 7,390 8,651 6,677 23,621 903 7.1% 7.7% 6,450,426 12.6% 3,115,727 Latvia 24.5 5.8 5,082 7.1% 1,496 1,748 1,337 4,730 149 32.4% 11.0% 1,291,610 10.0% 623,882 Finland 24.5 5.8 24,797 5.5% 7,298 8,530 6,523 23,075 723 13.1% 12.0% 6,301,250 8.1% 3,043,671 Indonesia 24.6 5.8 147,168 29.6% 43,316 50,593 38,533 136,310 3,870 25.5% 5.6% 37,223,826 4.6% 17,980,096 Tunisia 25.6 6.0 12,120 62.4% 3,567 4,143 3,047 10,780 23 28.6% 10.3% 2,943,736 8.4% 1,421,903

Table 16. Countries where the yearly savings from an ERS based transportation system are positive using the future battery cost (Conductive Road Case).

Country Optimal Battery Total % of Cost of grid Car Annual Annual Yearly % Of % Of Total GHG % of Total number % of grid- Size Length of Existing per year investment Electricity Fuel Cost Saving Electricity yearly Emission GHG of cars in Total mesh grid road per year Cost production BNP reduced Emission these Cars in size network reduced countries the World km kWh km MSEK/Year MSEK/Year MSEK/year MSEK/year MSEK/Year % % tons % % World 33.0 7.8 3,553,536 11% 1,045,902 1,511,625 2,465,996 8,723,566 3,700,045 16.7% 1.7% 2,382,242,687 6.7% 1,150,686,451 90.73%

Monaco 1.0 0.2 4 5.1% 1 14 68 242 158 - 0.4% 66,002 - 31,881 Singapore 3.7 0.9 372 10.9% 110 453 1819 6435 4053 5.4% 0.2% 1,757,211 2.5% 848,779 Bahrain 4.1 1.0 368 8.9% 108 412 1608 5687 3559 17.7% 1.4% 1,552,911 5.6% 750,097 Malta 4.2 1.0 149 4.8% 44 164 638 2255 1410 44.4% 2.4% 615,923 20.5% 297,507 Hong Kong 5.7 1.3 380 18.2% 112 341 1212 4288 2624 4.4% 0.2% 1,171,077 - 565,661 Barbados 7.4 1.7 117 7.3% 34 88 286 1013 604 40.8% 4.5% 276,730 7.8% 133,668 Netherlands 8.0 1.9 8,495 6.1% 2500 6129 19213 67968 40125 28.9% 1.8% 18,560,671 8.5% 8,965,297 Lebanon 8.1 1.9 2,513 36.1% 740 1791 5570 19702 11602 51.6% 5.1% 5,380,364 22.1% 2,598,858 South Korea 8.5 2.0 22,871 21.5% 6732 15930 48705 172296 100929 13.4% 2.1% 47,050,768 7.1% 22,726,770 Taiwan 8.5 2.0 7,597 18.3% 2236 5285 16145 57112 33446 9.2% 1.1% 15,596,210 5.6% 7,533,383 Guam 8.7 2.0 125 12.0% 37 86 259 917 535 21.4% 4.3% 250,467 - 120,982 Belgium 9.0 2.1 6,761 4.4% 1990 4563 13623 48193 28017 22.9% 2.3% 13,160,576 10.1% 6,356,908 Japan 9.1 2.1 80,183 6.6% 23600 53663 159183 563118 326671 21.4% 2.8% 153,776,941 12.2% 74,278,344 Liechtenstein 9.3 2.2 34 9.1% 10 23 67 237 137 - 1.7% 64,599 - 31,203 Luxembourg 10.1 2.4 512 17.7% 151 324 915 3237 1848 59.1% 1.4% 884,001 7.3% 426,996 Israel 10.8 2.5 3,763 20.3% 1108 2295 6285 22235 12547 14.3% 2.1% 6,071,927 7.0% 2,932,902 U.K. 11.0 2.6 44,062 11.2% 12969 26646 72420 256189 144154 29.0% 2.5% 69,960,303 250.8% 33,792,683 Italy 11.0 2.6 53,375 10.9% 15710 32216 87403 309193 173864 43.3% 3.7% 84,434,873 17.0% 40,784,285 Germany 11.1 2.6 62,675 9.7% 18447 37648 101670 359661 201896 23.7% 2.5% 98,216,595 10.9% 47,441,222 Switzerland 11.8 2.8 6,753 9.5% 1988 3931 10290 36401 20192 20.0% 2.1% 9,940,324 18.4% 4,801,440 Kuwait 11.9 2.8 2,989 