Energy Usage of Personal Rapid Transit Systems Simulation of the Skycab Concept

Home , Aquaplaning, Slip angle, Tweel, Tyre label

AnnualEnergy Report Usage 2011 of Personal Rapid Transit Systems Simulation of the SkyCab Concept School of Engineering Sciences Master of Science Thesis by Platzhalterﬂäche für Bildmotiv cand. ing. Alexander Vogel

Prof. Mats Berg Prof. Eckehard Schnieder Dipl. Ing. Tamás Kurczveil

TRITA-AVE 2015:11 ISSN 1651-7660 IVA Nr. 1433 BioingenieurwesenStrukturiertes Doktorat Matr. 4349156

March 2015 BachelorWorkshop-Programm und Master of Science KTH Royal Institute of Technology Technische Universität Braunschweig Department of Aeronautical and Institut für Verkehrssicherheit und Vehicle Engineering: Division of Rail Vehicles AutomatisierungstechnikWintersemester 2014/15

Sammanfattning

Den globala situationen för person- och godstransporter visar att energianvändningen inom transportsektorn stadigt ökar och prognoser tyder på att den kommer att fördubblas till 2050. Den största ökningen förväntas ske i Asien där, Kina kommer att stå för över 12 % av den globala energianvändningen år 2050. Inom EU, Europeiska Unionen, stod personbilarna 2012 för över 81 % av passagerartransporterna räknat i antal passager- arkilometrar. Nya energieffektiva och miljövänliga transportlösningar behöver utvecklas. En lösning med spårtaxi kombinerar fördelarna med konventionella vägtransportsys- tem (flexibilitet, tillgänglighet och attraktivitet) och spårtransportsystem (säkerhet, ka- pacitet och miljövänlighet). I detta examensarbete undersöks energianvändningen för spårtaxi. Detta sker i form av en fallstudie. Spårtaxi är en automatiserad transporttjänst för direktresor utan väntetider (likt taxiservice) i ett nätverk med banor som kompletterar masstransportsystem. Fokus i studien ligger på att utvärdera fordonens energianvändning i drift. Målet är att identifiera relevanta parametrar som avgör energianvändningen samt deras bidrag till denna. Frågan om effektiv energianvändning besvaras med hjälp av en simuleringsmodell. Denna baseras på konceptet SkyCab och en bedömning av fordonets parametrar. En beräkning är utförd som utgör en referens för att sedan jämföras med 16 variationer av nyckelparametrar. Relationen till växhusgaser undersöks och utsläppen beräknas för olika elektricitetsblandningar. Ett andragradspolynom är framtaget för att beskriva fordonets gångmotstånd som inkluderar uppskattningar av vagnens rullmotstånd för små, pneumatiska däck på en raksträcka samt i doserade kurvor. Hjälpkraftens energianvändning uppskattas säsom motsvarande en liten elektrisk bil och är starkt beroende av passagerarnas komfortbehov och yttre (väder)förhållanden. Ett resultat är att rullmotståndet står för cirka 44 % av energianvändningen och hjälpkraften för 33 %. Båda är potentiella mål för effektivitetsförbättringar. Ändringar av accelerationsnivåer har liten betydelse för energianvändningen då det är en mindre del av energin som regenereras. En ökning av topphastigheten är ett effektivt sätt att minska restiden med förhållanderis liten ökning av energianvändningen. Förslag lämnas i studien hur man kan minska energianvändningen genom att förbättra fordonets och banans nyckelegenskaper.

Keywords: Energianvändning, spårtaxi, fordon, bana, simulering, parametervari- ation, elektrisk framdrivning, förarlös, nätverk

I II Abstract

The global situation of personal and freight transport shows that the energy demand for transportation steadily increases, and prognoses indicate that the energy usage will double until 2050. The largest growth rates are expected in Asia, and China in particular will account for over 12 % of global transport energy usage in 2050. Over 81 % of passenger transport in passenger kilometre was produced by passenger cars in 2012 in the European Union, and new energy efficient and environmental friendly solutions have to be developed. PRT (Personal Rapid Transit) systems combine the benefits of traditional road systems (flexibility, accessibility, attractiveness) and rail systems (safety, capacity, environmental friendliness). This MSc thesis investigates a concept by SkyCab AB as a case study, which offers an automated, non-stop and on-demand transportation service in a dedicated network and is supposed to fill a gap between personal cars and public transport. The focus is put on the energy usage of the vehicles in the operational phase. The objective is to identify the relevant parameters that determine the energy usage and their contributions. This request is addressed by setting up a simulation model, based on the SkyCab concept and estimations of vehicle parameters. A reference calculation and 16 variations of key parameters are conducted. The relation to greenhouse gas emissions is investigated and emissions are calculated for different electricity mixes. A second-order polynomial of running resistance for the vehicle is determined, including estimations of rolling resistance of small pneumatic tyres on straight track and in superelevated curves. The auxiliary power is estimated for the SkyCab vehicle on basis of a small electric passenger car. For the reference case the energy for rolling resistance is approx. 44 % of the energy usage, and auxiliary energy contributes by 33 %. Both offer potential for efficiency improvement. The auxiliary power is strongly dependent on the passengers’ comfort needs and the ambient conditions. Changes of acceleration rates have low impact on the energy usage, since a smaller proportion of energy is regenerated. An increase in top speed is a sufficient measure to reduce trip time with comparably low increase in energy usage. Finally, suggestions are proposed to reduce the energy usage by improving key properties of the vehicle and guideway.

Keywords: Energy usage, Personal Rapid Transit, tracked taxi, vehicle, guideway, simulation, parameter variation, electric propulsion, autonomous, network

III IV Zusammenfassung

Der weltweite Energiebedarf des Personen- und Gütertransports zeigt einen kontinuier- lichen Anstieg, und der Ausblick bis 2050 zeigt eine Verdopplung des gesamten Ener- giebedarfs. Die größten Zuwachsraten werden in Asien erwartet, und insbesondere China allein wird in 2050 über 12 % des weltweiten Energiebedarfs verzeichnen. Über 81 % aller Personenkilometer in der Europäischen Union in 2012 wurden mit dem persönlichen Auto- mobil durchgeführt, und ein Bedarf für energieeffiziente und umweltfreundliche Transport- möglichkeiten wird deutlich. PRT (Personal Rapid Transit) Systeme vereinen die Vorzüge von traditionellen straßenge- bundenen Transportsystemen (Flexibilität, Zugänglichkeit, Attraktivität) und Schien- ensystemen (Sicherheit, Kapazität, Umweltfreundlichkeit). Diese MSc Thesis untersucht das Transportkonzept von SkyCab AB als Fallstudie. Es bietet einen automatisierten, un- unterbrochenen und bedarfsgesteuerten Transportdienst auf einem exklusiven Netzwerk und soll so die Lücke zwischen dem persönlichen Automobil und öffentlichen Transport- mitteln schließen. Der Fokus wird dabei auf den Energieverbrauch des Fahrzeugs in der operativen Phase gelegt. Die Zielsetzung besteht in der Identifizierung und Quantifizierung der relevanten Para- meter, die den Energieverbrauch bestimmen. Zu diesem Zweck wird ein Simulationsmodell konfiguriert welches auf dem Konzept von SkyCab basiert und zusätzlich Abschätzungen von Fahrzeugparametern enthält. Eine Referenzberechnung und 16 Parametervariationen werden durchgeführt. Der Bezug zur Emission von Treibhausgasen wird für verschiedene Energiemixe hergestellt. Das Polynom zweiter Ordnung für den Fahrwiderstand wird aufgestellt, wobei Abschätzun- gen bezüglich des Rollwiderstands kleiner pneumatischer Reifen auf gerader Strecke und in überhöhten Kurven berücksichtigt werden. Die Zusatzleistung für das Konzeptfahrzeug wird auf Basis eines kleinen rein elektrischen Fahrzeugs abgeschätzt. Der Energieverbrauch in der Referenzsimulation für den Rollwiderstand beträgt ca. 44 % des Gesamtenergieverbrauchs, und die Zusatzenergie beläuft sich auf ca. 33 %. Beide Anteile bieten Potential zur Optimierung, und die Zusatzenergie ist stark abhängig von den Komfortbedürfnissen der Passagiere und den Umgebungsbedingungen. Variationen der Beschleunigungs- und Bremsraten haben einen geringen Einfluss auf den Energiever- brauch, da gleichzeitig ein kleinerer Anteil regeneriert wird. Eine Zunahme der Höchst- geschwindigkeit wirkt sich durch mehr regenerierte Energie vergleichsweise gering auf den bezogenen Energieverbrauch aus, reduziert jedoch die Fahrzeit merklich. Abschließend werden Potentiale von Schlüsselparametern zur Reduktion des Energieverbrauchs des Fahrzeugs und der Fahrbahn aufgedeckt.

Keywords: Energieverbrauch, Personal Rapid Transit, Fahrzeug, Simulation, Para- meter, Variation, autonom, elektrischer Antrieb, Netzwerk

V VI Preface

This MSc thesis is the final part of my studies on Mechanical Engineering. It was carried out at the Department of Aeronautical and Vehicle Engineering in the Division of Rail Vehicles at KTH Royal Institute of Technology in Stockholm. The supervision in Germany was provided by the Institute for Traffic Safety and Automation Engineering (IVA) at Technische Universität Braunschweig. I would like to thank my supervisor at KTH, Mats Berg, for giving me the opportunity and for his support and constructive criticism throughout all phases of this thesis. In the same way I would like to thank my supervisors Eckehard Schnieder and Tamás Kurczveil at IVA for their guidance and their trust in me. I am thankful for the proposition of the thesis by Åke Åredal from SkyCab AB and his encouragement and interest in my work. I am very grateful for the help of Sebastian Stichel at KTH, who established the contact to Mats Berg and was my first contact person in Stockholm. I would like to thank Jenny Jerrelind from KTH Vehicle Dynamics for support in the first part of the thesis. I would also like to thank Johan Öberg from MiW Rail Technology AB for his great support during the simulation and his help when I needed it. Finally, I would like to thank my family and friends, for their continuous support and encouragement throughout my studies and this thesis.

March 2015

Alexander Vogel

VII I declare that I have authored this thesis with the title "Energy Usage of Personal Rapid Transit Systems" independently, that I have not used other than the declared resources, and that I have explicitly marked all material which has been quoted either literally or by content from the used sources.

Alexander Vogel Place, date

VIII Contents

List of Symbols XI

List of Figures XIII

List of Tables XV

Abbreviations XVII

1 Introduction 1 1.1 A new personal transport concept ...... 1 1.2 Deﬁnition of PRT systems ...... 3 1.3 Energy usage in PRT systems ...... 6 1.3.1 System energy architecture ...... 7 1.3.2 Life cycle phases ...... 8 1.3.3 The vehicle in the operational phase ...... 8 1.4 Problem formulation ...... 10

2 Energy usage of urban transport systems 11 2.1 Speciﬁc energy usage ...... 11 2.2 Comparison of urban transport modes ...... 13 2.3 Inﬂuence of occupancy rate ...... 15

3 Background: Resistance forces 17 3.1 Rolling resistance ...... 18 3.1.1 Straight track ...... 18 3.1.2 Flat curves ...... 19 3.1.3 Superelevated curves ...... 20 3.2 Aerodynamic resistance ...... 22 3.3 Acceleration resistance ...... 22 3.4 Gradient resistance ...... 23

4 System description 25 4.1 Technical description of the SkyCab system ...... 26 4.2 Track layout and speed proﬁle ...... 28 4.3 Running resistance diagram ...... 30 4.4 Operational conditions and auxiliary power ...... 35

5 Simulation of energy usage 39 5.1 Simulation software ...... 39 5.2 Reference simulation and parameter study ...... 42

IX 5.3 Results of reference simulation and its variations ...... 43 5.4 Discussion of results ...... 50 5.5 Greenhouse gas emissions for diﬀerent electricity mixes ...... 53

6 Improving energy efficiency 55 6.1 Reduction of rolling resistance ...... 55 6.1.1 Pavement and guideway ...... 55 6.1.2 Tyre ...... 56 6.1.3 Examples of energy efficient tyres ...... 58 6.2 Reduction of auxiliary power ...... 60 6.3 Reduction of aerodynamic resistance ...... 60 6.4 Drive train efficiencies and other factors ...... 61

7 Conclusions and future work 63

Bibliography 65

Appendices:

A Data for energy usage of urban transport modes 72

B Occupancy rate and energy usage per passenger kilometre 73

C Estimations of proportion of curves 74

D Vehicle validation with coeﬃcients from coast down tests 75

E Auxiliary power as function of temperature 76

F Monthly temperatures for investigated cities 77

G Simulation parameters and results 78

X List of Symbols

Latin Letters A [m2] Cross-sectional area of vehicle a [m/s2] Acceleration b [m] Width of vehicle C [N/rad] Cornering stiffness c [1] Coefficient dx [m] Track segment E [kWh] Energy F [N] Force f [1] Rolling resistance coefficient g [m/s2] Gravitational acceleration constant h [m] Height of vehicle i [1] Gear ratio J [kg m2] Mass moment of inertia l [m] Vehicle wheelbase m [1] Mass n [1] Number of seats P [W] Power p [bar] Pressure R [m] Curve radius r [m] Wheel radius s [km] Track length T [◦C] Temperature t [t] Time v [m/s] Speed

Greek Letters α [deg] Superelevation angle of curve γ [deg] Gradient β [deg] Slip angle η [%] Eﬃciency

XI κ [1] Relative mass factor µ [1] Adhesion coeﬃcient ρ [kg/m3] Air density

Indices 0 Basic 1 First coefficient 2 Second coefficient 3 Third coefficient acc Acceleration aux Auxiliary c Centrifugal ct Curved track D Air drag e Equivalent f Front G Weight force gr Gradient gross Gross l Lateral max Maximum N Normal to surface net Net occ Occupancy R Rolling resistance r Rear regen Regeneration rr Running resistance s Speed dependency st Straight track sup Superelevated t Tyre total Sum over all trac Traction w (Head)wind

XII List of Figures

1.1 Increase of transport energy demand ...... 2 1.2 Energy usage by sectors in the European Union in the operational phase .2 1.3 A typical PRT network ...... 5 1.4 Oﬀ-track station design ...... 5 1.5 Various PRT vehicles ...... 6 1.6 Energy ﬂow for a PRT system ...... 6 1.7 Energy distribution for a PRT system ...... 7 1.8 Energy composition of a full life cycle for the Vectus PRT system in Suncheon, South Korea ...... 7 1.9 Energy usage over the life cycle phases of the Vectus PRT system in Suncheon, South Korea ...... 8 1.10 Energy architecture of the vehicle from grid to the wheels ...... 9

2.1 Occupancy rates for metro and bus by daytime ...... 13 2.2 Energy usage in kWh/seat-km for for various urban transport modes . . . 15 2.3 Energy usage in kWh/pkm for various transport modes ...... 16

3.1 Resistance forces acting on a vehicle ...... 17 3.2 Rolling resistance coeﬃcients for the Stuttgart model ...... 19 3.3 Forces and accelerations in superelevated curves ...... 21

4.1 Concept pictures of the SkyCab vehicle ...... 25 4.2 Vehicle layouts for the SkyCab concept ...... 26 4.3 Tractive force at wheels as function of vehicle speed ...... 28 4.4 Track layout of the enlarged Arlanda region ...... 29 4.5 Methodology to determine average rolling resistance including curves and superelevation for the SkyCab vehicle ...... 30 4.6 Air drag coefficients comparison ...... 33 4.7 Running resistance curve for SkyCab ...... 34 4.8 Friction coefficient µ on different surfaces and conditions ...... 35 4.9 Tyre temperature and related tyre resistance ...... 36 4.10 Auxiliary power over temperatures and monthly temperatures ...... 37

5.1 Calculation process of STEC ...... 40 5.2 Sankey diagram of the energy split for the reference calculation ...... 41 5.3 Speeds and acceleration of the reference calculation ...... 44 5.4 Forces of the reference calculation ...... 45 5.5 Resistance force modelling close to zero velocity ...... 45 5.6 Powers and energies for the reference calculation ...... 46 5.7 Results of the reference calculation ...... 47

XIII 5.8 Results of the parameter variation ...... 48 5.9 Relative impact on energy usage of the parameter variation of the reference calculation ...... 49 5.10 Relative impact on regenerated energy of the parameter variation of the reference calculation ...... 49 5.11 Comparison of SkyCab to competitive PRT systems on the basis of kWh/seat- km...... 52 5.12 Comparison of SkyCab to competitive PRT systems on the basis of kWh/pkm 53

6.1 Pavement surfaces and their impact on rolling resistance ...... 56 6.2 Conflicting goals during tyre development ...... 57 6.3 Influence of tyre diameter and shoulder temperature on the rolling resistance coefficient ...... 58 6.4 EU tyre labels for electric vehicle tyres ...... 59 6.5 Airless tyre "Tweel" by Michelin ...... 59 6.6 Aerodynamic drag coefficient in relation to various front and rear design combinations ...... 61

Appendices:

D.1 Comparison of calculations and coast down tests for two sedan cars . . . . 75

E.1 Auxiliary power as function of temperature ...... 76

G.1 Input and results of the simulations (part 1) ...... 78 G.2 Input and results of the simulations (part 2) ...... 79

XIV List of Tables

1.1 Conﬂicting goals for the energy eﬃciency of a vehicle ...... 6

2.1 Technical data for three PRT systems ...... 14

4.1 Component efficiencies with references and combined overall efficiency . . . 28 4.2 Curve definition: Curve radii, design speed and superelevation angles . . . 29 4.3 Tyre data for reference tyre ...... 31 4.4 Resistance forces and total running resistance of SkyCab vehicle ...... 33 4.5 Auxiliary power of the SkyCab vehicle for considered cities ...... 38

5.1 Summary of reference vehicle variables for simulation ...... 42 5.2 Input values to the simulation software ...... 43 5.3 Varied parameters and their values for the simulation ...... 43 5.4 Greenhouse gases in relation to produced electric energy for the Nordic market ...... 54 5.5 Greenhouse gases in relation to produced electric energy for green energy sources ...... 54

6.1 Detailed variations of cD and the related change in % ...... 61

Appendices:

A.1 Data for energy usage of urban transport modes ...... 72

B.1 Occupancy rate and energy usage per passenger kilometre ...... 73

C.1 Estimations for proportion of curves ...... 74

F.1 Monthly temperatures and year averages for Stockholm, Delhi and Beijing 77

XV XVI Abbreviations

AC Alternating Current

DC Direct Current

EU European Union

GRT Group Rapid Transit

HVAC Heating, Ventilation and Air Conditioning

ICT Information and Communication Technologies

LCA Life Cycle Assessment

PRT Personal Rapid Transit

XVII XVIII Chapter 1

Introduction

The following chapter will give an overview on the global situation of personal transport and an outlook until 2050. A possible approach by a new transportation concept is presented, and the basic system with its key features is described. The system boundary of the transport concept that is to be investigated in this thesis is deﬁned with respect to the energy usage in diﬀerent life cycle phases and subsystems. As a conclusion, the present problem is formulated in questions towards the end of the chapter.

1.1 A new personal transport concept

A major feature of modern life is personal flexibility and freedom, especially when it comes to personal travelling. People need transportation services to increase their geographical area of living and to use resources of all kinds that are not only geographically surrounding them [91]. Personal transportation is the basis of everyday life and economic development and will increase as economies grow [61]. For economic development, reliable, safe, secure, efficient, and affordable transport services need to be available [91]. It has to be noted that transportation affects and reacts to economic growth in the same way [91]. Additional aspects of enhanced mobility are better access to schools and health services and a larger variety of goods [91]. A look at the global situation of transport shows that the energy demand for transportation steadily increases [25]. The forecast by the World Business Council for Sustainable Development (WBCSD) for global transport energy usage until 2050 by region (Figure 1.1) shows annual growth rates of 1.8 % (2000-2030) and 1.0 % (2000-2050), respectively [91], where the total energy demand roughly doubles until 2050. International marine bunkers in Figure 1.1 refer to the energy used for transport in international waters that can not be associated with specific countries. The largest growth rates are expected in Asia and China in particular. China will account for over 12 % of total global transport energy usage in 2050. This indicates not only a dramatical increase in total energy demand, but also a significant regional shift in transport energy usage from OECD countries in 2000 to developing countries in South and East Asia in 2050, leading to new potential markets for energy efficient technology. The lowest growth rates are predicted to be in the OECD countries, which may be explained by the already high standard of technological development. The growth of energy usage in OECD Europe (i.e. Western Europe) is comparatively low due to a slower population growth, high fuel taxes and higher improvements in efficiency [61].

1 Chapter 1. Introduction

Average Annual Growth Rates 2000-2030 2000-2050 Exajoules PWh Total 1.8% 1.0% 55.56200 Eastern Europe 2.1% 1.3% Middle East 2.1% 1.2% Africa 3.2% 1.8% 41.67150 Former Soviet Union 2.7% 1.6% India 3.6% 2.1% Other Asia 3.0% 1.8% 27.78100 Latin America 2.9% 1.7% China 4.2% 2.4% OECD Pacific 0.6% 0.4% 13.8950 OECD Europe 0.9% 0.4% OECD North America 1.2% 0.6% 0 0 International Marine Bunkers 0.9% 0.5% 2000 2010 2020 2030 2040 2050 Figure 1.1: Increase of transport energy demand for passengers and goods by region between 2000 and 2050. Units were modiﬁed for conformity by the following conversion: 3.6 EJ = 1 PWh (adapted from [91]).

For the European Union (EU)1, a split in sectors of energy usage can be made. The ﬁnal energy demand for passenger and goods transportation in the operational phase makes up for 31.8 % of the total energy usage over all sectors in 2012 as can be seen in Figure 1.2, which makes transportation the second largest consumer after households and services and a sector being important to improve on.

PWh 13.956 12.793 11.630 10.467 9.304 8.141 6.978 5.815 4.652 3.489 2.326 1.163

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 (a) Development of energy usage from 1990 (b) Detailed look at the year 2012 to the year 2012

Figure 1.2: Energy usage by sectors in the European Union in the operational phase (adapted from [31]). Units have been modiﬁed for conformity from Mtoe (Mega tons oil equivalent) to PWh by the following conversion factor: 100 Mtoe= 1.163 PWh

1EU-28: This includes the 28 member states of the European Union since 1. July 2013, as referred to in [31].

2 1.2. Deﬁnition of PRT systems

In addition to increasing demands for energy, costs will grow even more. Between 2005 and 2013, costs for fuel in the EU increased by 43.2 % [31]. This trend will continue, as fossil fuels for worldwide transportation are limited and rely on petroleum by 95 % [91, 61]. Most people do not use efficient public transport systems, but use their personal car for everyday travelling. In the EU, 81.6 % of the passenger kilometres is produced by passenger cars in 2012, whereas only 9.3 % of travelling people in the European Union use public buses [31]. This large share of the personal car transport mode has led to congestion, air pollution and slow flowing traffic [16]. This alarming picture drawn by the energy usage outlook requests a change in technology towards more efficient transport systems and new innovations for a sustainable transportation concept. As mentioned before, personal cars contribute to the problem to a large extent. A shift to public transport and the reduction of cars will not only lead to less fuel usage and greenhouse gas emissions, but also to reduced noise, air pollution and traffic congestion [40]. This mode shift is the first step to a reduction of energy usage, but further measurements have to be taken to address the forecasted increase in energy demand. Improved efficiencies for vehicles and the overall transport systems, but also for the travel itself are needed [12]. This is an important point, since significant amounts of energy could be saved if vehicles need to stop fewer times and thus have less energy demanding acceleration periods. Fossil fuels have to be saved, and transportation systems should use less energy for the same or even increased transportation services. The European Union is facing challenges from dependency on energy imports and the need for climate- friendly energy sources. The directive 2012/27/EU by the European Union focuses on the improvement on energy efficiency and addresses those challenges. The goal is to reach a reduction of primary energy usage in total by 20 % until 2020 [30]. The United States introduced a variety of policies to encourage developments towards higher energy efficiency, including mandatory and voluntary standards [74]. One of the problems for new personal transit systems is the low acceptance of the market for unknown systems that should complement existing and well proved modes of transport. Companies tend to push their new technology to the market, but without convinced customers this procedure is not very promising [55]. Another problem is the convenience, flexibility and value of a private car, to which public transportation can hardly compete so far [16]. This means that in order to attract people to travel with new public transport systems, they have to be offered the same or even better comfort: short waiting times, on-demand travelling, non-stop travel and travel with people of their choice [3].

1.2 Deﬁnition of PRT systems

To meet the requirements of a competitive transportation system, it has to combine the benefits of existing modes of transport. A combination of traditional road systems (flexibility, accessibility, attractiveness) and rail systems (safety, capacity, environmental friendliness) should lead to a system that is accepted by the customer and environmental friendly at the same time. A Personal Rapid Transit (PRT) is only one of many intelligent mass transportation concepts, among which are buses, vanpools, metros and trains [3]. The concept of PRT was established in the 1960’s, when the first publication was made by Fichter [33]. Since

3 Chapter 1. Introduction then, more than 200 references on this topic were published as of 2005 [22]. There were around 40 known PRT concepts until 2007, of which 19 were considered active [23]. The typical attributes that determine a PRT system were deﬁned by the Advanced Transit Association (ATRA) and are listed in the following collection [75]: • Fully automated vehicles (i.e., without human drivers)

• Vehicles captive to a guideway, which is reserved for the vehicle

• Small vehicles available for exclusive use by an individual or a small group travelling together by choice. These vehicles can be available for service 24 hours a day, if desired.

• Slender guideways that can be located above ground, at or near ground level, or underground.

• Vehicles able to use all guideways and stations on a fully connected (a “coupled”) PRT network.

• Direct origin to destination service, without a necessity to transfer or stop at intermediate stations (i.e., “nonstop” service).

• Service available on demand rather than on fixed schedules. A PRT system is predestined to act as a feeder system to public transport, for example at airports, business parks and city centres [55]. Its aim is not to replace existing transport systems, but to enhance their possibilities. It can be seen as a system in the niche between rail and road transportation systems, which combines the benefits of flexibility similar to a personal car and capacity and energy efficiency of trains. A typical PRT system can be divided in three physical parts: guideway, stations and vehicles. They are all necessary for the system’s functionality. The vehicles run autonomously on their dedicated network and stop at stations to pick up and drop passengers. Compared to a typical bus or metro corridor network, the PRT network is designed as a mesh with a maximum walking distance for passengers of approximately 150 to 300 m [83, 56]. A typical guideway network is shown in Figure 1.3, connecting city centres (red), living areas (blue), work places (yellow) and leisure places (green).

Guideway The guideway provides the main infrastructure on which the vehicles operate. It can be on ground level, elevated or in tunnels and should have a low proﬁle to limit the visual impact in urban areas [27, 4]. Anderson [4] summarised the diverse design criteria in his paper on "How to design a PRT guideway" in 2009. The main safety advantage of PRT systems comes from their dedicated guideways, which they do not share with other modes of transport and therefore have no level crossing [78].

Stations The stations can be on ground level or elevated and can not be compared to typical railway stations. They are much smaller and should be understood as on-oﬀ- points [83]. There is no need for long stations as the vehicles are short and only carry 2-4 passengers. Due to high frequency of incoming and leaving vehicles the station times are short compared to metro dwelling times. The stations are designed to be oﬀ the main track to enable vehicles to bypass the stations or wait in the queue [78]. Such a typical station design is shown in Figure 1.4,

4 1.2. Deﬁnition of PRT systems

Figure 1.3: A typical PRT network combining city centres (red), living areas (blue), work places (yellow) and leisure places (green) in an urban environment but many other station concepts are possible [36]. In addition, buﬀer places are provided to reduce waiting times and secure vehicle availability [83].

bufferBuffertplatser places På-on- och and avstigningsplatser off-places

Figure 1.4: Oﬀ-track station design [83]

Vehicles The vehicles run fully automatic, usually either guided by sensors and markers on the guideway or by a rail system. Although people may feel uncomfortable without a driver, automatic personal transport is much safer than non-automated systems [16]. The overall traffic management is done by a control centre to optimize availability and system performance. The vehicle size is usually similar to small personal cars. In the case of SkyCab, which is the case study concept in this thesis, up to four passengers can be carried in one vehicle [87], although this number differs significantly for different concepts between 1 to 15 persons as found out by Cottrell and Mikosza [23]. The line speeds differ in the same way, reaching from 20 km/h to 250 km/h [23]. It is obvious that these concepts are very different in design and purpose and will suit different applications. Figure 1.5 shows four vehicles that are currently operating, although the Vectus system is special because it runs on rails as a guiding structure and can accommodate 6 instead of 4 people. The Morgantown system by Boeing [11] offers even 8 seats, which denotes it more to Group Rapid Transit (GRT) systems. It is one of the oldest systems and running in Morgantown, West Virginia since 1975. All vehicles have an electric propulsion in common and are running on special smooth guideways made of concrete or steel rails, respectively.

5 Chapter 1. Introduction

Figure 1.5: PRT vehicles from Ultra [23], Vectus [23], 2getthere [56] and Boeing [11]

PRT systems offer low top speeds in urban areas. However, they are able to operate at high average speeds due to short dwelling times and skipped intermediate stops [3]. This non-stop trip concept without transfers for the passengers enables high energy efficiency compared to other means of transport [23]. The optimisation of a PRT system is a trade-off between conflicting goals. If one attribute is modified to be more energy efficient, other properties of the system will likely suffer. Some of the most challenging conflicting goals are listed in Table 1.1.

Table 1.1: Conﬂicting goals for the energy eﬃciency of a vehicle

Attributes Conﬂicting attributes Rolling resistance Noise, traction, wet braking performance Smaller tyres: rot. masses, visual impact Rolling resistance Vehicle weight Travel distance per battery charge, crash safety Air drag Space and capacity Electric motor eﬃciency Heating/cooling for passenger cabin

1.3 Energy usage in PRT systems

During the operational phase, the PRT system can be divided in three parts. Those parts are vehicles, infrastructure and Information and Communication Technologies (ICT). They all use energy and have to work in combination in order to deliver the desired transportation service (Figure 1.6). The energy flow is split between those three subsystems and the share of energy usage may be significantly uneven. Due to the limited scope of this thesis, it is necessary to find and focus on the most energy consuming subsystem.

Vehicle

Transportation Energy input ICT service

Infrastructure

Figure 1.6: Energy ﬂow for a PRT system

6 1.3. Energy usage in PRT systems

1.3.1 System energy architecture Most energy for operating a PRT system is used for propulsion of the vehicles [3]. In addition to this, energy is necessary for heating or cooling the passenger cabin [40]. Both needs can be assigned to the vehicle itself, and Figure 1.7 from a previous study shows the distribution of energy usage for diﬀerent purposes. The shares for propulsion and heating are about 75 % of the total energy usage. To obtain a more detailed look on the energy

Figure 1.7: Energy distribution for a PRT system (adapted from [40])

composition of a PRT system, a full Life Cycle Assessment (LCA) has to be conducted. There are LCAs on various transport modes available [50, 21, 24], but investigations on PRT systems are rare. The most suitable reference is an LCA for the Vectus PRT system that was built in Suncheon, South Korea in 2012. Although it is operating on rail-like guiding structures in contrast to the SkyCab concept, it can be considered as a comparative system if the battery powered variant is regarded. The result of this analysis is shown in Figure 1.8, dividing the system in diﬀerent subsystems. Most of the energy is

S. Korea, battery

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% kWh

Track Stations Substations & power coll. Control & com. syst. Maintenance facility Vehicles

Figure 1.8: Energy composition of a full life cycle for the Vectus PRT system in Suncheon, South Korea. In the displayed scenario, the vehicles run on batteries and therefore are most similar to the SkyCab system (adapted from [27]).

used for the vehicles (approx. 60 %) and track (approx. 20 %), whereas the other parts of the system share the last 20 %. Therefore this thesis focuses on the energy used by the vehicle subsystem.

7 Chapter 1. Introduction

1.3.2 Life cycle phases The life cycle of a product can be divided in three phases of construction, operation and end-life or recycling [46]. The LCA for the Vectus PRT system with respect to the life cycle phases is summarized in Figure 1.9. The author set up a detailed model of all energy consuming factors and calculated the composition [27]. To his advantage, he was able to use some measurements as a basis for the calculations. Figure 1.9 indicates that

S. Korea, battery

0.00E+00 1.39E+08 2.77E+08 4.17E+08 5.56E+08 kWh

Construction Operation End-life

Figure 1.9: Energy usage over the life phases of the Vectus PRT system in Suncheon, South Korea, with 20 years of assumed vehicle lifetime (adapted from [27]) most of the energy is used during the operational phase, roughly twice as much as for construction, whereas the end-life phase uses very little energy in comparison. This leads to the conclusion that the operational phase of the vehicle should be investigated in this thesis. Other references came to the same conclusion for PRT systems [3, 40]. For public buses, personal cars and light rail, the use-phase is consuming most of the energy for the product life time [21, 24].

1.3.3 The vehicle in the operational phase The vehicle’s overall energy flow (Figure 1.10) can be described without specific knowledge or assumption of the vehicle power train layout. Nevertheless, for different layouts, small adoptions have to be made concerning the efficiency rates and the calculation of the overall power train efficiency, respectively. The composition of system components will find further consideration in Chapter 4, where the efficiencies and their references are displayed as well. The vehicle energy flow can be divided in two major groups, electrical and mechanical components. The main layout is taken and extended from Fernández [32], who designed the electrical drive system for a PRT system in his master thesis. For a better under- standing, red arrows account for energy flow from the battery to the wheels and green arrows for regeneration of energy while braking. Although the electrical grid is shown as a component in the figure, it is not part of the present energy study. It has to be noted that the charging process includes additional losses to those considered in the present simulations.

Electrical components The electrical part of the vehicle consists of the battery, controller/converter unit, motor, heating, ventilation and air conditioning systems and auxiliary systems with DC/DC converter. This DC/DC converter is necessary to step down the battery voltage to 12 V or 24 V of the minor consumers.

8 1.3. Energy usage in PRT systems

HVAC systems

Propulsion Transmission

Controller Electric Grid Battery /converter motor Gearbox Differential Wheels

DC/DC converter Auxiliary systems: • Illumination • Information/ communication • Steering

System boundary

Electrical vehicle components Mechanical vehicle components

Figure 1.10: Energy architecture of the vehicle from the public electricity grid to the wheels. The system can be divided in electrical and mechanical subsystems.

Controller/Converter The purpose of the controller unit is to convert the Direct Cur- rent (DC) power of the battery to the desired Alternating Current (AC) voltage and amplitude for the electrical motor for propulsion of the vehicle [32]. A converter is needed to utilise regenerative braking at a maximum. This unit enables regenerative braking at low speeds because it converts the voltage of the motor generated during braking to the desired charging voltage of the battery [54].