45.2% 880 1735 4526 16010 8870 10.3% 1.5% 4,371,946 2.3% 2,111,766 Mauritius 12.4 2.9 328 15.3% 97 187 479 1695 932 26.1% 2.0% 462,822 7.8% 223,555 Qatar 12.7 3.0 1,831 18.6% 539 1032 2612 9242 5058 9.7% 0.8% 2,523,687 3.4% 1,219,008 Trinidad and Tobago 13.4 3.2 766 9.2% 225 420 1033 3653 1974 18.7% 2.5% 997,534 2.2% 481,835 Nauru 14.8 3.5 3 9.6% 1 2 3 12 7 16.7% 2.6% 3,378 3.8% 1,632 Poland 15.1 3.6 40,216 9.5% 11837 20896 47970 169697 88994 41.7% 5.0% 46,341,208 12.3% 22,384,033 France 15.7 3.7 69,870 6.8% 20565 35693 80104 283371 147010 20.6% 3.6% 77,383,281 15.1% 37,378,179 Cyprus 15.8 3.7 1,172 5.9% 345 598 1341 4745 2461 41.9% 5.7% 1,295,892 14.6% 625,950 Czech Republic 15.9 3.8 9,687 7.4% 2851 4922 10964 38784 20047 18.0% 4.0% 10,591,153 7.8% 5,115,808 San Marino 16.0 3.8 8 2.6% 2 4 9 31 16 - 0.5% 8,354 - 4,035 Denmark 16.2 3.8 5,247 7.1% 1544 2650 5854 20708 10661 24.2% 2.8% 5,655,093 9.6% 2,731,560 Portugal 16.5 3.9 11,075 13.4% 3260 5545 12102 42810 21903 32.8% 5.2% 11,690,517 15.9% 5,646,830 Austria 16.6 3.9 9,907 8.0% 2916 4945 10744 38006 19402 22.7% 3.3% 10,378,799 12.2% 5,013,235 Spain 17.5 4.1 56,894 8.3% 16745 27801 58541 207090 104003 29.3% 4.6% 56,552,400 16.1% 27,316,310 Slovenia 17.9 4.2 2,252 5.1% 663 1092 2271 8032 4007 22.0% 4.7% 2,193,489 11.2% 1,059,513 Saint Lucia 18.2 4.3 67 5.5% 20 32 66 235 116 31.2% 4.3% 64,054 5.8% 30,940 Jamaica 18.6 4.4 1,164 5.3% 343 556 1129 3996 1967 34.0% 6.2% 1,091,104 9.7% 527,032 94

Antigua and Barbuda 18.7 4.4 47 4.0% 14 23 46 162 79 62.2% 3.0% 44,159 4.2% 21,330 Saint Kitts and Nevis 18.7 4.4 28 7.3% 8 13 27 95 47 30.7% 2.7% 25,938 7.2% 12,529 Greece 19.4 4.6 13,488 11.5% 3970 6343 12566 44454 21575 30.8% 6.2% 12,139,601 11.1% 5,863,749 Brunei 20.1 4.7 523 17.3% 154 242 469 1658 793 19.1% 2.0% 452,823 2.2% 218,726 Slovakia 20.2 4.8 4,752 12.2% 1399 2198 4235 14983 7151 21.2% 3.8% 4,091,510 9.4% 1,976,308 Grenada 21.0 4.9 33 2.9% 10 15 28 99 47 26.7% 3.0% 27,108 1.4% 13,094 Hungary 21.1 5.0 8,491 4.3% 2499 3871 7261 25688 12056 34.2% 4.2% 7,014,842 10.4% 3,388,355 Seychelles 21.2 5.0 43 8.5% 13 20 37 129 61 - 2.2% 35,353 4.6% 17,077 Ireland 21.9 5.2 6,290 6.5% 1851 2830 5183 18333 8470 29.2% 3.2% 5,006,512 8.2% 2,418,278 Maldives 22.0 5.2 27 30.8% 8 12 22 79 36 18.8% 0.7% 21,437 1.8% 10,355 United Arab Emirates 22.0 5.2 7,588 186.0% 2233 3407 6216 21990 10133 8.0% 1.5% 6,004,974 0.1% 2,900,562 Malaysia 22.3 5.3 29,435 20.4% 8664 13157 23791 84161 38550 25.8% 4.6% 22,982,854 8.1% 11,101,328 Croatia 24.2 5.7 4,620 15.7% 1360 2009 3441 12172 5362 48.4% 6.3% 3,323,835 12.1% 1,605,500 U.S. 24.2 5.7 754,729 11.5% 222137 328264 561938 1987878 875540 18.7% 5.2% 542,852,359 142.5% 262,212,099 Dominican Republic 24.4 5.7 3,955 20.1% 1164 1716 2921 10333 4533 31.9% 3.3% 2,821,838 9.0% 1,363,022 El Salvador 24.6 5.8 1,686 15.5% 496 730 1238 4380 1916 29.5% 3.9% 1,196,132 9.4% 577,763 Thailand 24.8 5.8 41,270 22.9% 12147 17829 30085 106426 46366 26.1% 4.6% 29,063,002 8.4% 14,038,201 Serbia 24.9 5.9 6,212 14.0% 1828 2677 4495 15901 6900 17.9% 7.9% 4,342,254 7.2% 2,097,424 Romania 25.2 5.9 18,224 21.6% 5364 7826 13036 46117 19891 31.4% 5.5% 12,593,590 9.8% 6,083,038 Turkey 25.4 6.0 60,678 14.2% 17859 26012 43171 152718 65676 25.8% 4.7% 41,704,503 10.9% 20,144,382 Tonga 25.5 6.0 56 8.3% 17 24 40 141 61 105.5% 13.2% 38,514 10.1% 18,603 Bulgaria 25.6 6.0 8,477 21.1% 2495 3624 5978 21147 9050 19.4% 7.8% 5,774,908 9.4% 2,789,434 Lithuania 25.7 6.1 4,874 5.8% 1435 2081 3420 12100 5164 102.2% 7.3% 3,304,201 15.3% 1,596,017 Sri Lanka 26.2 6.2 4,927 4.3% 1450 2090 3390 11991 5061 40.4% 2.6% 3,274,400 7.9% 1,581,622 Kiribati 28.6 6.7 57 8.5% 17 23 36 127 51 568.2% 40.2% 34,580 43.2% 16,703 Moldova 29.6 7.0 2,225 23.8% 655 911 1358 4805 1880 35.6% 15.0% 1,312,157 10.4% 633,806 China 29.8 7.0 626,006 14.0% 184250 255863 379188 1341395 522093 9.3% 3.8% 366,309,862 3.8% 176,937,386 Mexico 30.4 7.2 127,789 33.7% 37612 51929 75808 268175 102826 36.9% 6.9% 73,233,659 10.7% 35,373,801 Ukraine 31.5 7.4 36,751 21.7% 10817 14790 21039 74427 27781 15.5% 13.0% 20,324,684 3.5% 9,817,362 Belarus 31.6 7.4 12,860 13.6% 3785 5174 7356 26021 9706 33.7% 9.0% 7,105,843 7.3% 3,432,311 Costa Rica 31.6 7.4 3,229 8.3% 950 1298 1842 6518 2427 26.6% 5.2% 1,779,876 11.7% 859,728 Bosnia and Herzegovina 32.6 7.7 3,145 13.7% 926 1255 1744 6168 2244 17.1% 9.4% 1,684,497 6.2% 813,657 Dominica 32.6 7.7 46 3.0% 14 18 26 90 33 45.5% 6.6% 24,640 10.7% 11,902 Estonia 33.8 8.0 2,505 4.3% 737 989 1335 4724 1662 16.4% 7.7% 1,290,061 6.0% 623,134 Jordan 34.2 8.0 5,192 72.1% 1528 2046 2740 9692 3378 28.4% 7.3% 2,646,619 10.3% 1,278,387 Montenegro 34.2 8.1 786 10.1% 231 310 415 1467 511 23.2% 9.4% 400,527 11.4% 193,465 Azerbaijan 35.5 8.3 4,657 8.8% 1371 1818 2369 8379 2822 14.6% 3.1% 2,288,210 3.9% 1,105,266 Albania 35.8 8.4 1,531 8.5% 450 596 772 2730 911 28.4% 5.5% 745,422 10.5% 360,059 Sweden 36.7 8.6 22,343 3.9% 6576 8650 10979 38838 12634 9.8% 5.5% 10,605,966 17.1% 5,122,963 New Zealand 37.4 8.8 14,335 15.2% 4219 5527 6927 24504 7830 22.9% 10.0% 6,691,456 9.4% 3,232,151 Comoros 37.6 8.8 119 13.5% 35 46 57 202 64 407.7% 11.3% 55,142 19.7% 26,635 Armenia 39.4 9.3 1,431 18.4% 421 545 655 2317 696 13.2% 6.5% 632,741 8.9% 305,631 Guatemala 39.9 9.4 5,371 46.7% 1581 2040 2430 8595 2545 41.5% 4.9% 2,347,202 6.8% 1,133,761 Iran 40.1 9.4 76,306 35.7% 22459 28938 34308 121364 35660 18.6% 6.3% 33,142,338 4.7% 16,008,629 Philippines 40.5 9.5 14,738 6.8% 4338 5579 6574 23255 6764 12.4% 2.3% 6,350,591 4.3% 3,067,504 Norway 40.6 9.5 15,005 16.0% 4416 5677 6677 23621 6850 7.1% 4.9% 6,450,426 12.6% 3,115,727 Latvia 41.0 9.6 3,037 4.3% 894 1146 1337 4730 1353 32.4% 6.9% 1,291,610 10.0% 623,882 95