Motor The vehicle is driven by one or more electrical motors, depending on the drive train layout. For this purpose, a permanent magnet synchronous motor is used due to its high degree of eﬃciency [32]. The input to the motor comes from the converter unit and the motor converts this electrical energy to mechanical energy, which is then passed on to the transmission.

Auxiliary systems These systems are not necessarily needed for propulsion, but include safety, steering and cooling systems of the vehicle which are needed for operation. In addition, comfort systems for communication, information and illumination are summarised in this group. The power demanding Heating, Ventilation and Air Condition- ing (HVAC) components are wired to the high voltage side of the battery to reach a good degree of eﬃciency without conversion losses [32]. It includes energy usage for ventilation of the heated or cooled air in the passenger cabin and the overall energy usage will signiﬁcantly change with the climate conditions.

Mechanical components The mechanical part of the vehicle consists of the transmission and the wheels.

Transmission For an electrical vehicle, no clutch is needed between the motor and the transmission as it can produce torque up from zero revolutions per minute [54]. Nev-

9 Chapter 1. Introduction ertheless, a single gear transmission is mandatory to convert the high rotating speed of the motor (up to 10,000 rpm) to the desired wheel rotating speeds. This includes the gears and the diﬀerential gearbox between the wheels (Figure 1.10).

Wheels The wheels are the ﬁnal mechanical component of the drive train and transfer traction, braking, vertical and steering forces to the vehicle [49]. The losses in the contact area between tyre and guideway and due to tyre deﬂection are referred to as rolling losses or rolling resistance (Section 3.1).

1.4 Problem formulation

The energy usage of individual vehicles, acting as taxis, has to be as low as possible to be competitive to other means of transport and to reduce greenhouse gas (GHG) emissions. The key questions concerning the energy usage of PRT systems are the following and will be investigated in this thesis:

1. What parameters are relevant and which are aﬀecting the energy usage of tracked vehicles most?

2. How is the energy usage distributed among all factors (e.g. rolling resistance, air resistance, propulsion eﬃciency, braking eﬃciency as well as auxiliary systems)?

3. How does the energy usage aﬀect GHG emission?

4. How can the vehicle’s energy usage and GHG emissions be minimized so that the energy and environmental beneﬁts of the mobility aspect is clear? What can be done to reduce the energy usage?

10 Chapter 2

Energy usage of urban transport systems

The energy demand for the transportation process of passengers and goods takes up to 80 % of the total energy for transportation services, compared to energy usage for infrastructure, vehicle production, fuel production and distribution [50]. Highest emissions come from the operation phase [45, 8], and this is the phase that is presented in the following figures and numbers [52]. Although it is important to improve transport service efficiency and to attract passengers to a new transportation concept, it is important that they are attracted from the less efficient transport modes such as cars and not from cycling or walking [69]. For comparison of transport modes, the influencing parameters should be held constant, but they are not always stated in detail in the available publications on energy usage. This makes it necessary to be cautious while comparing numbers from various papers, as numbers can differ significantly due to differences in estimations and assumptions. The parameters on vehicle level that determine the energy usage of transport systems will be examined in Chapter 5 when the energy simulation is conducted for the SkyCab concept as a case study. In this chapter, the units to measure specific energy usage are explained and the influence of occupancy rate is made clear. Numbers for specific energy usage of various urban transport modes are mainly taken from papers on transport energy usage by Potter [69] and Blomberg [10], backed up with data from Kenworthy [52], Lowson [57] and Anderson [3]. They refer to the use-phase and represent the energy that is actually needed for operation of the vehicles.

2.1 Speciﬁc energy usage

Comparing various modes of transport with regard to their energy usage requires a common unit to express efficiency [57]. This is especially the case if energy is not only provided as fossil fuels to cars and buses, but also in other forms such as electricity to rail systems like trams and metros. The efficiency of vehicles can be expressed in different ways. Most common for personal cars is the expression in litre per 100 km (l/100 km) in Europe or miles per gallon (miles/gal) in the United States and United Kingdom1. For a more general specification, energy is measured in Watt hours, commonly in kWh. For comparison reasons, this energy

1Please note the diﬀerent conversion rates between litres and UK gallons and US gallons

11 Chapter 2. Energy usage of urban transport systems usage can be related to either the passenger kilometres travelled (kWh/pkm), to vehicle kilometres travelled (kWh/vehicle-km or simply kWh/km) or to seat kilometres travelled (kWh/seat-km). The energy in kWh/vehicle-km is dependent on the operational conditions of the vehicle, its size and the efficiency of its components. It is a result of internal and external factors and varies significantly by the type of vehicle. This measure is useful to monitor the energy usage of a single vehicle over time or for judging the measures to improve vehicle efficiency such as driver training or alternative fuels of personal cars [94]. Nevertheless, this number gives the same energy usage for a fully loaded or empty vehicle, independent of the transportation service it delivers, which is not a number that can be compared over various modes of transport. The comparability can be assured by relating the energy usage to the offered seats per vehicle kilometre (kWh/seat-km), giving a very general value with which the customer can compare using his own numbers for occupancy rate. When the specific energy usage is defined as kWh/pkm, it is depending on the occupancy rate cocc during transport services. This number indicates the ratio between passengers travelling and offered seats in the vehicle, relation 2.1. The percentage can be well over 100 %, especially for metros and trams, but also for buses due to standing passengers. Metro systems often have more people standing than sitting during peak times, which can give an occupancy rate of more than 200 % [69]. #passengers c = (2.1) occ #seats

The following relation between kWh/pkm, kWh/seat-km and cocc can be defined. kWh kWh E · c = E (2.2) pkm occ seat − km The relation between kWh/pkm and kWh/seat-km by occupancy rate is linear, which makes this value determinant for the energy usage. The difficulties in determining the occupancy of public transport systems is obvious. Not only the people who enter a vehicle have to be counted, but also those who leave the vehicle to calculate the actual occupancy rate. In an urban environment, the occupancy rate is higher in central parts of the line network compared to the outskirts. In addition, the occupancy rate changes with time of the day and thus the energy efficiency changes in the same manor. For energy calculations, an average value is usually used to represent a whole operational weekday (Figure 2.1). The occupancy rates vary with respect to the used system, time, demography and even culture [69] and can be seen as a value for vehicle capacity utilisation. The occupancy rate has two peak times during the daytime of weekdays in public passenger transport [10]. A higher utilisation in peak times reduces the specific energy demand, whereas it increases in off peak times. The occupancy rate does not only change with daytime, but also with purpose of travel [69]. This means that in typical off-peak times car occupancy rates increase when people go for shopping or leisure trips, and public transportation occupancy rates are declining in the same time slot. The high energy efficiencies of public transport systems are mainly due to high occupancy rates and many offered seats, and specific energy usage may increase dramatically in off-peak times [69]. Due to these uncertainties, a comparison of transport modes is made on the basis of kWh/seat-km. When it comes to service measurements and energy for people transportation itself, the numbers in kWh/pkm are most interesting from the energy point of view

12 2.2. Comparison of urban transport modes

45 06:00 to 09:00 35 45 09:00 to 15:00 30 55 15:00 to 18:00 35 35 18:00 to 21:00 25 20 21:00 to 06:00 15 Metro

40 Bus Day average 28 0 10 20 30 40 50 60 Occupancy rate in %

Figure 2.1: Occupancy rate for metro and bus in Stockholm by daytime on an average weekday [10]. for public transport systems [6] and enable a comparison of energy usage between various transport modes. However, in this study, the focus is on vehicle energy eﬃciency, and thus the energy usage is mainly compared in kWh/seat-km.

2.2 Comparison of urban transport modes

Relevant transport modes for comparison with PRT systems are buses, personal cars and light rail systems like trams and metros. They operate in the same urban environment and are direct competitors to personal rapid transit. Numbers for the PRT systems are displayed in Appendix A including the references. These systems were mentioned previously in Chapter 1 and include the Vectus PRT system in Suncheon, South Korea, the Ultra PRT system running at Heathrow Airport in the United Kingdom and the system of 2getthere running in Masdar city, United Arab Emirates. Those three systems are fairly similar in size and have the same face to face layout for passenger accommodation. Ultra and 2getthere have the guideway and propulsion system with an electric motor in the vehicle in common, being driven with power from an internal battery [1]. In contrast to this, Vectus uses linear induction motors along the track and gets energy via current collectors, although battery powered vehicles were considered in the design process [27]. A second difference in the guideway design is the monorail for the Vectus system, which is different to the pavement layout of 2getthere and Ultra. This includes a track that is captive of the vehicle, providing a secure and safe ride. The vehicle tare weights are similar for 2getthere (1400 kg) and Vectus (1500 kg), but Ultra is significantly lighter with 850 kg. Vectus offers the maximum payload of 1000 kg, which is nearly twice as much as the competitors [1]. Some of the technical parameters are summarized in Table 2.1. For comparison, data for buses, metros, trams and cars were taken on the basis of kWh/seat-km. Figure 2.2 shows the chosen types of vehicles, and the data for this figure is printed in the Appendix A together with the corresponding references. The data set

13 Chapter 2. Energy usage of urban transport systems

Table 2.1: Technical data for three PRT systems [1]

Parameters 2getthere Ultra Vectus Vehicle power principle battery battery current collector Drive principle electric motor to electric motor to linear motor in wheels wheels guideway Vehicle support semi-solid rubber pneumatic tyres solid polymer tyres tyres Dimensions L×W×H 3920×1460×2010 3700×1470×1800 3736×2010×2500 mm mm mm Passengers per vehicle 4 adults, 2 children 4 adults, 2 children 4 adults, 4 children/ 6 adults Weight (empty/full) 1400/2050 kg 850/1300 kg 1500/2500 kg Max. speed 40 km/h 40 km/h 70 km/h Acceleration/deceleration rates 0.8 m/s2 1.25 m/s2 1.2 m/s2 Emergency deceleration 4.7 m/s2 2.5 m/s2 5 m/s2 Max. range 60 km 20 km n.a. Minimum track radius 5.5 m 5 m 5 m Guideway design pavement pavement monorail Energy usage for full vehicle 0.19 kWh/km 0.13 kWh/km 0.24 kWh/km 0.048 kWh/seat-km 0.033 kWh/seat-km 0.040 kWh/seat-km Energy usage for empty vehicle 0.17 kWh/km 0.09 kWh/km 0.23 kWh/km 0.043 kWh/seat-km 0.023 kWh/seat-km 0.038 kWh/seat-km for buses and metros comprises data from a survey by Blomberg [10] about public transport systems in Stockholm and data from a survey by Potter [69] about UK transport services. The numbers for cars are taken from the annual report of The Society of Motor Manufacturers and Traders Limited in the United Kingdom, which refer to the energy efficiency of new cars in the years 2003 and 2013 and had to be converted from imperial miles per gallon to kWh/seat-km. As expected, the energy usage of light rail systems is very low compared to other modes of transport. This is mainly due to high efficiencies of electrical propulsion [52], low rolling resistance of steel wheels on rails and a scheduled service on dedicated guideways without disturbances. Metro and tram systems offer many seats compared to their energy usage, which improves the energy efficiency in terms of kWh/seat-km. When it comes to road traffic, the double-deck bus is one of the most efficient ways to travel. It is as energy efficient as as the UK metro, although its numbers are from 2003. Unfortunately, no newer numbers were available. The development of personal cars between 2003 and 2013 shows a 25 % decrease in specific energy usage. Nevertheless, the world average car in 2011 uses twice the energy as the UK double-deck bus and the UK metro. Compared to a single-deck bus, the double deck uses 25 % less energy per offered seat kilometre, and the same energy as the UK metro. The number for the Stockholm diesel bus is relatively high compared to the single- deck bus in the UK. This can be explained with the fact that the bus was built in 1995, although the data of energy usage was taken in 2005. Assuming the same development in the bus sector as for private cars, the energy usage should be 25 % lower if the bus was built 10 years later. The Stockholm metro is the most energy efficient rail system, but it has to be noted that the data for the UK metro involves an older system than the other vehicles that were investigated [69]. It can be seen that PRT vehicles are in the same low region in energy usage as rail systems, having values between 0.033 and 0.048 kWh/seat-km and an average of 0.041

14 2.3. Inﬂuence of occupancy rate 0.131 0.131 0.127 0.127

0.14 0.122 0.12 0.10 0.093 0.081 0.081 0.08 0.061 0.061 0.061 0.050 0.050 0.048 0.048

0.06 0.043 0.041 0.041 0.040 0.040 0.038 0.038 0.033 0.033

0.04 0.023

kWh/seat-km kWh/seat-km 0.02 0.00

UK tramUK metro

Ultra PRT (full) Vectus StockholmPRT (full) metro Ultra PRT (empty) UK single-deck bus Vectus PRT (empty) 2getthere PRT (full)UK double-deckNew bus cars New UK cars (2013) UK (2003) World Stockholmavg. car (2011) diesel bus 2getthere PRT (empty)

PRT Rail Bus Car

Figure 2.2: Energy usage in kWh/seat-km for various urban transport modes. The data is displayed in Appendix A, including the references. The comparability of energy usage is limited due to uncertainties in the related assumptions. kWh/seat-km for full vehicles. The relation to the average car in the world in the year 2011 shows that PRT systems use less than 1/3 of the energy per seat-kilometre, and half the energy of a single-deck bus.

2.3 Inﬂuence of occupancy rate

The relation introduced in Equation 2.2 indicates that the energy efficiency is strongly dependent on the occupancy rate if it is measured in kWh/pkm. For the transport modes that were introduced in Section 2.2, occupancy rates were determined from the same surveys. They are averages for the specific transport modes and include peak and off- peak times. The car occupancy rates, that were used for the PRT systems as well, are based on estimates by Potter [69]. The energy usage per passenger-kilometre can be obtained through Equation 2.2. The values are given in Appendix B together with the corresponding references. The result is displayed in Figure 2.3, and values from full PRT systems were taken as a basis. The introduction of the occupancy rate and application to the various transport modes can change the whole picture of transport efficiency. In general, it can be seen from Figure 2.3 that the energy usage per passenger-kilometre decreases if the occupancy rate is increased and vice versa. Light rail transportation systems like metros and trams are considered to be environmental friendly and able to attract passengers from road traffic [16], providing high capacity and safety for passengers. The specific energy usage in kWh/pkm is comparative low due to high occupancy rates that can exceed 50 % in peak times (Figure 2.1). The

15 Chapter 2. Energy usage of urban transport systems

Occupancy rate kWh/pkm

0.60 0.55 0.55

0.50 0.468 0.40 0.40 0.397 0.397 0.387 0.387

0.40 0.35 0.35 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.295 0.295 0.30 0.28 0.231 0.231 0.174 0.174

0.20 0.150 0.125 0.125 0.111 0.111 0.103 0.103 0.103 0.103 0.091 0.091 0.10 0.00

UK tram Ultra PRT UK metro Vectus PRT 2getthere PRT Stockholm metro UK double-deckUK single-deck busNew cars bus NewUK (2013)cars UK (2003) World avg.Stockholm car (2011) diesel bus PRT Rail Bus Car

Figure 2.3: Energy usage in kWh/pkm for various transport modes. The data for this diagram is printed in Appendix B, including the references. energy usage of rail systems is on the low end of the scale, and here the high occupancy rates enforce this effect. The UK tram improves its place in the ranking from 5th to 1st place and is thus the transport mode with the highest efficiency of 0.091 kWh/pkm. While the Stockholm metro stays on place three, the UK metro improves by two places from 6th to 4th. The differences between UK single-deck bus and the Stockholm diesel bus are even increased by the occupancy rates, which makes the Stockholm bus the least efficient in the ranking. However, the order in road traffic stays the same as when the modes are compared by kWh/seat-km because occupancy rates are all around 30 % for those modes. The effect of occupancy rate is not as positive for the PRT systems as for rail systems. They are all assumed to have the same occupancy rate of 32 % which is the same as personal cars. The Vectus system moved to place five from two, mainly because it is one of the larger PRT systems with six seats and the same occupancy rate. The 2getthere PRT system loses two places as well, falling from four to six and marking the edge to road transport. The Ultra PRT system loses only one place mainly due to its exceptionally low energy usage per seat-kilometre and is now on second place, although it has the same value as the Stockholm metro after rounding. Nevertheless, the high occupancy rates for metros improve their energy efficiency. It can be concluded that all PRT systems lost places when their high energy efficiency is connected to occupancy rates, but still they range in the high efficiency class of light rail systems. In addition, the advantages of PRT systems are not only within the energy efficiency. As mentioned in Chapter 1, they can combine this low energy usage with personal car-like travel and high average speeds, which combine the benefits of public and personal transport in one environmental friendly concept.

16 Chapter 3

Background: Resistance forces

For a car driving at a constant speed on a horizontal track, the propulsion forces and resistance forces are in an equilibrium state. The force to overcome rolling and aerodynamic resistances and even accelerate or overcome graded streets is called tractive force Ftrac and is provided by the vehicle motor through the transmission. For vehicle design, it is important to know the minimum power requirements to overcome the sum of the various resistance forces. The general equation for the traction force is given below [63, 60].

Ftrac = Frr + Fgr + Facc = FD + FR + Fgr + Facc (3.1)

The forces in question are illustrated in Figure 3.1. The air drag force FD and rolling resistance force FR are always present when the vehicle is moving and are summarised as running resistance force Frr. The gradient resistance Fgr and the acceleration resistance force Facc are only contributing to the overall resistance if the road is not ﬂat or the vehicle accelerates, respectively. In addition, the energy used to overcome acceleration and grade resistance can be regained with respect to the degree of eﬃciency, whereas air drag and rolling resistance are dissipative energies from the system.