Finland 41.0 9.6 14,819 3.3% 4362 5593 6523 23075 6597 13.1% 7.5% 6,301,250 8.1% 3,043,671 Indonesia 41.2 9.7 87,950 17.7% 25886 33163 38533 136310 38729 25.5% 3.5% 37,223,826 4.6% 17,980,096 Tunisia 42.9 10.1 7,243 37.3% 2132 2707 3047 10780 2893 28.6% 6.5% 2,943,736 8.4% 1,421,903 Georgia 43.6 10.3 3,195 15.6% 940 1190 1322 4677 1224 18.9% 10.2% 1,277,085 9.8% 616,866 Fiji 43.8 10.3 835 24.3% 246 311 344 1218 317 58.8% 12.1% 332,609 15.2% 160,659 India 45.8 10.8 129,856 2.7% 38220 47886 51182 181057 43769 6.1% 1.8% 49,443,304 2.0% 23,882,428 South Africa 47.5 11.2 51,150 6.8% 15055 18726 19441 68773 15551 10.8% 7.9% 18,780,582 4.1% 9,071,520 Syria 47.8 11.2 7,687 12.8% 2263 2811 2904 10274 2296 10.7% 15.4% 2,805,524 2.9% 1,355,142 Panama 48.8 11.5 3,049 20.1% 898 1111 1129 3993 856 21.1% 4.2% 1,090,486 7.2% 526,734 Viet Nam 49.0 11.5 12,647 6.1% 3722 4602 4655 16468 3489 4.2% 2.6% 4,497,096 1.7% 2,172,217 Honduras 49.2 11.6 4,547 30.8% 1338 1653 1667 5898 1240 35.5% 12.4% 1,610,683 8.2% 778,003 Colombia 49.3 11.6 42,145 29.8% 12404 15319 15432 54591 11436 35.4% 7.1% 14,907,773 8.7% 7,200,850 Swaziland 50.0 11.8 689 19.2% 203 250 249 880 179 77.2% 7.1% 240,280 8.6% 116,062 Nigeria 51.4 12.1 35,408 18.3% 10422 12768 12423 43945 8333 69.1% 3.6% 12,000,613 3.7% 5,796,614 Brazil 51.9 12.2 321,831 18.4% 94724 115844 111830 395604 73207 27.4% 11.3% 108,032,183 9.8% 52,182,412 Chile 54.8 12.9 27,141 34.9% 7988 9676 8937 31616 5014 17.6% 7.1% 8,633,740 9.2% 4,170,326 Micronesia 55.2 13.0 25 10.6% 7 9 8 29 5 6.2% 9.1% 8,040 - 3,884 Morocco 55.5 13.1 16,072 27.5% 4730 5717 5223 18477 2806 31.5% 6.5% 5,045,674 5.5% 2,437,195 Samoa 56.3 13.3 100 4.3% 29 36 32 114 16 39.9% 10.7% 31,009 7.8% 14,978 Venezuela 56.6 13.3 31,153 32.4% 9169 11044 9929 35126 4983 10.8% 7.0% 9,592,168 3.4% 4,633,272 Argentina 57.9 13.6 94,593 40.9% 27841 33414 29506 104378 13617 30.4% 10.8% 28,503,703 7.8% 13,768,045 Saudi Arabia 57.9 13.6 74,270 33.5% 21860 26233 23156 81915 10667 11.3% 4.9% 22,369,535 4.4% 10,805,079 Haiti 59.7 14.1 923 21.6% 272 324 279 987 112 61.1% 5.3% 269,505 3.5% 130,178 Zimbabwe 59.8 14.1 12,931 13.3% 3806 4543 3901 13799 1550 76.4% 50.8% 3,768,357 18.6% 1,820,216 Pakistan 61.2 14.4 25,207 9.6% 7419 8824 7438 26313 2632 11.4% 3.0% 7,185,686 2.4% 3,470,877 Iraq 62.6 14.7 13,964 23.8% 4110 4870 4023 14233 1230 11.2% 2.9% 3,886,710 1.7% 1,877,384 Egypt 63.2 14.9 31,518 22.9% 9277 10977 9006 31858 2598 8.7% 3.5% 8,699,855 3.1% 4,202,261 Ecuador 63.3 14.9 8,746 20.7% 2574 3045 2493 8820 708 15.3% 5.3% 2,408,495 4.6% 1,163,367 Cuba 65.4 15.4 3,360 6.4% 989 1164 928 3282 201 7.9% 2.8% 896,286 1.6% 432,930 Uruguay 65.4 15.4 5,351 16.7% 1575 1854 1476 5222 317 20.8% 8.0% 1,426,038 4.3% 688,814 Bangladesh 67.0 15.8 3,887 11.3% 1144 1342 1047 3705 172 3.0% 0.7% 1,011,813 0.8% 488,733 Ghana 67.5 15.9 6,741 6.2% 1984 2324 1802 6376 265 23.6% 6.5% 1,741,108 6.2% 841,001

9.3 Appendix C – Comparison between a Pure Battery Electric Car fleet vs ERS and Electric Car Combination

Table 17. A list with countries where the ERS plus battery combination is cheaper than a pure battery based passenger car fleet.