Fgr, Facc

Figure 3.1: Resistance forces acting on a vehicle (adapted from [13])

The sum of the air and rolling resistance forces is often described as a second order polynomial with the coeﬃcients c1, c2, c3 and the vehicle speed v (Equation 3.2), which is speciﬁc for every set of vehicle and ground/track [82, 90, 34].

2 Frr = FD + FR = c1 + c2 · v + c3 · v (3.2)

17 Chapter 3. Background: Resistance forces

The c1 coefficient represents the rolling resistance to a large extent, but also includes mechanical resistances (bearings etc.) [65, 90] and tyre vibrations. c2 indicates higher- order rolling resistance and some mechanical rotating friction losses [65]. The air drag force is mainly represented by the c3 coefficient [90], but may have an influence on the c2 coefficient as well. These coefficients are needed for the energy simulation (Chapter 5) as they describe the characteristic resistance curve of the vehicle (Chapter 4) on a specific ground/track. Usually, these values can be measured as coast down coefficients in coast down tests with real test vehicles under supervised conditions [70, 51]. For a vehicle concept like SkyCab, they have to be calculated and composed by the various forces (see Section 4.3 for further details). In the further proceeding of this chapter, the resistance forces are presented in general terms.

3.1 Rolling resistance

Rolling resistance is a resistance force that is always present if the vehicle is rolling and which is responsible for approximately 20 % of the fuel consumption of a regular car [13]. This share will even increase if the vehicle speeds are low and thus the effect of air drag is small compared to rolling resistance. The reduction of rolling resistance is a substantial benefit for vehicles with low speeds [54], and thus focus is put on this aspect here. Rolling resistance is caused by numerous phenomena, of which the major cause is the repeated tyre deflection. This results in hysteresis and a force that acts against the vehicle motion [97]. It is dependent on many parameters and attributes of the tyre, suspension and ground/track. Some of the attributes for straight track are inflation pressure, tyre temperature, tyre load, vehicle speed, track unevenness, track roughness and suspension angles [37, 51]. Rolling resistance can be divided in resistances on straight track (FR,st) and in curves (FR,ct), where additional forces act on the wheel [13, 71], Equation 3.3.

FR = FR,st + FR,ct (3.3)

3.1.1 Straight track The general equation for rolling resistance on a straight track is the following, describing the rolling resistance force as a portion of the force produced by the vehicle weight and payload normal to the road surface with the dimensionless rolling resistance coeﬃcient fR,st.

FR,st = fR,st · FG = fR,st · m · g (3.4)

The rolling resistance coefficient can be obtained from different empirical equations. More than six phenomena influence this value [37] and are interrelated which makes it infeasible to be described by an analytical model. The very basic approach is to assume a constant rolling resistance coefficient, which may be suitable for speeds below 20 m/s [13, 54]. All formulas are limited in their accuracy due to the neglected phenomena [37]. Typical 1 values range from fR,st = 0.0015 for special tyres for the Shell Eco Marathon [76] over fR,st = 0.005 for tyres especially developed for electric cars [54] to an average for personal cars of fR,st = 0.008 [60, 13]. A more detailed formula for personal car tyres on concrete was developed by the Institute of Technology in Stuttgart (Equation 3.5) and takes into

1Michelin UrbanConcept tyre in the dimension of 95/80R16 at 5 bar inﬂation pressure

18 3.1. Rolling resistance

account the vehicle speed v in m/s[37]. In addition, the coeﬃcients f0 and fs are dependent on the inﬂation pressure of the tyre and can be obtained from the graph in Figure 3.2. 1609 · v 2.5 f = f + 3.24 · f · (3.5) R,st 0 s 3600 · 100

For speeds up to 20 m/s, fR,st changes little with speed, being almost linear. For higher speeds the coeﬃcient increases almost with the square of speed [37]. When the speed is increased even more, a standing wave occurs, which leads to very high rolling resistance and may cause tyre failure [97, 37].

0.020 !"!#! 0.015 !"!" $%&'( !"

#$" 0.010s %" !)!' ! #"

&" Coefficient !"# $" 0.005 !*!!+ , - ! 1.04 #!1.38 .!2.07 /!2.76 3.45+! 012345671Inflation89:--;9: pressure<= -6in> bar Figure 3.2: Rolling resistance coeﬃcients for the Stuttgart model. These values are for pneumatic tyres on a concrete pavement (adapted from [37]).

Further empirical formulas to determine the rolling resistance coefficient are shown below [65, 70, 41]. They all have a basic rolling resistance coefficient f0 and a speed dependent term with diverse modifications. The speed v is given in m/s.

1609 · v f = f · 1 + (3.6a) R,st 0 44.7

fR,st = f0 + fs · v (3.6b) 2.5 fR,st = f0 + fs · v (3.6c)

3.1.2 Flat curves The slip angle of a tyre, also called side-slip angle, is deﬁned as the angle between the direction in which the wheel points and towards which it is moving. With increasing slip angle in curves the lateral force on the wheel has a force component against the direction in which the vehicle is moving [71]. This additional force is called curve rolling resistance FR,ct [13]. The basic relation between the vehicle mass m, vehicle lateral acceleration al, front and rear slip angles βf and βr, vehicle wheelbase l and distances of wheels from the centre of gravity lf and lr and weight force FG is shown in Equation 3.7 for a ﬂat curve [63]. l l F = m · a · r · sin(β ) + f · sin(β ) = f · F (3.7) R,ct l l f l r R,ct G

19 Chapter 3. Background: Resistance forces

This equation can be simpliﬁed to Equation 3.8 with the assumptions that the centre of lf lr gravity is in the middle between the wheels l = l = 0.5 and that the slip angles on front and rear tyres are the same due to four-wheel steering or large curve radii (βf = βr) [63].

FR,ct = m · al · sin(β) (3.8)

The slip angle β can be substituted with a relation containing the tyre cornering stiﬀness C, which is a characteristic value for tyres, and the lateral force on the tyres Fl [71]. This relation contains also the lateral acceleration al and the vehicle mass m (Equation 3.9a) and is only valid for small slip angles β. It can be rearranged to the slip angle β in Equation 3.9c [71]. l F = C · β = m · a · f (3.9a) l,f f l l l F = C · β = m · a · r (3.9b) l,r r l l which can be rearranged to β for the whole vehicle: m · a β = l (3.9c) C After inserting Equation 3.9c in Equation 3.8, the following relation can be obtained. m · a F = m · a · sin l (3.10) R,ct l C

To determine the curve resistance coefficient fR,ct in correlation to the straight track rolling resistance coefficient, Equation 3.4 can be used equivalently [63]. The following definition is the result.

FR,ct FR,ct al m · al fR,ct = = = · sin (3.11) FG m · g g C

However, curves are in general superelevated and the lateral acceleration al will thus be reduced. This eﬀect is described in the following section. It has also to be noted that speeds close to zero are not considered, as the describing models are completely diﬀerent and tyre relaxation over distance is not given. This can be seen at high steering forces, e.g. during parking manoeuvres [49].

3.1.3 Superelevated curves Superelevated curves are often used to limit lateral acceleration and to increase passenger comfort. Additionally, higher speeds can be maintained throughout the curve. Concerning rolling resistance, two eﬀects of superelevated curves that inﬂuence the resistance force can be distinguished. On one hand, the force normal to the road surface increases somewhat, on the other hand the lateral acceleration decreases.

Additional normal force Due to the superelevation angle α, the centrifugal force Fc creates an additional force component to the force FG,N that the vehicle’s weight creates perpendicular to the track surface. It is assumed that all forces apply to the centre of

20 3.1. Rolling resistance gravity of the vehicle and that the curve radius R is measured to the same point. The force vectors are shown in Figure 3.3a.

al,sup

m Fc,N m || Fc

FG,N g | FG

R R FG, ||

(a) The centrifugal force Fc adds a com- (b) Lateral acceleration in superelevated ponent to the normal force of the vehicle. curves

Figure 3.3: Forces and accelerations in superelevated curves

From this ﬁgure, the resulting normal force in superelevated curves can be calculated with respect to the superelevation angle α. The following equation shows this relation.

FG,sup = m · g · cos(α) + Fc · sin(α) (3.12)

v2 Inserting the centrifugal force Fc = m· R in Equation 3.12 yields to the ﬁnal expression of the normal force. This increased normal force has to be used in Equation 3.4 to calculate the rolling resistance.

v2 F = m · g · cos(α) + m · · sin(α) (3.13) G,sup R

Reduced lateral acceleration The lateral acceleration al in flat curves is reduced in superelevated curves, which in turn has a reducing effect on the curve rolling resistance coefficient fR,ct (see Equation 3.11). In flat curves the lateral acceleration is dependent on speed and curve radius only. If the curve is superelevated and has a curve superelevation angle α, a portion of the gravitational acceleration reduces the lateral acceleration on the vehicle. This can be seen in Figure 3.3b in correlation to the forces in Figure 3.3a. The resulting equation for the lateral acceleration is given in Equation 3.14.

v2 a = − g · sin(α) (3.14) l,sup R To calculate the rolling resistance coeﬃcient in superelevated curves, Equation 3.14 has to be substituted in Equation 3.11. For the rolling resistance force, equations 3.11 and 3.13 have to be substituted into Equation 3.7.

a m · a v2 F = f ·F = l,sup ·sin l,sup · m · g · cos(α) + m · · sin(α) (3.15) R,ct R,ct,sup G,sup g C R

21 Chapter 3. Background: Resistance forces

3.2 Aerodynamic resistance

The force produced by air drag is dependent on the air drag coefficient cD, cross-sectional area of the vehicle AD, air density ρ, headwind speed vw and vehicle speed v [60]. Similar to the rolling resistance, it is always present if the vehicle is moving and has a significant quadratic speed dependent effect. (v + v )2 F = c · A · ρ · w (3.16) D D D 2 The wind direction is rather random, which not only affects the fraction of wind speed as headwind but also the drag coefficient and the relevant cross-sectional area of the vehicle [39]. The drag coefficient is typically subject to changes between 5 to 10 %, dependent on the type of car and headwind angle [37]. Typical values for the air drag coefficient of passenger cars are between cD = 0.25 and cD = 0.4 [13]. To estimate the unknown air drag coefficient for a vehicle, a comparison to a similar vehicle is recommended [54]. It has to be noted that this value is not of that high significance for the air drag force on its own, but only in combination with the cross-sectional area AD of the vehicle. Air density is dependent on pressure and temperature and will vary if the surrounding conditions change. It can be obtained from Equation 3.17 [53], where the temperature T is given in ◦C and the pressure p in bar. This effect has to be considered if different driving conditions are regarded, e.g. summer in the Emirates and winter conditions in Sweden or if changes in altitude occur. 348.7 · p ρ(p, T ) = (3.17) 273.15 + T The cross-sectional area of a vehicle can be calculated by an empirical equation if no detailed measurements are available, relating the area to a percentage of maximum width times height of the vehicle. The proportions can range from 81 % [53] to 90 % [60].

AD = 0.9 · b · h (3.18)

3.3 Acceleration resistance

The additional force to the previous resistances to overcome the vehicle inertia and thus to accelerate it, comes from Newtons 2nd law of motion and is dependent on the acceleration a and the vehicle mass m. The acceleration resistance is especially important for short distance vehicles with frequent stops and acceleration phases [6].

Facc = m · a (3.19) However, the rotating masses and their inertias have to be taken into account as they undergo rotational acceleration. This eﬀect is indicated in Equation 3.20a by using an equivalent mass me. The equivalent vehicle mass can be expressed by means of a relative factor of the vehicle mass, usually called κ. The following equations also show the relations between the equivalent mass moment of inertia Je of the rotating parts and wheel radius r [60].

Facc = me · a = (1 + κ) · m · a (3.20a) J κ = e (3.20b) m · r2

22 3.4. Gradient resistance with the various mass moments of inertia J and transmission ratios i:

2 2 2 Je = Jwheel + idiff · Jshafts + idiff · igear · Jmotor (3.20c)

3.4 Gradient resistance

The gradient resistance is generated by the vehicle weight on gradient roads and is increasing with the climbing angle γ of the road (see Figure 3.1).

Fgr = m · g · sin(γ) (3.21)

This force becomes negative as the vehicle goes downhill, and this reduces the needed force for propulsion. Potential energy can be regenerated if braking is necessary to maintain speed [53].

23 24 Chapter 4

System description

For the simulation of energy usage of a PRT system, the SkyCab1 concept will be used as a case study and is described in this chapter. Relevant parameters are pointed out and values as input for simulation are determined either from SkyCab references or from general references. Due to the conceptual state of development, some parameters are based on assumptions and have to be seen as ﬁrst estimates. The basic equations that will be needed are introduced in Chapter 3. The system characteristics are deﬁned in this chapter and the input values are summarised in Section 5.2. The SkyCab system (Figure

(a) On the guideway (b) Side view

Figure 4.1: Concept pictures of the SkyCab vehicle [88]

4.1) satisfies the PRT definition that was given in Chapter 1. It is driving autonomously (i.e. without a human driver) on its dedicated guideway, operating similar to a taxi with very short waiting times, personal travel and non-stop travel to the desired destination [87]. To be competitive to existing transport modes, SkyCab aims at a high reliability of 99.8 % of all trips to take place without disturbances [67]. The system is designed with five goals in mind that were defined by the SkyCab developer to meet customer requirements [87]:

1www.skycab.se

25 Chapter 4. System description

1. Attractiveness for the passengers

2. Availability to all passengers

3. Safety and security (technical and for passengers)

4. Environmental friendliness and energy eﬃciency

5. Cost eﬃciency, also in a life cycle perspective

Although environmental friendliness and energy eﬃciency of the SkyCab system is in focus of this thesis, it can be seen that there are other aspects that have to be considered for an overall successful concept.

4.1 Technical description of the SkyCab system

The vehicle consists of two main parts, the bogie running inside the captive guideway and the passenger cabin which is mounted on top of the bogie and visible from the out- side. The bogie contains all necessary technical systems such as the complete propulsion components, the batteries and the steering system [67]. The passenger cabin can accommodate four people sitting face to face above the wheels. Two seats can be folded to enable travelling for one person in a wheelchair with a companion or for other goods passengers need to carry along [67]. The tare weight is deﬁned at 1000 kg with additional 400 kg of payload [85]. Various drive train layouts were investigated and a proposition was made by Fernández [32], but the ﬁnal decision for this concept is still pending. The vehicle layouts in question are shown in Figure 4.2, where 4.2a represents the proposal by Fernández [32], 4.2b is a two-motor layout and 4.2c shows an advanced hub motor concept.

(a) 2WD with one motor (b) 4WD with two motors (c) 4WD with four hub mo- and one transmission and two transmissions tors and no transmissions

Figure 4.2: Vehicle layouts for the SkyCab concept [32]

A four-wheel drive concept is favourable for regenerative braking, which should be the normal braking mode [85]. Although there are disadvantages concerning weight and rotational masses of a four-wheel drive train, more energy can be restored due to more traction and a better brake distribution. In addition, as deceleration is usually stronger than acceleration, a more powerful motor is able to provide higher braking power and thus can convert more energy to charge the batteries [82]. For further investigation, the concept from Figure 4.2b is considered, assuming that the sum of power is the same for all concepts.

26 4.1. Technical description of the SkyCab system

For urban manoeuvrability, small cornering radii are desired [7], which can be achieved by a four-wheel steering system. Regarding energy efficiency, four-wheel steering reduces the slip angles of the tyres and the cornering resistance. Better stability at higher speeds can be achieved with the same technology [37]. A minimum head time between vehicles of 1.6 seconds at 10 m/s enables emergency brakes and prevents crashes. The energy for vehicle operation is stored in batteries in the bogie which are charged at station stops or during idling times. The state of charge is supervised by a control system to ensure that no stops occur during operation [67]. Using an electric drive is one of the steps towards higher efficiency and lower GHG emissions because it is easy to use environmental friendly resources and to adapt to new technologies of energy production without system redesign [6]. For safety reasons, the vehicles have disc brakes on all four wheels for emergency braking in redundancy of the electrodynamic motor brake. They can brake with regenerative braking or disc braking alone or in blended mode with both braking systems in action. This same redundancy with two motors ensures propulsion and a safe return to the maintenance facility in case of system failure. The track contributes to the passive safety of the system with a captive design which secures the vehicle from falling off the track [67, 85]. The chassis of SkyCab is very similar to a personal electric car and has a typical suspension with springs and dampers. The tyres, however, are not yet specified. For energy usage, energy to overcome rolling resistance is expected to play a major role as speeds are low and air drag is speed dependent by the power of two (compare Chapter 3). The wheels are limited to a diameter of maximum 400 mm due to a low profile guideway structure [85], which makes it complicated to use numbers for the rolling resistance coefficient from typical car tyres. Solid rubber tyres would be interesting from the maintenance point of view, but the rolling resistance and the comfort is worse compared to pneumatic tyres [79]. The same applies for metal wheels with a slender rubber perimeter. The dissipation of energy by deflection of the tyre material is higher with solid rubber tyres than with pneumatic tyres and thus the rolling losses are higher. Further investigation and the approach towards a rolling resistance coefficient is given in Section 4.3. The electric motor was chosen by Fernández [32] with a total propulsion power for one motor of 32 kW at peak and a continuous power of 13 kW. The tractive force at the wheels, which is needed as an input for the simulation, can be calculated with respect to the overall transmission ratio i = 13 and the wheel radius r = 0.2 m defined by Fernández [32]. The result of tractive force as a function of vehicle speed is given in Figure 4.3. For the braking characteristics of the motor, the tractive curve is mirrored as a first estimate to get negative traction force. This is based on the assumption that the braking and propulsion force curves are the same except for signs. The components of the vehicle were shown previously in the energy architecture in Figure 1.10. All values for energy efficiency are only estimates and are displayed in Table 4.1 with the references where they originate. The overall efficiency can be determined by multiplication of the component efficiencies and yields approximately 81 % from battery to wheel. The number for battery efficiency relies on various parameters and represent only a rough estimate [26]. The calculated overall efficiency is in correlation with Helms [45] (81.2 %) when the charging process is not considered.