Country Optimal grid- Battery Size Total Length of Total Investment Total ERS cost per Fullsize EVs + Fullsize EVs + ERS cost Total number of % of Total mesh size grid Grid+Battery+ year Fastchargers Fastchargers per compared to cars in these Cars in the Pickup arm year full EV cost countries World in % km kWh km MSEK MSEK/year MSEK MSEK/Year % % World 17.8 2.8 13,968,123 28,498,080 2,096,939 506,110,987 37,240,532 5.6% 1,117,917,387 99.99%

Monaco 0.4 0.1 11 173 13 12,723 936 1.4% 28,103 Singapore 1.3 0.2 1,078 5,840 430 338,730 24,924 1.7% 748,199 Bahrain 1.4 0.2 1,066 5,342 393 299,348 22,027 1.8% 661,211 Malta 1.5 0.2 433 2,135 157 118,729 8,736 1.8% 262,252 Hong Kong 2.0 0.3 1,100 4,502 331 225,743 16,611 2.0% 498,630 Barbados 2.5 0.4 338 1,190 88 53,344 3,925 2.2% 117,828 Netherlands 2.8 0.4 24,608 82,843 6,096 3,577,858 263,265 2.3% 7,902,909 Lebanon 2.8 0.4 7,279 24,247 1,784 1,037,149 76,315 2.3% 2,290,894 South Korea 2.9 0.5 66,254 216,202 15,908 9,069,766 667,369 2.4% 20,033,648 Taiwan 2.9 0.5 22,007 71,739 5,279 3,006,412 221,217 2.4% 6,640,677 Guam 3.0 0.5 362 1,166 86 48,281 3,553 2.4% 106,646 Belgium 3.1 0.5 19,585 62,159 4,574 2,536,905 186,670 2.5% 5,603,614 Japan 3.1 0.5 232,279 731,798 53,847 29,642,893 2,181,176 2.5% 65,476,361 Liechtenstein 3.2 0.5 100 311 23 12,452 916 2.5% 27,505 Luxembourg 3.5 0.6 1,483 4,444 327 170,405 12,539 2.6% 376,397 Israel 3.7 0.6 10,901 31,662 2,330 1,170,458 86,124 2.7% 2,585,353 U.K. 3.8 0.6 127,642 368,078 27,084 13,485,935 992,319 2.7% 29,788,250 Italy 3.8 0.6 154,619 445,140 32,754 16,276,133 1,197,626 2.7% 35,951,347 Germany 3.8 0.6 181,562 520,527 38,301 18,932,774 1,393,107 2.7% 41,819,438 Switzerland 4.1 0.7 19,563 54,582 4,016 1,916,152 140,994 2.8% 4,232,470 Kuwait 4.1 0.7 8,659 24,094 1,773 842,760 62,012 2.9% 1,861,522 Mauritius 4.3 0.7 951 2,606 192 89,216 6,565 2.9% 197,064 Qatar 4.4 0.7 5,305 14,399 1,060 486,480 35,796 3.0% 1,074,555 Trinidad and Tobago 4.6 0.7 2,219 5,887 433 192,290 14,149 3.1% 424,738 Nauru 5.1 0.8 8 21 2 651 48 3.3% 1,438 Poland 5.2 0.8 116,500 294,933 21,702 8,932,987 657,305 3.3% 19,731,525 France 5.4 0.9 202,403 505,080 37,165 14,916,829 1,097,606 3.4% 32,948,864 Cyprus 5.4 0.9 3,395 8,467 623 249,803 18,381 3.4% 551,775 Czech Republic 5.5 0.9 28,063 69,706 5,129 2,041,609 150,225 3.4% 4,509,585 San Marino 5.5 0.9 22 55 4 1,610 118 3.4% 3,557 Denmark 5.6 0.9 15,199 37,562 2,764 1,090,107 80,212 3.4% 2,407,870 97

Portugal 5.7 0.9 32,083 78,713 5,792 2,253,529 165,819 3.5% 4,977,680 Austria 5.7 0.9 28,700 70,227 5,167 2,000,675 147,213 3.5% 4,419,167 Spain 6.1 1.0 164,813 396,149 29,149 10,901,353 802,141 3.6% 24,079,327 Slovenia 6.2 1.0 6,523 15,574 1,146 422,829 31,113 3.7% 933,961 Saint Lucia 6.3 1.0 193 459 34 12,347 909 3.7% 27,273 Jamaica 6.4 1.0 3,373 7,952 585 210,327 15,476 3.8% 464,579 Antigua and Barbuda 6.5 1.0 137 323 24 8,512 626 3.8% 18,802 Saint Kitts and Nevis 6.5 1.0 81 190 14 5,000 368 3.8% 11,044 Greece 6.7 1.1 39,073 90,948 6,692 2,340,097 172,188 3.9% 5,168,895 Brunei 7.0 1.1 1,515 3,484 256 87,289 6,423 