27 Chapter 4. System description

10 9 Speed Tractive force 8 in km/h in kN 7 6 0 8.8 5 15 8.8 18 6.0 4 36 3.0 in kN Force 3 54 2.2 2 72 1.6 1 80 0.0 0 0 20 40 60 80 100 Speed in km/h

Figure 4.3: Tractive force at wheels as function of vehicle speed

Table 4.1: Component eﬃciencies with references and combined overall eﬃciency

Component Eﬃciency Applicability for propulsion (p) Reference and braking (b) Transmission 97 % p, b [53] Motor 96 % p, b [32] Controller 97 % p [32] Converter 97 % b [32] Battery 90 % p, b [77] Grid 97 % out of system boundary [86] Total for propulsion 81 % Total for braking 81 %

4.2 Track layout and speed proﬁle

The SkyCab track is around 1.7 m wide and four to five metres above the ground [67]. On/Off points are on the same level as the track which makes it more energy efficient for the vehicles to stop and reduces energy for braking and accelerating [87]. The guideway is captive of the vehicles with an enclosing design that prevents snow, leaves and other things to fall on the traction path. The lane on which the tyres run is made of asphalt or concrete with a very smooth surface. For cold conditions, a heating system in the guideway may be possible, but is optional [7]. When vehicles need to switch lanes, no mechanical switches like for railway applications are used. There are no moving parts in the guideway, but the vehicle is steering itself and switching lanes autonomously, using sensors to determine its lateral position. The overall workload among the vehicles should be equally shared among travel with passengers, waiting for passengers at stations or at maintenance facilities and transit to stations where there is a need for vehicles [7]. The design speeds and corresponding curve radii are 10 m/s at 30 m, 18 m/s at 90

28 4.2. Track layout and speed profile m and 25 m/s at 160 m [84] (Table 4.2). In case of emergency, a deceleration of 6 to 9 m/s2 is possible [84]. During operation, braking and accelerating takes place on dedicated lanes in front of and after on/off points, respectively. This ensures that the bypassing traffic on the main line is not disturbed and that full deceleration and acceleration rates are utilized [87]. The track layout and speeds have to correlate in order to limit lateral accelerations to 1 m/s2, which is a comfortable level for the passengers in curves. In addition, to maintain higher speeds, curves are superelevated. In Table 4.2, the possible curve layouts with related speeds and superelevation angles for a lateral acceleration of 1 m/s2 are displayed. The case used for simulation purposes in urban-only areas is curve 1 with a limit to 10 m/s vehicle speed, which is also the reference case in the present study [84].

Table 4.2: Curve deﬁnition: Curve radii, design speed and superelevation angles

Curve type Curve radius in m Design speed in m/s Superelevation angle αsup in degree Curve 1 30 10 13.8 Curve 2 90 18 15.4 Curve 3 160 25 23.6

The reference simulation track has no gradients and has a track length of 2 km, which is an estimate from the track layout in Figure 4.4. The simulation software, which is used in Chapter 5, is not capable of calculating explicitly a curved track layout. For this reason, the curve rolling resistance has to be estimated with respect to the share of curves of the total track length. This can be done by investigating the track layout from a study of SkyCab in the enlarged Arlanda region, which is representative for a SkyCab application [32, 83] (Figure 4.4). Vision SkyCab i Sigtuna kommun

2 km

FigureFigur 4.4:13. Åkriktningari Track detutvidgade layout of bannätet the enlarged Arlanda region

From these references it is possible to estimate2 the share of curves of the total track length in this surrounding to 6 % roughly (see Appendix C). This amount of curve res-

2This includes assumptions of trip quantity, curve quantity, trip length, curve length and consist only of curves of type 1.

Sid. 34 Chapter 4. System description istance force will be added to the resistance curve in Section 4.3. The speed proﬁle will include acceleration at start, cruising speed of 10 m/s and full deceleration at station approach.

4.3 Running resistance diagram

The characteristic running resistance force Frr = FR +FD is a sum of different components that can be described by a second-order polynomial (see Chapter 3 for details). Gradient and acceleration resistances are not added in this polynomial, since they are dependent on the operational state and track layout. In this section, the various coefficients are determined and the running resistance curve is generated, followed by a curve fit to calculate the coefficients of the polynomial that fits the curve. The rolling resistance is expected to be the largest contributor due to low speeds. As described in Chapter 3, it is composed of the straight track rolling resistance FR,st and curve rolling resistance FR,ct. The methodology to determine the rolling resistance for the SkyCab case is displayed in Figure 4.5.

fR,st on straight track

FR,st on straight track

Effects of superelevated curves: FR,ct

Determine track layout and share of curves

FR = FR,st + 0.06 FR,ct

Figure 4.5: Methodology to determine average rolling resistance including curves and superelevation for the SkyCab vehicle

The rolling resistance on straight track is generally calculated by Equation 3.4, using the speed dependent rolling resistance coefficient fR,st from equation 3.5. To include the change of tyre diameter from a typical passenger car tyre to the SkyCab specific tyre with a diameter of 400 mm, data is needed to determine the rolling resistance coefficient of this smaller tyre. Due to a deficit of information in the literature and the secrecy of the tyre manufacturers about their measurements, a publication on the effect of tyre dimensions on rolling resistance [68] has been used. This basis is limited due to the validation only down to a 13 inch wheel, but the derived relation gives at least an indication of rolling resistance change. The relation between rolling resistance force FR,st and tyre radius r is given in Equation 4.1, using a constant k that accounts for constant

30 4.3. Running resistance diagram

tyre inﬂation pressure, same viscoelasticity of the material, constant load on one tyre Ft and small deﬂections, which are assumed to be the same for similar constructed tyres [68]. The following equations are for one tyre under the load of 1/4th of the vehicles weight.

k F = (4.1a) R,st 2 · r1/3 k fR,st = 1/3 (4.1b) 2 · r · Ft To derive an equation that accounts for both tyre radius and vehicle speed, Equations 4.1b and 3.5 have to be combined. From Pillai [68], tyre data for a 185/80R13 passenger car tyre is available, measured as per SAE procedure J1269 on a dynamometer wheel. The constant k can be calculated from Equation 4.1, the data is displayed in Table 4.3.

Table 4.3: Tyre data for a 185/80R13 passenger car tyre as reference and calculated SkyCab values

Parameter 185/80 R13 [68] SkyCab Radius r 0.3131 m 0.2 m Speed at point of measurement v 80 km/h 80 km/h Tyre load Ft 4448 N 3500 N Tyre pressure pt 2.07 bar 2.07 bar Constant k 70.62 Nm1/3 49.61 Nm1/3 Resistance force FR,st 52 N 42.42 N Rolling resistance coeﬃcient fR,st at 80 km/h 0.0117 0.01212

For the application in question, the change in tyre load between reference and SkyCab from 4448 N to 3500 N has to be accounted. This is approximately equivalent to a load of 350 kg per tyre, which is 1/4th of the total weight. A relation from Michelin is used (Equation 4.2) [89] and the results are displayed in Table 4.3 as well. The tyre pressure is assumed to be constant and the powers are for average passenger car tyres.

−0.4 0.85 p Ft FR,st = FR,st,0 · · (4.2) p0 Ft,0 At this specific point, where the rolling resistance coefficient is determined as a function of pressure, speed, tyre design and load, the radius dependent (Equation 4.1b) and the speed dependent (Equation 3.5) rolling resistance coefficients are set equal as they should give the same value at the same speed of 80 km/h. That yields to the following expression:

k 1609 · v 2.5 1/3 = f0 + 3.24 · fs · (4.3) 2 · r · Ft 3600 · 100

This relation contains two unknown coefficients, f0 and fs. To solve the equation, one of the two coefficients has to be assumed because a second set of data at a different speed for the exact same tyre is missing. The speed dependent term is very small at low speeds compared to the basic coefficient, which will give the major direction of the final rolling

31 Chapter 4. System description

resistance coeﬃcient. For this reason, fs is assumed from Figure 3.2 to fs = 0.005 for a tyre pressure of 2.07 bar. Rearranging Equation 4.3 to f0 gives Equation 4.4. k 1609 · v 2.5 f0 = 1/3 − 3.24 · fs · (4.4) 2 · r · Ft 3600 · 100

For a tyre radius r = 0.2 m, the basic rolling resistance coeﬃcient is calculated to f0 = 0.01207 at a tyre load of 3500 N from the data set. Using the calculated f0 and the assumed fs in Equation 3.5 yields the following expression for the rolling resistance on straight track of a tyre with 200 mm radius and at 3500 N load, which is pneumatic and similar constructed as the measured radial tyre. 1609 · v 2.5 f = 0.01207 + 3.24 · 0.005 · (4.5) R,st 3600 · 100

The rolling resistance coefficient is in the region of personal cars (fR,st = 0.008) [60, 13], but somewhat higher due to the smaller radius of the wheels. If special energy efficient tyres are used, a reduction may be achieved to get closer to the numbers of special tyres for electric cars of fR,st = 0.005 [54]. Small electric cars can be equipped with energy efficient tyres made by Continental, called Conti.eContact. The smallest tyre size that is available is in the dimension 125/80 R13, which is categorized in rolling resistance according to the EU tyre label in category E after regulation Number 1222/2009 [28]. This means that the rolling resistance coefficient ranges between 0.0091 and 0.0105, measured at 80 km/h according to UNECE Regulation No 117 annex 6 [29]. The higher coefficient of the calculated tyre can be justified by the smaller tyre radius and thus gives a basis for the proposed rolling resistance coefficient in Equation 4.5. A compensation in the same way for the rolling resistance in curves is neglected due to superelevated curves and the application limitation to straight track of the used equations. The curve rolling resistance is calculated using Equation 3.15 for superelevated curves, 2 using the defined maximum lateral acceleration for superelevated curves al,sup = 1 m/s [84], vehicle mass including payload of m = 1400 kg, cornering stiffness of C = 1400 N/deg for a pneumatic tyre [43], superelevation angle of α = 13.8 ◦ [84], speed of v = 10 m/s and curve radius at that speed of R = 30 m [84]. The vehicle mass of 1400 kg is the maximum and indicates a conservative assumption. Although the cornering stiffness is for a 175/65R15 tyre, it is assumed that its change with tyre diameter is negligible. The result of this calculation is an additional rolling resistance in curves of FR,ct = 25.7 N. Using the determined track layout and the derived share of type one curves of approximately 6 %, the relation from Equation 4.6 can be obtained to include the additional curve resistance. This generalized approach for curve resistance is inevitable due to the fact that the simulation software regards explicitly straight tracks only.

FR = FR,st + 0.06 · FR,ct (4.6) The aerodynamic resistance is calculated with Equation 3.16, but neglecting wind inﬂu- ence due to the related uncertainties of wind speed and direction. The value for cD is set to 0.33 [85], which is in good correlation with typical passenger cars (Figure 4.6), where the mean value in 2010 was at cD = 0.326. The cross-sectional 2 area AD is set to 2.89 m [85], which is in approximate agreement with the empirical estimation of Equation 3.18 at vehicle height and width of each 1700 mm. In addition to the previous parameters, the air density ρ is assumed to be at 1.25 kg/m3.

32 4.3. Running resistance diagram

> 0.40 0.40 0.39 mean value 0.38 cD = 0.326 0.37 0.36

0.35 D c 0.34 0.33 0.32 0.31 0.30 0.29 0.28 0.27 0 10 20 30 40 50 60 Quantity

Figure 4.6: Air drag coeﬃcients: 412 cars compared and the cD quantity in the year 2010. The average value is cD = 0.326 (adapted from [13])

From the stated values in this section, the following set of resistance forces have been calculated using equations from Chapter 3. The single components are summed up to the total resistance force for the SkyCab vehicle. Acceleration and grade resistances are not included here as they are dependent on the speed proﬁle and track layout. The curve resistance seems to be speed independent, but it is dependent on the lateral acceleration which in turn depends on the curve speed and road superelevation angle (compare Equation 3.14). In the SkyCab case, this angle is adopted to the corresponding speeds to achieve a maximum and constant lateral acceleration of 1 m/s2, which gives a constant resistance force. The components of the running resistance force are listed in Table 4.4. A detailed summary of the used numbers is given in Table 5.1 for the reference calculation.

Table 4.4: Resistance forces and total running resistance of SkyCab vehicle

v in m/s Frr in N FD in N FR,st in N 0.06·FR,ct in N 0 119.16 0.00 118.39 1.54 1 119.75 0.60 118.39 1.54 2 121.54 2.38 118.39 1.54 3 124.53 5.36 118.39 1.54 4 128.70 9.54 118.39 1.54 5 134.07 14.90 118.40 1.54 6 140.64 21.46 118.41 1.54 7 148.39 29.21 118.41 1.54 8 157.34 38.15 118.43 1.54 9 167.49 48.28 118.44 1.54 10 178.83 59.61 118.45 1.54

33 Chapter 4. System description

When this data is plotted over speed, the typical running resistance diagram is obtained. It is shown in Figure 4.7. As expected, the rolling resistance force is the major component of the total resistance force, and the speed dependency is comparably low. The coeﬃcients c1, c2 and c3 of the running resistance curve are taken from a curve ﬁt with a second-order polynomial, giving c1 = 119.98 N, c2 = -0.002 Ns/m and c3 = 0.597 Ns2/m2.

200 ! ! 2 FD +Ftotal&=&119.978&-&0.002&0&v&&+&0.597&0&v²&FR = 119.978 - 0.002 v + 0.597 & v 180 FFtotalD + [N]FR FFD [N] 160 D FFR,stR,st [N] ! 140 0.06FR,ct [N]F R,ct 120 100 80 60 40 Resistance force in force N Resistance 20 0 0 1 2 3 4 5 6 7 8 9 10 11 Vehicle speed in m/s

Figure 4.7: Running resistance curve for SkyCab

These values are for the reference calculation, which means for a vehicle without passengers and a weight of 1000 kg. The procedure of calculating the total resistance forces and conducting a curve fit with a second-order polynomial was validated by calculations with real cars, whose coefficients were measured by real world coast down tests by [65] and compared to the calculated values. These validations made clear that a speed dependent rolling resistance coefficient is needed to give a sufficient fit of the model to the real system at higher speeds, although it may not be necessary at vehicle speeds considered in this thesis. Comparisons of curves from calculations and measurements are given in Appendix D, and for low speeds the curve fits the measured values sufficiently. For acceleration resistance, the vehicle mass and the moments of inertia of the power train need to be specified. Due to the concept character of SkyCab, an relative mass factor for electric vehicles of κ = 0.05 is assumed [54].

34 4.4. Operational conditions and auxiliary power

μ 1.2 0.8 !" #$" ' !!""# %&" ()*+,-" 1.0 $# #"0.6!" $" 0.8 %" &" $" 0.4#$ 0.6 Dry tarmac Wet tarmac !" #" 0.4 Loose gravel 0.2!"

Loose snow Friction coefficient 0.2 Ice % % &% '% (% $% )% "% *% 0 16 32 48 64 80 96 0 % slip in % !"##$ %&"'( 20 40 s 60 80 100 Speed in km/h !"#$% &'()% *+","-.%/0123"1-4%56%5%78 - 9 : " 0 - % 07% 6;11,%0-%<5="086%68>5316(% (a) Friction coeﬃcient over wheel slip for (b) Friction coeﬃcient over speed (adopted various surface materials [49] from [37]

Figure 4.8: Friction coeﬃcient µ on diﬀerent surfaces and conditions for a typical passenger car tyre

The available adhesion coefficient has to be determined as input value for the simulation software and changes with surface material, road conditions and speed and has to be defined for the simulation at zero and respectively maximum speeds. Figure 4.8 shows the friction coefficient with relation to wheel slip and speed. As the vehicle is controlled with sensors and has an electrical drive, an utilisation close to the maximum can be assumed. For the SkyCab vehicle, a friction coefficient of pneumatic rubber tyres on asphalt/concrete is assumed to be 1.0 for v = 0 and 0.8 for the vehicle’s top speed (compare Figures 4.8a and 4.8b).

4.4 Operational conditions and auxiliary power

The SkyCab system should be operational under various conditions, covering snow, ice, rain, wind and dust [85]. These conditions vary significantly with place of service and season and influence the energy usage. For this reason, three possible application regions are considered: Stockholm, Delhi and Beijing. They are different in their climate conditions and may offer a market for PRT systems. In the following paragraphs, the general influencing factors on energy usage are described. The variation of weather has impacts on various parameters of the system, ranging from temperature over wind speed to precipitation.

Influence of temperature The ambient temperature has a large variety of impacts on the vehicle energy usage. Air density changes with air temperature and pressure (Equation 3.17) and thus influences the air drag force FD directly (Equation 3.16). For instance, a change in temperature from 20 ◦C to -7 ◦C increases the air density and thus the air drag force by approximately 10 %. The rolling resistance of a cold tyre can increase up to 20 % compared to a tyre at optimum temperature (Figure 4.9) [37]. This effect is particularly present at low temperature surroundings, when the optimum tyre temperature is never or rarely reached. Especially for a PRT system like SkyCab this effect is significant, as average trips are short and usually followed by idling and cool down of the vehicles and their tyres. For the

35 Chapter 4. System description simulation in this report, the optimum conditions and working temperature of the tyre are assumed.