4.0% 192,807 Slovakia 7.0 1.1 13,765 31,606 2,326 788,702 58,034 4.0% 1,742,116 Grenada 7.3 1.2 95 215 16 5,225 385 4.1% 11,542 Hungary 7.3 1.2 24,598 55,786 4,105 1,352,220 99,499 4.1% 2,986,835 Seychelles 7.3 1.2 124 282 21 6,815 501 4.1% 15,053 Ireland 7.6 1.2 18,220 40,877 3,008 965,083 71,012 4.2% 2,131,712 Maldives 7.6 1.2 78 176 13 4,132 304 4.3% 9,128 United Arab Emirates 7.6 1.2 21,983 49,236 3,623 1,157,552 85,175 4.3% 2,556,845 Malaysia 7.7 1.2 85,270 190,259 14,000 4,430,302 325,989 4.3% 9,785,821 Croatia 8.4 1.3 13,383 29,197 2,148 640,721 47,145 4.6% 1,415,249 U.S. 8.4 1.3 2,186,346 4,769,534 350,951 104,643,221 7,699,831 4.6% 231,139,965 Dominican Republic 8.4 1.3 11,457 24,939 1,835 543,953 40,025 4.6% 1,201,504 El Salvador 8.5 1.4 4,884 10,617 781 230,573 16,966 4.6% 509,298 Thailand 8.5 1.4 119,552 259,351 19,083 5,602,345 412,230 4.6% 12,374,675 Serbia 8.6 1.4 17,995 38,962 2,867 837,037 61,591 4.7% 1,848,879 Romania 8.7 1.4 52,791 113,960 8,385 2,427,610 178,628 4.7% 5,362,198 Turkey 8.8 1.4 175,775 378,914 27,881 8,039,190 591,538 4.7% 17,757,272 Tonga 8.8 1.4 163 351 26 7,424 546 4.7% 16,399 Bulgaria 8.8 1.4 24,558 52,818 3,886 1,113,203 81,911 4.7% 2,458,886 Lithuania 8.9 1.4 14,120 30,330 2,232 636,936 46,867 4.8% 1,406,889 Sri Lanka 9.1 1.4 14,273 30,505 2,245 631,191 46,444 4.8% 1,394,200 Kiribati 9.9 1.6 164 344 25 6,666 490 5.2% 14,724 Moldova 10.2 1.6 6,445 13,386 985 252,939 18,612 5.3% 558,700 China 10.3 1.6 1,813,452 3,759,435 276,626 70,611,913 5,195,748 5.3% 155,970,306 Mexico 10.5 1.7 370,188 763,816 56,203 14,116,925 1,038,748 5.4% 31,182,006 Ukraine 10.9 1.7 106,463 217,942 16,037 3,917,898 288,286 5.6% 8,654,005 Belarus 10.9 1.7 37,254 76,249 5,611 1,369,761 100,789 5.6% 3,025,582 Costa Rica 10.9 1.7 9,353 19,134 1,408 343,099 25,246 5.6% 757,850 Bosnia and Herzegovina 11.2 1.8 9,110 18,522 1,363 324,713 23,893 5.7% 717,238 Dominica 11.3 1.8 133 271 20 4,750 349 5.7% 10,491 Estonia 11.7 1.9 7,255 14,630 1,076 248,679 18,298 5.9% 549,292 Jordan 11.8 1.9 15,041 30,264 2,227 510,177 37,540 5.9% 1,126,898 Montenegro 11.8 1.9 2,277 4,582 337 77,208 5,681 5.9% 170,540 Azerbaijan 12.2 2.0 13,491 26,944 1,983 441,088 32,456 6.1% 974,292 Albania 12.4 2.0 4,434 8,840 650 143,692 10,573 6.2% 317,392 Sweden 12.7 2.0 64,725 128,399 9,448 2,044,465 150,435 6.3% 4,515,892 New Zealand 12.9 2.1 41,526 82,113 6,042 1,289,882 94,912 6.4% 2,849,141 Comoros 13.0 2.1 344 680 50 10,629 782 6.4% 23,479 98

Armenia 13.6 2.2 4,145 8,113 597 121,971 8,975 6.7% 269,414 Guatemala 13.8 2.2 15,560 30,393 2,236 452,460 33,293 6.7% 999,410 Iran 13.9 2.2 221,049 431,299 31,736 6,388,700 470,092 6.8% 14,111,607 Philippines 14.0 2.2 42,694 83,183 6,121 1,224,175 90,077 6.8% 2,704,005 Norway 14.0 2.2 43,467 84,654 6,229 1,243,420 91,493 6.8% 2,746,514 Latvia 14.2 2.3 8,797 17,101 1,258 248,978 18,320 6.9% 549,952 Finland 14.2 2.3 42,928 83,443 6,140 1,214,664 89,377 6.9% 2,682,996 Indonesia 14.2 2.3 254,778 494,825 36,410 7,175,470 527,984 6.9% 15,849,455 Tunisia 14.8 2.4 20,982 40,465 2,978 567,451 41,754 7.1% 