55 120 !"" Temperature!"#$"%&'()"* rise+,-"* !" C

115 44 !" ° #" $" .*#" #" %"/* 0* 3* #" 110 3312* $"%" !"0* 45* !" 22 !#" 105 %" $" 6*&" !"$"7* 0* 100 Tyre resistance8,)"* 9)&* 11 &

%" Temperature in rise Relative tyre resistance in % tyre resistance Relative 32 64 96 128 160 192 :;<"-*+(=*&>*Run at maintained:&;='&;="?*@=AB&';C=* inflation in km

Figure 4.9: Tyre temperature and related tyre resistance as function of travelled distance (adapted from [37])

The HVAC unit has a major impact on energy usage under diﬀerent temperatures. Depending on the surrounding temperature, either heating or cooling is necessary to as- sure passenger comfort. The maximum power for the HVAC unit is estimated to be between 3000 and 5000 W with fans for ventilation included [13]. The auxiliary power of a small electric passenger car (Nissan Leaf) in the United States was measured by data loggers by the company FleetCarma and set in relation to the ambient temperature [2] (see Appendix E). This measurement gives a relation between needed auxiliary power and the operational temperature, which is assumed to be in the same region as the SkyCab vehicle. The auxiliary power includes the cabin heater (and AC) and fan, headlights, power steering, information system and component heaters for battery to improve eﬃ- ciency. The polynomial for the auxiliary power that was derived from the given data is given in Equation 4.7, where the temperature is given in ◦C and the auxiliary power in W.

2 Paux = 1721 − 63.6 · T − 1.3 · T (4.7) The chosen cities have very different average temperatures over the year and thus the needed comfort energy for HVAC will differ significantly. The variation of related auxiliary power calculated with Equation 4.7 is visualized in Figure 4.10. The change in HVAC power utilisation over temperature and time of the year is clearly visible, indicating a minimum at approximately 20 ◦C and around the summer times in Stockholm. Although heating and cooling power level out over the year, the auxiliary power can reach double the value in some months compared to the summer months. The average temperatures in the cities can be calculated form the data in Appendix F, which will then lead to the related average auxiliary power for this average temperature. The results are given in Table 4.5, and efficiencies do not have to be applied as the values are measured and the efficiencies are included. The total auxiliary load for all simulations will be estimated to 1.08 kW, which is an average of the investigated cities. The influence of

36 4.4. Operational conditions and auxiliary power

6000 5000 cooling 4000 heating 3000 2000

Auxiliary powerAuxiliary in W 1000 0 -40 -20 0 20 40 60 Temperature in °C (a) Power demand over temperature estimated with Equation 4.7

40 Stockholm Delhi 35 Beijing C

° 30 Average 25 20 15 10 5

Temperature in 0 -5 -10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month (b) Temperature variation for diﬀerent cities, based on monthly average temperatures stated in Appendix F.

Figure 4.10: Auxiliary power over temperatures and monthly temperatures auxiliary power on the overall energy consumption will be investigated in the simulations during the parameter variations.

Influence of precipitation and wind speed Precipitation is considered to have minor impact on the vehicle energy consumption. The additional energy for de-icing or snow clearing is attributed to infrastructure energy and is not considered in this thesis. Never- theless, an important aspect is the change of adhesion coefficient with changing pavement conditions. Wet asphalt has roughly a 20 % lower adhesion coefficient than the dry equivalent (see Figure 4.8) and thus less propulsion and braking forces can be transmitted. Under icy conditions, the situation is even worse, reducing the adhesion coefficient to a tenth of the dry condition value of about µ = 0.1. The situation of water on the pavement is not considered to contribute to rolling resistance by displacement of water, because a drainage systems is assumed. Wind speed directly influences the air drag force FD by the power of two (see Equation 3.16). As the actual conditions are completely unclear, no headwind in the investigated

37 Chapter 4. System description

Table 4.5: Auxiliary power of the SkyCab vehicle for considered cities

City Avg. temperature in ◦C Stockholm 6.63 Delhi 25.17 Beijing 12.08 Average 14.63 Related Paux in W 1077 areas will be assumed.

Influence of occupancy rate For large vehicles such as trains, the energy usage is nearly independent of the number of passengers due to the huge differences in train and passenger weight. The increase between empty and full trains is between 3 % and 5 % [6], which is usually neglected. In the case of very small and light vehicles, this effect needs to be considered more thoroughly. Considering a tare weight of 1000 kg, an additional payload of 400 kg for the SkyCab vehicle represents over 28 % of the total weight. This effect has to be accounted by adapting the weight if the occupancy rate is changed, although the energy usage per passenger-kilometre will have an effect in the opposite direction.

Additional energy usage besides the vehicle Energy that is needed for the infrastructure and IT systems will not be included in the simulation, but is mentioned here for the sake of completeness. The main consumers besides the vehicle are the communication systems, control room, maintenance facilities, stations and a possible heating option of the guideway for winter operation. Assuming wireless communication like the Vectus PRT system in Suncheon, South Korea, the radio boxes along the track use approximately 15 W each, adding to the control room power of 1.5 kW [27]. Power usage for maintenance facilities is specified with 28 kW for Vectus, which may be an indicator for the SkyCab system. The Vectus system has two station designs, differing in size. The small station is most similar to the SkyCab on/off points, and the power need is roughly 2 kW [27]. Under heavy winter conditions, the covered and snow protective design of the track is assumed to give an important saving in energy usage. The power for heating the track is assumed to be around 250 W per m2 of track, compared to 750 W per m2 of track for the Morgantown people mover PRT without covered guideways [40].

38 Chapter 5

Simulation of energy usage

In the following chapter the simulation software is introduced and the basic procedure of energy calculation is explained. Further on, the reference calculation and the varied parameters are presented, followed by the detailed results of energy usage in kWh/seat- kilometre in Section 5.3. The calculated energy usage and the eﬀects of the parameter variation are discussed in Section 5.4. Finally, the related GHG emissions are calculated on basis of the obtained energy usage.

5.1 Simulation software

The following information is mainly taken from documents by Johan Öberg [64] and comments along the code, which contain a rudimentary documentation of the STEC simulation software. STEC is an acronym for "Simulation of Train Energy Consumption" and was developed by MiW Rail Technology AB on behalf of KTH Royal Institute of Technology in Stockholm. The version that is used for this thesis is 2.10b and the software has been validated for various trains [82]. It was initially developed for trains, but as it is a flexible and highly customizable tool, it can be adopted to multiple purposes. The user interface is given in Microsoft Excel, which runs on all computers equipped with Microsoft Excel 1997 and later. The data processing and calculation is made invisible for the user and programmed as Visual Basic for Applications in Excel. It contains multiple main and sub modules that are called when the user requires certain calculations or outputs. The used energy is calculated for each distance step and than accumulated to the total energy usage. The software returns a speed profile of the vehicle, which can be compared to the target line speed that was set by the user, and forces, powers and energies over time or distance. The model can handle regenerative braking and coasting with customizable factors as well as traction limits due to available adhesion coefficients. Some modifications were made to the code to reduce round-off errors and thereby increase the accuracy compared to large numbers of train calculations. The simulation process is divided in three parts: the pre-processing, calculations and post-processing. During preprocessing, the user has to give all values that define the vehicle and the track that should be used for simulation. Those numbers were determined in Chapter 4 for SkyCab and are unique for every system. The values are given in Table 5.2 and need to be entered in the first sheets of the Excel file. The track can be designed with respect to gradients, line speeds and stops, but curves can not be defined explicitly. For this reason the curve resistance is implemented in the rolling resistance for straight

39 Chapter 5. Simulation of energy usage

track (c1 coefficient), as done in Section 4.3. The propulsion and regenerative braking characteristics of the motor are implemented by traction and braking curves, respectively, as defined in Figure 4.3. Intermediate values are calculated by the software from the given points via linear interpolation. The system efficiencies for the vehicle, feeding systems and auxiliary power can be defined separately and were determined in the previous chapter. The calculation process is initiated and customized by the user with pop-up windows that require additional input. The calculation process is described in Figure 5.1.

Discretise track in segments dx

Check allowed line speed vs. vehicle top speed in each segment

Determine state of motion - accelerate by traction - keep speed by traction/braking - brake to slow down - optional: coasting

Calculate traction force

Calculate running resistance

Calculate acceleration or retardation with respect to - adhesion - line speed in next segment - vehicle top speed - braking/tractive capacity

Step back and recalculate numerically if speed is larger than allowed (per segment)

Stop at any defined stations within 1 m accuracy

Figure 5.1: Calculation process of STEC

Any stop at a desired position is iteratively calculated using the track discretisation as a basis. The brake curves are interpolated to ﬁnd a stop position within ± 1 m from the desired position. If a track segment is too long, it will be further discretised to match the station position within the indicated accuracy. The software is capable of considering the adhesion coeﬃcients that may limit the traction or braking force. These values are

40 5.1. Simulation software also modified at input to fit to the desired maximum acceleration, which means that the adhesion is used to limit the maximum tractive force. For user convenience, it is possible to import and export defined vehicle and track data for simulation of various scenarios. When the simulation has run, a post processing is started to process the generated data and to visualize the results. This includes the traction and resistance forces, utilized adhesions, speed profile (actual speed and target speed), acceleration and accumulated energy. The energy usage is split in different parts, containing the gross energy, regenerated energy, net energy, potential energy due to gradients, energy to overcome rolling and aerodynamic resistances, auxiliary energy and energy that is dissipated by mechanical braking if applicable. In the present simulations, only electrodynamic braking is investigated as this should be the operational mode, and no gradients are considered. The output of accumulated energy usage is given in kWh, kWh/km, kWh/seat-km and kWh/pkm and a comparison between the effects of rolling resistance, aerodynamic resistance and auxiliary power can be made. Two key indicators are the energy usage per seat kilometre (kWh/seat-km) and the total trip time. The comparability of the energy usage by this unit was shown and demonstrated in Chapter 2, and it is calculated from the net energy for the total trip and the specified number of seats and the trip length. The net energy can be calculated by subtracting the regenerated energy. It is the energy that has to be transferred into the system by charging the battery at station stops and is consumed by dissipative resistances such as rolling resistance, air drag and auxiliary powers as well as energy for mechanical braking if applicable. The sum of these energies gives the net energy, and the energy split is visualized in Figure 5.2.

Regenerated energy

Rolling resistance energy

Auxiliary energy Net energy Net energy Gross energy

Aerodynamic resistance energy

Figure 5.2: Sankey diagram of the energy split for the reference calculation

The split of the net energy in the sub energies is made on basis of the forces that are calculated by the running resistance polynomial coeﬃcients. The share of rolling resistance is calculated from the c1 coeﬃcient which determines the rolling resistance force (see Chapter 4.3). The rolling resistance energy is calculated with Equation 5.1 per segment and than accumulated over all segments of the trip distance. 1 Z E = · F · dx (5.1) R η R

The energy to overcome air drag is calculated from the forces indicated by the c2 and c3 coefficients from the running resistance polynomial. The speed dependency of the rolling resistance, which influences these coefficients as well, is here negligible. The forces, powers and energies are calculated per segment as indicated in Figure 5.1 and the energy

41 Chapter 5. Simulation of energy usage is accumulated over the total number of intervals. The basic relations are displayed in the following set of equations: dx P = F · v = F · (5.2a) dt Z Z E = P · dt = F · dx (5.2b)

The auxiliary energy is calculated as a constant over all intervals. It is obtained by multiplication of the auxiliary power by the total trip time to calculate the total energy used.

Eaux = Paux · ttotal (5.3)

For all calculations, the forces (and speeds in the same way) are averaged over each interval. F (x) + F (x + dx) F ∗ = (5.4) 2

5.2 Reference simulation and parameter study

The reference simulation is used as a basis to which all further calculations are compared. The summarised values in the following tables were determined in Chapter 4 or external references. For the simulation software, the c1, c2 and c3 coeﬃcients have to be determined according to the procedure described in Section 4.3. The input values to this calculation are summarised in Table 5.1, including the resulting values.

Table 5.1: Summary of reference vehicle variables for simulation

Parameter Notation Value Unit Reference Gravity constant g 9.81 m/s2 Basic rolling resistance f0 0.012 07 1 Chapter 4.3 Speed dep. rolling res. fs 0.005 1 Chapter 4.3 Cornering stiﬀness C 1400 N/deg [43] 2 Cross sectional area AD 2.89 m [85] Air drag coeﬃcient cD 0.33 1 [85] Air density ρ 1.25 kg/m3 Chapter 4.3 Vehicle mass m 1000 kg [85] 2 Lateral acceleration al,sup 1 m/s [85]

Coefficient 1 c1 119.98 N Coefficient 2 c2 −0.002 N · s/m 2 2 Coefficient 3 c3 0.597 N · s /m

All input values for the reference simulation are summarised in Table 5.2. The acceleration and retardation rates are limited to 2.5 m/s2 for passenger comfort, which is approximately made by limiting the adhesion coefficient to 0.255. This procedure is necessary because the software is not yet capable of implementing limited acceleration and deceleration rates. The efficiency of the auxiliary systems is set to 100 % because the value is taken from measurements where the efficiency is already included. In addition to

42 5.3. Results of reference simulation and its variations the listed parameters, the traction and braking curves according to Figure 4.3 have to be entered.

Table 5.2: Input values to the simulation software

Parameter Notation Value Unit Reference

Coefficient 1 c1 119.98 N Section 4.3 Coefficient 2 c2 −0.002 N · s/m Section 4.3 2 Coefficient 3 c3 0.597 N · s /m Section 4.3 Adhesion coefficient (v = 0) µ0 0.255 1 Section 4.4 Adhesion coefficient (v = vmax) µs 0.255 1 Section 4.4 Auxiliary power Paux 1080 W Section 4.4 Rotating mass coefficient κ 0.05 1 [54] Track length s 2 km Chapter 4 Occupancy rate cocc 0 % Max. operational speed v 10 m/s [85] Vehicle mass m 1000 kg [85] Number of seats n 4 seats [85] Propulsion/ braking efficiency ηtrac, ηregen 81 % Table 4.1 Auxiliary systems efficiency ηaux 100 % Discretisation dx 1 m

To determine the parameters that inﬂuence the energy usage of the vehicle, seven important ones are varied and 16 diﬀerent variations from the reference calculation were conducted. They are usually altered in a range of ± 15 %, but sometimes even more. The ranges of variation are given in Table 5.3.

Table 5.3: Varied parameters and their values for the simulation

Parameter Notation Unit Range Values

Basic rolling resistance f0 1 ± 15 % 0.01388; 0.01026 Auxiliary power Paux W ± 15 % 1242; 918 2 Cross sectional area AD m ± 15 % 2.457; 3.324 Acceleration rates a m/s2 − 30 %; − 50 % 1.75; 1.25 Track length s km ± 50 % 1; 3 Occupancy rate cocc % 25 to 100 % 25; 50; 75; 100 Max. operational speed vmax m/s ± 20 % 8; 12

A change in the basic rolling resistance, cross sectional area and occupancy rate inﬂu- ence the coeﬃcients of the running resistance polynomial. Those values are not shown here, but the total set of parameters for each simulation is displayed in Appendix G.

5.3 Results of reference simulation and its variations

The results from the reference calculation are given ﬁrst and in detail, followed by the results for the cases with varied parameters according to Table 5.3. The target speed is limiting the vehicle speed. The target speed, vehicle speed and vehicle acceleration are displayed in Figure 5.3. With increasing vehicle speed, the running resistance increases

43 Chapter 5. Simulation of energy usage

12 3

10 2 2 8 1

6 0

Speed in m/s 4 -1

Target speed in m/s Acceleration 2 Vehicle speed -2 Acceleration

0 -3 0 50 100 150 200 Time in s

Figure 5.3: Speeds and acceleration of the reference calculation. Please note the diﬀerent scales for speed and acceleration.

(compare Figure 4.7). This relation is obtained from the running resistance polynomial with the predefined coefficients, and the running resistance is plotted in Figure 5.4. It can also be seen that the traction force reaches its maximum at the acceleration and deceleration phases, whereas it is rather small during steady cruise. The maximum traction force that the vehicle can provide limits the available acceleration if the other resistances are subtracted. The maximum acceleration in turn is linked to the adhesion coefficient µ defined by the user (Equations 5.5), and thus the maximum acceleration can be limited by the adhesion coefficient.

Facc,max Ftrac,max − Frr − Fgr amax = = (5.5a) me me F + F a µ = rr gr + max (5.5b) m · g g At the deceleration phase at the end of the trip, the traction force is negative, delivering a braking force which is used to regenerate energy. The running resistance curve in the beginning and end of the trip should be the same as the resistance curve that was defined in Figure 4.7. However, there is a discrepancy that has to be noted between no running resistance at zero speed and the rolling resistance that starts immediately when the vehicle speed is not zero (i.e. rolling resistance). This behaviour is not modelled properly as Figure 5.5 indicates (dotted line), but due to the use of average forces and speeds the effects on the calculation are minor. The resistance force at a point infinitesimal close to zero should have the c1 coefficient as the speed independent resistance force. A possibility would be to decrease the step length dx, but this would increase the calculation time substantially.

44 5.3. Results of reference simulation and its variations

3000 200 180 2000 160 140 1000 120 0 100 0 50 100 150 200 80 -1000

Traction in force N 60 Resistance force in force N Resistance 40 -2000 Traction force Running resistance 20 -3000 0 Time in s

Figure 5.4: Forces of the reference calculation. Please note the diﬀerent force scales.

3000 200 180 2000 160 140 1000 120 0 100 0 1 2 3 4 5 6 80 -1000 60 Traction in force N Resistance force in force N Resistance 40 -2000 Traction force Running resistance 20 -3000 0 Time in s

Figure 5.5: Resistance force modelling close to zero velocity and correct running resistance (dotted line). Please note the diﬀerent force scales.

The powers and accumulated energies versus time can be obtained by the given equations in Section 5.1 and are presented in Figure 5.6 for the beginning and the end of the trip. The intermediate time is not shown as there is no change in numbers. Please note

45 Chapter 5. Simulation of energy usage that powers are displayed on the left axis and energies on the right.

30 0.0010 0.0009 25 0.0008 0.0007 20 0.0006 15 0.0005 0.0004 Power in kW 10 Tractive power Refeed power 0.0003

Tractive energy 0.0002 Energy in kWh per segment 5 Refeed energy 0.0001 0 0.0000 0 1 2 3 4 5 6 Time in s (a) At the acceleration phase at the beginning of the trip

25 0.0010 Tractive power 0.0009 Refeed power 20 0.0008 Tractive energy Refeed energy 0.0007 15 0.0006 0.0005 10 0.0004 Power in kW 0.0003

5 0.0002 Energy in kWh per segment 0.0001 0 0.0000 199 200 201 202 203 204 205 Time in s (b) At the deceleration phase at the end of the trip

Figure 5.6: Powers and energies for the reference calculation. Please note the diﬀerent scales for powers and energies.

46 5.3. Results of reference simulation and its variations

The tractive power is rising with speed, which increases while the traction force is constant over the acceleration time. It is the force that is calculated from the running resistance and acceleration parameters. The tractive energy per segment is constant during the acceleration phase because the time step decreases and the traction power increases. The change in time step is possible because the calculation is not based on a time-step discretisation dt, but on a length discretisation dx. The traction power for the acceleration phase can be calculated by the following equation, which is done by STEC using average values for forces and speeds per interval.

Ftrac · v Ptrac,gross = (5.6) ηtrac For the acceleration phase in the beginning of the trip, the refeed power and energy are zero. This changes at the deceleration phase, when tractive power and energy decrease to zero and energy is regenerated in the braking process. The total trip time for the reference calculation is 204.05 seconds, and the average speed is 9.8 m/s. The power during regeneration can be calculated with the following relation, including the losses in the regeneration process that reduce it.

Pregen,net = Ftrac · v · ηregen (5.7)

The refeed and traction powers are diﬀerent due to the applied eﬃciencies in the propulsion and regeneration system. The energy usage for the present 2 km long trip is split in Figure 5.6, which is also visualized in Figure 5.2. It is calculated as an accumulation of the energy over track segments. Note that the three rightmost columns are the split of the net energy.

0.25 0.200 0.189 0.20 0.15 0.083 0.063 0.10 0.042 0.05 -0.011 0.00 Energy in kWh -0.05

Figure 5.7: Results of the reference calculation

The net energy can be transformed to the unit of kWh/seat-km by a simple conversion,

47 Chapter 5. Simulation of energy usage including the travelled distance and the number of seats.

kWh E [kWh] 0.189 kWh kWh E = net = = 0.0236 (5.8) seat − km n [seats] · s [km] 4 seats · 2 km seat − km

This relation yields a related energy usage of 0.0236 kWh/seat-km for two kilometres of trip distance and four seats for the empty vehicle. All results from the variation from the reference calculation are displayed in Figure 5.8, which shows numbers for the energy split similar to Figure 5.7.

0.035 0.030 0.025 0.020 0.015 0.010 0.005

Energy in kWh/seat-km 0.000 -0.005

Net energy Gross energy Auxiliary energy Regenerated energy Air resistance energy Rolling resistance energy ReferenceReference calculation calculation f0 f+0 +15% 15 % f0f -0 15%- 15 % Paux Paux + +15% 15 % PauxPaux - 15%- 15 % AD A -D 15%- 15 % ADA D+ +15% 15 % amax amax - 30% - 30 % amaxamax - -50% 50 % Track s - 50length % s - 50% Tracks + length50 % s + 50% Occupancy cocc = 25 rate % cocc 25 % Occupancycocc = 50 rate % cocc 50 % Occupancy cocc = 75 rate % cocc 75 % Occupancycocc = 100 rate % cocc 100 % vmax vmax - 20%- 20 % vmaxvmax + + 20% 20 %

Figure 5.8: Results of the parameter variation

The detailed results are given in Appendix G. A detailed view on the impact on the total energy usage expressed in kWh/seat-km is displayed in Figure 5.9, and focus on the amount of regenerated energy and its variation with the varied parameters is shown in Figure 5.10.

48 5.3. Results of reference simulation and its variations

15%

10% % 14.7 4.9 % 4.9 11.0 % 11.0

5% % 3.7 3.2 % 3.2 3.2 % 3.2 7.4 % 7.4 6.4 % 6.4 5.4 % 5.4 0.3 % 0.3 0.0 % 0.0 0.1 % 0.1 0% -6.4 % -6.4 -5% % -1.0 -1.1 % -1.1 -3.2 % -3.2

-10% % -4.9

+ 20 % + - 20 % - 30 % - 50 % + 15 % + - 15 % = 25 % = - 15 % 15 % + 50 % = 75 % = 100 % =

max D D aux aux + 15 % + - 15 % max max max occ occ occ occ - 50 % 50 % + 0 0 Reference f f P P A A a a s s c c c c v v

Figure 5.9: Relative impact on net energy usage of the parameter variation of the reference calculation (0.0236 kWh/seat-km)

120% 100% 80%

60% % 100.0 40% 28.7 % 28.7 21.6 % 21.6

20% % 14.4 7.2 % 7.2 0.2 % 0.2 0.0 % 0.0 % 0.0 43.3 % 43.3 0% -20% 0.7 % 0.7 0.0 % 0.0 -0.7 % -0.7 % -0.2 -2.5 % -2.5

-40% % -5.9 -60% -35.7 % -35.7 -33.3 % -33.3

+ 20 % + - 20 % - 30 % - 50 % + 15 % + - 15 % = 25 % = - 15 % 15 % + 50 % = 75 % = 100 % =

max D D aux aux + 15 % + - 15 % max max max occ occ occ occ - 50 % 50 % + 0 0 Reference f f P P A A a a s s c c c c v v

Figure 5.10: Relative impact on regenerated energy of the parameter variation of the reference calculation (0.0014 kWh/seat-km)

49 Chapter 5. Simulation of energy usage

5.4 Discussion of results

The results of the reference calculation (Figure 5.7) show, that the energy to overcome the rolling resistance is higher than the energy for the auxiliary systems. However, it has to be noted that the auxiliary energy is simply a multiplication of the auxiliary power and the total trip time. The air resistance for the reference calculation is around half of the rolling resistance, and about 5.5 % of the gross energy is regenerated during braking.

Auxiliary power The auxiliary power was taken from measurements in real cars, and are subject to major variations depending on the operating environment, the passengers habits and their comfort needs. The auxiliary power can thus change within a large range (up to factor two), and only an average value was taken into account for the simulation. This indicates that the chosen auxiliary power Paux has a major eﬀect on the global energy usage. Changing the auxiliary power by ± 15 % leads to a change in total energy usage that is around ± 4.9 %. Due to the direct eﬀect on the net energy, it can rise above the energy for rolling resistance.

Rolling resistance A change in the basic rolling resistance coefficient f0 is directly linked to the c1 coefficient of the resistance polynomial, which in turn influences the energy used to overcome the rolling resistance. A change of 15 % in f0 results in a rise of 14.6 % of the rolling resistance energy. As this is approximately one third of the net energy, it is increasing by 6.4 %. The maximum running resistance force is increasing by 9.9 % and so reflects the higher rolling resistance at a speed of 10 m/s.

Aerodynamic resistance The aerodynamic values are investigated by the change of the cross-sectional area AD, which can be seen as a modification of the total product of the aerodynamic parameters (cD · AD). A change in one of those parameters results in a modified c3 coefficient, and an increase or decrease by 15 % will lead to an energy to overcome aerodynamic resistance that is changed by 14.3 %. This discrepancy arises since the software uses both the c2 and c3 coefficients for the energy split in aerodynamic resistance. The trip time is nearly not effected, but the maximum resistance force changes by ± 5.0 % due to the different air resistance. The final effect on the total energy usage per seat kilometre is not as large, it is at ± 3.2 %.

Acceleration The vehicle acceleration was reduced in two stages, from 2.5 m/s2 to 1.75 m/s2 and 1.25 m/s2. The reason was that the reference acceleration (and deceleration) was at the upper limit of what is usual for passenger transport, and a more comfortable ride was taken into consideration. The trip time increases slightly by 2 % for 1.25 m/s2, which increases the auxiliary energy correspondingly. Nevertheless, the total energy usage increased by values below 0.4 %, which can be explained by the decrease of regenerated energy of about 6 % for the 2 km trip. The lower acceleration/deceleration rates result in lower traction and braking forces, which reduce the regeneration power and thus the amount of regenerated energy.

Trip distance When the trip distance s is changed in the speciﬁed way to one and three kilometres, the used energies decrease and increase as expected. The impact on the energy usage per seat kilometre is below 3.3 % when the trip distance is modiﬁed by factor

50 5.4. Discussion of results two. It has to be noted that a decrease in trip distance results in higher energy usage per seat kilometre, because the acceleration phase takes a larger share of the total trip. In turn, longer trips will reduce the energy usage per seat kilometre, but the eﬀect will fade out as the acceleration phase is negligible at some point. The regenerated energy in kWh is not aﬀected by a change in trip length, and thus the regenerated energy in kWh/seat-km increases by 100 % at half the distance.

Occupancy rate From all varied parameters, only the occupancy rate (which is directly linked to the vehicle mass by an assumed passenger weight of 80 kg) and the vehicle speed have a significant influence on the amount of regenerated energy (compare Figure 5.9). As mentioned before, a change in trip length has no impact on the regenerated energy, and the increase of the occupancy rate by 100 % is due to the change of the related basis for the conversion to kWh/seat-km. A change in the occupancy rate results in new c1 coefficients, which explains the direct and linear impact on the rolling resistance and regenerated energy. The energy to overcome rolling resistance from empty to full vehicle is increased by 31.7 %, which basically is the percentage of load increase by four passengers (320 kg compared to 1000 kg empty weight). The regenerated energy increases by 28.7 %. As the occupancy rate was changed in four steps from empty to full vehicle, an increase in the total energy usage of approximately 3.7 % per additional passenger can be seen (Figure 5.9). The total net energy usage per seat kilometre is 14.8 % higher compared to the empty vehicle, although the maximum resistance force rises by 20 %. The change in total trip time is minor, being 1.24 seconds longer which is a relative increase by 0.6 % between empty and fully loaded vehicles.

Speed A change in maximum speed is mainly investigated to see the energy saving potential of this parameter. It is usually increased to reduce the trip time or decreased to reduce the energy usage. The vehicle target speed is set to values 20 % below and above of the initial value, which means 8 m/s and 12 m/s. A decrease of maximum speed by 20 % will result in a 24 % longer trip time, but only a saving of energy of approximately 1 %. On the other hand, an increase by 20 % reduces the trip time by 15.9 %, but the needed energy rises only by 5.5 % in kWh/seat-km. A deceleration from higher speeds enables a much higher amount of regenerated energy (+ 43.3 %), but the air resistance energy is increased in the same range (+ 43.4 %).

Conclusions From the above mentioned results, the following conclusions can be drawn. It can be seen that an increase in speed is a sufficient measure to reduce the trip time with relatively low increase in energy. The energy usage can not be reduced in the same range by reducing the target speed, because the resulting increase in trip time will require more auxiliary energy and the regenerated energy is decreased to a large extent. A reduction of the necessary auxiliary power is a direct benefit for the total energy usage, and a desirable parameter for optimisation. It has to be clear that some parts of the auxiliary power are related to the operational environment and passenger comfort needs and can be influenced, and some are related to the vehicles operational systems such as the steering and communication system. The influence by the rolling resistance is in the same range as the auxiliary power, and a reduction in the rolling resistance coefficient will lead to direct savings of energy. The reduction of the rolling resistance will be accounted for in Chapter 6.

51 Chapter 5. Simulation of energy usage

Modifying the aerodynamic properties of the vehicle and reducing the aerodynamic resistance is not as effective as the rolling resistance or auxiliary power modifications, but it will have a positive effect on the vehicle energy usage. This is especially the case if the operational speed is increased to more than the investigated 10 m/s. A reduction by 15 % of the aerodynamic area AD results in a 3.2 % lower energy usage. This is equivalent to a reduction of the product of (cD · AD), which means that these two parameters have to be improved. Although the mentioned parameters are efficient to modify to reduce the energy usage, there are others that were expected to have influence but do not reduce the energy as intended. As indicated above, reduced acceleration rates will not have a significant effect on the energy usage, which means that they can be adopted to the passenger comfort needs without significant negative aspects concerning energy and trip time. Very short trips have a negative impact on the energy usage, but they are a necessary part of the urban transportation concept. Although the energy usage of a fully loaded vehicle is over 14 % higher compared to an empty vehicle, the transportation efficiency in kWh/pkm increases by a higher rate as the occupancy rate rises from 0 % to 100 %. The propulsion and braking efficiencies are directly linked to the traction and refeed energies via the powers (compare Equations 5.6 and 5.7) and thus have a great potential to improve energy usage. The energy usage of the SkyCab system can be compared to the numbers of competitive PRT systems that were introduced in Chapter 2 by kWh/seat-km and kWh/passenger km. The results are displayed in Figures 5.11 and 5.12, where empty vehicles are considered.

0.045 0.043 0.040 0.038 0.035 0.030 0.024 0.024 0.025 0.023

kWh/seat-km kWh/seat-km 0.020 0.015 0.010 0.005 0.000 Ultra PRT SkyCab Vectus PRT 2getthere PRT

Figure 5.11: Comparison of SkyCab to competitive PRT systems on the basis of empty vehicles in kWh/seat-km. Please note that measurements or simulation conditions of other systems are not known and thus great discrepancies may arise from considered gradients, wind speeds, trip distances and loads.

52 5.5. Greenhouse gas emissions for diﬀerent electricity mixes

Occupancy rate kWh/pkm

0.35 0.32 0.32 0.32 0.32 0.30 0.25 0.20 0.133 0.133

0.15 0.120 0.074 0.074 0.10 0.070 0.05 0.00 Ultra PRT SkyCab Vectus PRT 2getthere PRT

Figure 5.12: Comparison of SkyCab to competitive PRT systems on the basis of kWh/pkm and with the same occupancy rate taken from personal cars. Please note that measurements or simulation conditions of other systems are not known and thus great discrepancies may arise from considered gradients, wind speeds, trip distances and loads.

The energy usage of the SkyCab system is thus in the same range as the Ultra PRT. The Vectus energy usage is higher due to higher speeds with six passengers, and the system by 2getthere has a much higher empty weight and optional payload. Additionally, its operational range is three times higher, making it necessary to carry large batteries (compare Table 2.1). The relation between the systems is maintained when the energy is combined with the occupancy rate (Figure 5.12) since the occupancy rates are assumed to be the same. The occupancy rate was chosen in Chapter 2 to 32 %, which is similar to personal cars. The simulation conditions for the SkyCab system are clear, but the basis of numbers for competitive systems are unknown. In this report, gradients and wind speeds were not considered as they are subject to great uncertainties depending on the operational environment. In addition, only the power to operate the vehicle was regarded for all systems in the comparison, without energy for construction of track and vehicle, electricity distribution, maintenance and additional power for winter operation. A suﬃcient comparison can only be conducted with more detailed information about the other systems and a full life cycle analysis of all systems.

5.5 Greenhouse gas emissions for diﬀerent electricity mixes

The greenhouse gases that are emitted by electric operating transport modes can be directly linked to the energy they use to provide the transportation service. The transport sector makes up approximately one third of the world energy usage (see Chapter 2) and about one fourth of the worldwide energy-related CO2 [8], which dominates the greenhouse gases. Average emissions for the market of the Nordic countries between the years 2000 to 2004 are given in Tables 5.4 and 5.5. A green electricity mix, mainly consisting of hydro

53 Chapter 5. Simulation of energy usage power and measured by the Swedish company Vattenfall in 1996, is displayed in the same table. The greenhouse gas emission of the empty SkyCab vehicle can be calculated by multiplying the energy usage per seat kilometre with the respective amount of produced pollutants per generated kWh of electric energy. The results are given in the same table. The vehicle results for a fully loaded vehicle will be 14.8 % higher according to the simulation results.

Table 5.4: Greenhouse gases in relation to produced electric energy for the Nordic market [6] and for the present reference case

Greenhouse gas Nordic market Vehicle related emission

CO2 96 g/kWh 2110 mg/seat-km NOx 208 mg/kWh 4.58 mg/seat-km HC 1 mg/kWh 0.022 mg/seat-km CO 14 mg/kWh 0.308 mg/seat-km Total 2114.9 mg/seat-km

Table 5.5: Greenhouse gases in relation to produced electric energy for green energy sources[6] and for the present reference case

Greenhouse gas Green energy (hydro power) Vehicle related emission

CO2 0.07 g/kWh 1.54 mg/seat-km NOx 0.27 mg/kWh 0.00594 mg/seat-km HC 0.26 mg/kWh 0.00572 mg/seat-km CO 1.9 mg/kWh 0.0418 mg/seat-km Total 1.594 mg/seat-km

It can be seen that the greenhouse gas production by the conventional electricity mix is much higher than by the green energy production. This indicates a huge potential to inﬂuence and decrease the environmental footprint of the transport system, and makes it easy to adopt it to available green energy sources. The very low numbers for the regenerative sources suggest to focus on these energies for a sustainable transport system.