1,253,407 Georgia 15.1 2.4 9,257 17,802 1,310 246,178 18,114 7.2% 543,767 Fiji 15.1 2.4 2,419 4,649 342 64,116 4,718 7.3% 141,621 India 15.8 2.5 376,175 717,678 52,808 9,530,965 701,305 7.5% 21,052,361 South Africa 16.4 2.6 148,174 281,063 20,681 3,620,249 266,384 7.8% 7,996,545 Syria 16.5 2.6 22,269 42,201 3,105 540,808 39,794 7.8% 1,194,558 Panama 16.8 2.7 8,834 16,688 1,228 210,208 15,467 7.9% 464,316 Viet Nam 16.9 2.7 36,637 69,152 5,088 866,885 63,787 8.0% 1,914,809 Honduras 17.0 2.7 13,171 24,846 1,828 310,484 22,846 8.0% 685,809 Colombia 17.0 2.7 122,089 230,257 16,943 2,873,705 211,452 8.0% 6,347,549 Swaziland 17.2 2.8 1,995 3,754 276 46,318 3,408 8.1% 102,308 Nigeria 17.8 2.8 102,572 192,222 14,144 2,313,304 170,217 8.3% 5,109,716 Brazil 17.9 2.9 932,301 1,744,696 128,378 20,824,881 1,532,331 8.4% 45,998,796 Chile 18.9 3.0 78,624 146,019 10,744 1,664,287 122,461 8.8% 3,676,142 Micronesia 19.0 3.0 74 137 10 1,550 114 8.8% 3,424 Morocco 19.2 3.1 46,558 86,311 6,351 972,632 71,568 8.9% 2,148,387 Samoa 19.4 3.1 290 537 40 5,978 440 9.0% 13,203 Venezuela 19.5 3.1 90,246 166,859 12,278 1,849,039 136,056 9.0% 4,084,229 Argentina 20.0 3.2 274,024 505,195 37,173 5,494,531 404,297 9.2% 12,136,532 Saudi Arabia 20.0 3.2 215,150 396,630 29,185 4,312,075 317,290 9.2% 9,524,677 Haiti 20.6 3.3 2,674 4,909 361 51,951 3,823 9.5% 114,752 Zimbabwe 20.7 3.3 37,460 68,762 5,060 726,409 53,450 9.5% 1,604,521 Pakistan 21.1 3.4 73,021 133,664 9,835 1,385,153 101,922 9.6% 3,059,578 Iraq 21.6 3.5 40,452 73,826 5,432 749,224 55,129 9.9% 1,654,914 Egypt 21.8 3.5 91,304 166,462 12,249 1,677,032 123,399 9.9% 3,704,293 Ecuador 21.9 3.5 25,335 46,176 3,398 464,275 34,162 9.9% 1,025,508 Cuba 22.6 3.6 9,734 17,673 1,300 172,773 12,713 10.2% 381,628 Uruguay 22.6 3.6 15,500 28,140 2,071 274,891 20,227 10.2% 607,190 Bangladesh 23.1 3.7 11,260 20,385 1,500 195,043 14,352 10.5% 430,818 Ghana 23.3 3.7 19,528 35,323 2,599 335,626 24,696 10.5% 741,343 Oman 24.9 4.0 24,844 44,603 3,282 399,362 29,386 11.2% 882,127 Bahamas 25.1 4.0 797 1,429 105 12,695 934 11.3% 28,041 Nicaragua 26.2 4.2 9,156 16,349 1,203 139,898 10,294 11.7% 309,012 Belize 26.8 4.3 1,703 3,035 223 25,480 1,875 11.9% 56,282 Russia 26.9 4.3 1,218,234 2,169,645 159,646 18,146,271 1,335,234 12.0% 40,082,181 Burundi 27.3 4.4 1,883 3,350 246 27,662 2,035 12.1% 61,101 Canada 27.6 4.4 659,790 1,172,136 86,248 9,586,498 705,391 12.2% 21,175,025 Uzbekistan 27.6 4.4 30,829 54,763 4,030 447,414 32,921 12.2% 988,264 Iceland 28.5 4.6 7,037 12,460 917 98,907 7,278 12.6% 218,469 99

Tajikistan 29.4 4.7 9,639 17,019 1,252 131,472 9,674 12.9% 290,401 Australia 29.5 4.7 521,591 920,707 67,747 7,091,674 521,818 13.0% 15,664,361 North Korea 29.5 4.7 8,169 14,419 1,061 110,982 8,166 13.0% 245,140 Benin 30.1 4.8 7,359 12,966 954 98,040 7,214 13.2% 216,555 Guinea-Bissau 30.1 4.8 1,869 3,292 242 24,870 1,830 13.2% 54,933 Kenya 31.7 5.1 35,864 62,881 4,627 452,569 33,301 13.9% 999,652 Algeria 32.2 5.2 147,809 258,812 19,044 1,836,902 135,162 14.1% 4,057,420 Cambodia 32.6 5.2 10,813 18,912 1,392 132,643 9,760 14.3% 292,986 Kyrgyzstan 32.9 5.3 11,665 20,390 1,500 142,069 10,454 14.4% 313,807 Yemen 33.2 5.3 31,810 55,559 4,088 383,800 28,241 14.5% 847,753 Peru 33.3 5.3 76,921 134,319 9,883 925,670 68,112 14.5% 2,044,655 Senegal 33.6 5.4 11,471 20,017 1,473 136,871 10,071 14.6% 302,327 Uganda 35.0 5.6 11,256 19,574 1,440 128,736 9,473 15.2% 284,356 Malawi 36.5 5.8 5,160 8,944 658 56,669 4,170 15.8% 125,172 Côte d'Ivoire 37.0 5.9 17,168 29,724 2,187 185,605 13,657 16.0% 409,971 Kazakhstan 37.2 6.0 145,045 251,032 18,471 1,560,528 114,826 16.1% 3,446,955 Afghanistan 37.4 6.0 34,849 60,290 4,436 372,882 27,437 16.2% 823,637 Gambia 37.6 6.0 539 932 69 5,741 422 16.2% 12,680 Turkmenistan 40.4 6.5 23,236 39,972 2,941 230,068 16,929 17.4% 508,184 Bhutan 41.5 6.6 1,849 3,175 234 17,836 1,312 17.8% 39,398 Madagascar 42.4 6.8 27,400 46,981 3,457 258,528 19,023 18.2% 571,046 Djibouti 43.0 6.9 1,079 1,848 136 10,052 740 18.4% 22,204 Libya 43.9 7.0 80,232 137,271 10,101 732,607 53,906 18.7% 1,618,210 Nepal 44.7 7.1 6,420 10,971 807 57,569 4,236 19.1% 127,160 Paraguay 46.9 7.5 16,957 28,893 2,126 144,935 10,665 19.9% 320,137 Burkina Faso 49.6 7.9 11,046 18,757 1,380 89,236 6,566 21.0% 197,108 Angola 50.5 8.1 49,384 83,773 6,164 391,726 28,824 21.4% 865,260 Cameroon 53.2 8.5 17,763 30,045 2,211 133,668 9,836 22.5% 295,251 Bolivia 54.2 8.7 39,981 67,559 4,971 295,482 21,742 22.9% 652,673 Laos 57.9 9.3 7,978 13,435 989 55,219 4,063 24.3% 121,971 Sudan 58.0 9.3 64,220 108,142 7,957 443,671 32,646 24.4% 979,998 Eritrea 58.7 9.4 3,442 5,793 426 23,493 1,729 24.7% 51,893 Myanmar 58.7 9.4 22,262 37,465 2,757 151,867 11,175 24.7% 335,450 Sierra Leone 60.3 9.6 2,376 3,993 294 15,785 1,161 25.3% 34,866 Botswana 60.9 9.7 18,603 31,250 2,299 122,281 8,998 25.6% 270,099 Zambia 65.2 10.4 22,806 38,191 2,810 140,102 10,309 27.3% 309,463 Mozambique 67.6 10.8 23,253 38,877 2,861 137,689 10,131 28.2% 304,132 Tanzania 67.9 10.9 26,107 43,643 3,211 154,080 11,337 28.3% 340,337 Equatorial Guinea 70.6 11.3 795 1,327 98 4,511 332 29.4% 9,965 Namibia 78.4 12.5 21,008 34,917 2,569 107,351 7,899 32.5% 237,120 Ethiopia 81.0 13.0 24,677 40,964 3,014 121,942 8,973 33.6% 269,351 Lesotho 84.0 13.4 723 1,199 88 3,449 254 34.8% 7,617 Togo 85.3 13.7 1,275 2,112 155 5,984 440 35.3% 13,217 Guinea 87.3 14.0 5,630 9,322 686 25,835 1,901 36.1% 57,064 Papua New Guinea 94.8 15.2 9,552 15,773 1,161 40,343 2,968 39.1% 89,110 Mali 98.2 15.7 24,847 40,986 3,016 101,321 7,455 40.5% 223,802 Liberia 118.2 18.9 1,630 2,676 197 5,526 407 48.4% 12,205 100

Mongolia 120.0 19.2 25,888 42,470 3,125 86,386 6,356 49.2% 190,813 Vanuatu 129.4 20.7 188 308 23 583 43 52.9% 1,287 Niger 132.4 21.2 19,132 31,315 2,304 57,869 4,258 54.1% 127,824 Suriname 140.2 22.4 2,226 3,639 268 6,359 468 57.2% 14,047 Gabon 144.7 23.2 3,560 5,816 428 9,851 725 59.0% 21,759 Chad 170.5 27.3 14,774 24,060 1,770 34,712 2,554 69.3% 76,673 Solomon Islands 177.4 28.4 316 513 38 712 52 72.1% 1,573 Somalia 194.6 31.1 6,446 10,475 771 13,264 976 79.0% 29,298 Guyana 232.3 37.2 1,695 2,747 202 2,922 215 94.0% 6,453

101