54 Chapter 6

Improving energy eﬃciency

In Chapter 5, key parameters were found that have significant influence on the energy usage. This chapter gives suggestions for the optimisation of those parameters and indicates the potential and limitations they imply. A focus is put on the rolling resistance, since it is the major contributor. This comprises the pavement and tyre specifications, including suggestions for energy efficient tyres. Measures to reduce the auxiliary power are presented, followed by suggested improvements concerning aerodynamic resistance. Finally, additional factors are investigated such as drive train efficiencies and track layout.

6.1 Reduction of rolling resistance

This report focuses on the energy efficiency of a vehicle, and this implies a low rolling resistance of the vehicles tyres. For all suggestions in the following section to reduce the rolling resistance, it has to be clear that this resistance is only one of several properties that define a high-performance tyre. The final product is a compromise of all functions. The parameters in the following section are only investigated with regard to a reduction of the energy usage, and effects on other properties may limit the extent of optimisation. Although speed is one important aspect of rolling resistance, it is neglected due to the low operational speeds of the vehicle. The calculations (and the running resistance polynomial) show that an increase in speed by 20 % raises the rolling resistance by approx. 1 % at speeds below the investigated 12 m/s.

6.1.1 Pavement and guideway The texture of the pavement is defined as the deviation from the ideal flat surface, and a coarser texture increases the running resistance [96]. Texture can be distinguished by its wavelength, and is called micro and macro texture. Smoother pavements can reduce rolling resistance by approx. 2.5 to 4.5 % [96]. Figure 6.1 gives an overview of common pavement types, their characteristics and the related rolling resistance compared to new concrete. Concrete is thus more energy efficient than asphalt and polished concrete is the most energy-saving pavement, but other factors such as wet braking behaviour have to be taken into account. The traction capability of a polished surface may be insufficient. The difference between new concrete and rolled asphalt with rounded aggregates is minor. A coated asphalt with its coarse macro texture and harsh micro texture should not be used as the rolling resistance can rise by one third compared to new concrete. A third factor of

55 Chapter 6. Improving energy eﬃciency

Figure 6.1: Pavement surfaces and their influence on rolling resistance for passenger car tyres [97] the rolling resistance is the stiffness of the pavement, but in a literature review by Willis [96], not all studies came to a consistent conclusion due to the inherent differences in texture and smoothness and their affect on the road stiffness. The rolling resistance on wet roads will increase due to the necessary displacement of water, and a drainage system should be implemented [44]. To conclude, the pavement should be constructed of either smooth concrete or rolled asphalt, including a drainage system to keep the track clear of water.

6.1.2 Tyre To give an overview of the complexity of tyre manufacturing, Figure 6.2 shows the conflicting goals during tyre development and the compromise for wet braking conditions as an example. The optimisation of one parameter results in a compromise for all other parameters. Compared to the pavement, the tyre itself offers various parameters that influence the rolling resistance. They comprise geometrical values such as tyre width and diameter as well as the tyre working temperature and other factors. The influence of tyre width is not simple to answer as there are various interrelations to other parameters such as aspect ratio (which is defined as the ratio between section width to section height), section height, wheel diameter etc. An overview on investigations by various authors on aspect ratio is given by Schuring [80], but a consistent conclusion could not be drawn. In general, a larger width does not only affect rolling resistance, but also increases the aerodynamic drag of a vehicle and improves the tyre life. A rule of thumb is that reducing the tyre width by 1 cm results in a reduction of (cD · AD) by approx. 1.5 % [89]. Tyre diameter affects the rolling resistance of a wheel significantly, especially on soft surfaces. On hard pavement, the effect is not as pronounced but still present. The influence of tyre diameter, as well as hard and soft pavements, is shown in Figure 6.3a. Equation 4.1 gives the relation between the wheel radius and the rolling resistance force [68]. An increase of the rolling diameter by 20 % gives a reduction of rolling resistance force of approx. 5.9 %, which results in a saving in total energy usage per seat kilometre of approx. 2 %. This observation is roughly confirmed by an estimate by Michelin [89], who

56 6.1. Reduction of rolling resistance

DirectionalDirectional stabilitystability

Tyre weightTyre weight SteeringSteering precision precision

ServiceService lifelife Ride comfort comfort

Rolling Rollingresistance resistance WetWet braking capability

AquaAquaplaning planing Figure 6.2: Conﬂicting goals during tyre development and the compromise for wet braking conditions (adopted from [19]). A larger radial position is equivalent to an improvement.

predicts a reduction in rolling resistance by 1 % for each cm of increasing tyre diameter. This behaviour suggests to increase the tyre diameter of the vehicle to reduce rolling losses, although there may be limiting factors such as space in the track or visual impact that limit this optimisation. The tyre temperature in the simulation was assumed to be at the optimum working temperature of the tyre, and thus enabling the lowest rolling resistance. As indicated in Figure 6.3b, the rolling resistance can increase by up to 20 % for a cold tyre. The simulation results show that a change in the rolling resistance is directly linked to the overall energy usage, and this sensitivity should be taken great care of. A more meaning- ful temperature than the overall tyre temperature is measured at the shoulder, which is the part that is mostly contributing to the rolling losses. The relation of rolling resistance coefficient and the shoulder temperature for a 195/75R14 passenger car tyre was investigated by the Society of Automotive Engineers and is displayed in the following Figure 6.3b. It can be seen that, if the vehicle starts at an ambient temperature of 15 ◦C, the rolling resistance coefficient is at approximately 0.015, whereas it drops to the simulated coefficient of 0.012 at shoulder temperatures around 40 ◦C and above. For the case of a PRT system a heating of tyres could compensate for the losses and optimise the performance. A closed track design of the guideway will support such heating systems, because the dissipative heat from rolling tyres, motor and brakes is partly used to keep the track warm and reduce heating effort. This effect of closed guideways was earlier mentioned by Irving [47]. A wet track would also lead to higher rolling resistance due to lower temperatures [37]. The cool down of the vehicles tyres should be reduced by constant service without long stops. The distribution and availability of vehicles should be on demand, but still as less vehicles as possible should circulate on the network to achieve the highest utilisation. The inflation pressure of a tyre may be increased to reduce rolling resistance, and a higher pressure will reduce the flexure work of the sidewalls. An increase from the

57 Chapter 6. Improving energy eﬃciency

(a) Influence of tyre diameter on the rolling (b) Impact of shoulder temperature on the resistance coeff. of a passenger car tyre [97] change of the rolling resistance coeff.[97]

Figure 6.3: Inﬂuence of tyre diameter and shoulder temperature on the rolling resistance coeﬃcient

considered 2.07 bar in the simulation to 2.76 bar would decrease the rolling resistance by approx. 20 %. The passenger comfort is one limiting factor, as the damping capability of the tyre is reduced. The rolling resistance force in curves is dependent on the cornering stiﬀness, and a larger and wider tyre will have a higher stiﬀness [37]. The wheel geometry has to be adjusted properly to reduce potential resistances due to misalignment of the wheels. A change of 1◦ in camber angle will increase the rolling resistance by approx. 1 % [49].

6.1.3 Examples of energy eﬃcient tyres

The predefined limitation to tyres with an outer diameter of maximum 400 mm leads to the problem of finding a manufacturer with those tyres available. Even the smaller passenger car tyres (e.g. 125/60R13) have diameters of more than 480 mm and are not applicable to the SkyCab vehicle. A custom tyre especially for SkyCab may increase costs dramatically, as considerable development effort is required by the tyre manufacturers. Electric cars in the past such as the GM EV1 (1996) were equipped with special tyres to enhance performance and range [62]. GM relied on a special tyre by Michelin, called Proxima RR, in the dimensions of 175/65R14, that were self-sealing and inflated to 3.5 bar pressure [54]. The actual BMW i3 is fitted with energy efficient tyres by Bridgestone. They use the model Ecopia EP500 in the dimension of 155/70R19, which indicates the trend towards large and slim tyres for increased energy efficiency. This relation can be seen in Figure (6.4), displaying the EU tyre labels for actual electric vehicle tyres. Figure 6.4a and 6.4b show the smallest tyres in the Continental portfolio for electric cars, whereas Figures 6.4c and 6.4d show the larger equivalent and the Bridgestone Ecopia tyre.

58 6.1. Reduction of rolling resistance

A A B B C E E E

(a) Continental eCon- (b) Continental eCon- (c) Continental eCon- (d) Bridgestone Eco- tact 125/80R13 [72] tact 145/80 R13 [73] tact 235/60 R18 [20] pia EP500 155/70 R19 [15]

Figure 6.4: EU tyre labels for electric vehicle tyres

From the shown tyre labels the correlation between size and rolling resistance can be seen. Although even the smallest tyres are too large for the present specification, the Continental eContact in the dimension 145/80 R13 with an outer diameter of 562 mm would be the choice with the best compromise between size, rolling resistance and wet braking capability. Solid rubber tyres do not offer lower rolling resistance due to the increased dissipation of heat in the deflection process [79]. Non-pneumatic wheels are currently under development by the tyre manufacturers, consisting of polyurethane composites. The Michelin tyre is called "Tweel" (Figure 6.5) and is expected to have at least 10 % lower rolling resistance than pneumatic wheels due to small hysteresis effects [18]. They occur in the rubber tyre shoulder during the deflection process in a load and unload cycle of the contact area. The rolling resistance coefficient for Michelin’s "Tweel" is estimated to 0.0055, and the Michelin goals are to decrease the rolling resistance between 30 to 50 % compared to an energy efficient pneumatic tyre [18]. Bridgestone is in the development of a similar project, the "air free concept tyre" [14]. Besides the lower rolling resistance, these tyres can not deflate and increase vehicle safety.

(a) Components of the airless tyre from the initial design with de- formable inner part [59] (b) "Tweel" mounted on Audi A4 [18]

Figure 6.5: Airless tyre "Tweel" by Michelin

59 Chapter 6. Improving energy eﬃciency

6.2 Reduction of auxiliary power

The auxiliary power is the second major part for optimising the present energy usage, although it is very dependent on the passenger needs and the surrounding conditions. Very hot or cold environments can increase the needed auxiliary power dramatically, and a full utilisation of heating or cooling capability could reach 5 kW instead of the investigated average of 1.08 kW [13]. The spread in auxiliary power for the Nissan Leaf and a total of 7375 cycles can be seen in appendix E. The lowest energy usage is just above zero kW at the considered temperature, which is probably caused by non-comfort related energy usage. The maximum power is at 3 kW, which is approx. 3 times higher than the average and caused by high utilisation of heating or cooling systems. Two different approaches towards a reduction of auxiliary power in buses were considered in a PhD thesis by Andersson [5], improving the power management and the components. During acceleration phases, auxiliary powers should be switched off or reduced to make high power available for the propulsion system. This will reduce the peak power requirement and energy can be saved. The safety margin of available power can be reduced and smaller systems can be utilised to a larger extent. Overall acceleration performance is improved and acceleration time reduced if the limiting factors are not yet reached. Energy that is regenerated during braking should be used as much as possible by auxiliary powers instead of charging it to the battery. This can be achieved by setting the HVAC unit to 100 % and the rest of the energy is converted by the converter unit and stored in the batteries. Such a procedure would reduce the conversion losses during charging and discharging processes and heat or cool the compartment at deceleration phases if necessary. Great care should be taken in the selection of the components for the vehicle systems [5]. Converting the energy to air pressure and using it for pneumatic door openers is not an energy efficient approach. Instead, electric door openers should be used. For all illumination purposes, high efficiency light-emitting devices (LED) should be used [93]. This includes the illumination in the passenger compartment as well as headlights, door and station lights. The combination of energy efficient components and an intelligent control process reduced the energy usage in a public hybrid bus by 2.5 to 5 % [5]. Vehicle range can be improved if the heating or cooling process of the passenger cabin is carried out at the stations when vehicles are connected to the electric grid and charged.

6.3 Reduction of aerodynamic resistance

Simulation results show that the change in aerodynamic parameters has a minor impact on the overall energy usage for the present application. However, the aerodynamic influence will increase by the power of two when the vehicle speed is raised to reduce trip times. The aerodynamic resistance can be reduced in two ways, shape and detail optimisation. The aerodynamic coefficient cD is changing with the shape of the vehicle, and a low profile design with an aerodynamic shape should be considered [63]. This implies a round front part and a long aerodynamic tail, as can be seen in Figure 6.6. An excellent way to optimise the air drag coefficient without visible design changes is to smoothen the underbody of a vehicle. Further improvements regarding vehicle details

60 6.4. Drive train eﬃciencies and other factors 1 Einführung 23

Abb. 1.24 Kurzfassung der Versuchsergebnisse,die W. E. c Lay (1933) an einem wandel- D baren generischen Automodell erzielte

Figure 6.6: Aerodynamic drag coefficient in relation to various front and rear design combinations [81] werden sollte, wurde zu einer Zeit als Ziel ins Auge gefasst, als die Autos mit cW =0,55bis0,65auf die Straße kamen. Auf die Idee, deren sehr hohen Lufwiderstand mit weniger radikalen Formen schrittweise abzubauen, sind die frühen Aerodynamiker offenbar nicht gekommen. So kam es zu der unglücklichenare Polarisierung: displayed „Normale“in Table 6.1. Autos A blieben combination aerodynamisch of indicated ungünstig. improvements Dagegen warte- could allow for a ten „aerodynamisch“ ausgebildete mit exotisch anmutenden Formen auf – und in deren Folge mit technischen Einschränkungen, die vom Markt nicht akzeptiert wurden21. Table 6.1: Detail variations of cD and the related change in % for a typical passenger car [60] 1.2.7 Erste Parameterstudien Influence on cD by Change in cD in % Diese Kluf wurde erst überbrückt,Lower car body als sich by die 30 mmFahrzeugtechniker approx.selbst der - 5Aerodynamik annah- men. Das geschah annäherndSmooth gleichzeitig rim surface in zwei Schulen: in den USA - 1 durch. . . - 3 Walter E. Lay22 und in Deutschland durch WunibaldSlim tyres Kamm23. - 2 . . . - 4 Sealing of gaps - 2 . . . - 5 Lay (1933) führte als erster systematische Parametervariationen durch. Er veröffentlichte sie 1933 Smooth underbody - 1 . . . - 7 in seiner berühmten ArbeitExternal „Is 50 Miles mirrors per Gallon Possible with Correct + 2 . Streamlining?“;. . + 5 einen Aus- zug daraus fasst Abb. 1.24Ventilationzusammen. for Front- interior und Heckpartie eines approx. generischen + 1 Modells mit den Hauptabmessungen einesOpened Pkw wurden windows schrittweise abgewandelt. Die approx. Messungen + 5 offenbarten eine starke Wechselwirkung zwischenOpened sunroof Bug und Heck. So kommt z. B. ein approx. strömungsgünstiges + 2 Heck nur dann voll zum Tragen, wenn der Vorderwagen anliegend umströmt wird. Darauf wurde schon bei der Diskussion des „Bootshecks“ hingewiesen. Stellt man z. B. der Strömung eine zu steile Windschutz- scheibe entgegen,reduction so hat das of einen up to starken 15 %, Anstieg but des these Widerstands detailed zur optimisations Folge. Wenn andererseits have to be der applied stepwise to a vehicle prototype. The tunnel effect of the vehicle bogie in the narrow, enclosing guideway similar to trains is not yet considered, and an increase in aerodynamic resistance 21 Dass die „Stromlinie“is expected auch [58]. in vielen anderen Bereichender Technik und bei Gebrauchsgegenstän- den aktuell war, wurde von Lichtenstein und Engler (1992) dargestellt. 22 Professor an der Michigan University. 23 Professor an6.4 der TechnischenDrive Hochschule train in efficiencies Stuttgart und Direktor and des dortigen other Forschungs- factors institutes für Kraffahrwesen und Fahrzeugmotoren (FKFS). Seine Biographie haben Potthoff und Schmid (2012)To vorgelegt. increase the total efficiency of the heating system, waste heat from the electric components such as DC converters, control unit and the electric motor should be utilised as much as possible [48]. During stops when doors are opened for passenger boarding it is sufficient to reduce the heating or cooling power, respectively, and to stop the ventilation systems to prevent a total exchange of air volume in the vehicle [9].

61 Chapter 6. Improving energy eﬃciency

Electric motor efficiencies increase with size and speed, and cooled motors are even more efficient, although this effect contributes only by approx. 1 % [54]. The choice of a high power motor will lead to a high rate of regeneration and no necessity for mechanical braking during regular operation [82]. On the other hand, high power motors have low efficiencies at low torque and will use more energy [54]. The traction performance can be enhanced by the use of torque-vectoring transmissions, which will lead to optimal brake torque at each wheel and a maximum of traction utilisation [42]. The benefit is a maximum utilisation of traction and braking forces. The influence of weight on the overall energy efficiency is clearly visible in the simulation results, and the vehicle battery is a major part of the total vehicle weight. High energy density batteries can potentially decrease the battery weight and increase vehicle range. The weight can further be reduced by lightweight materials and integration of several components in one system. The transmission and differential can be omitted if the motors are placed in the wheels. A gearbox is not necessary for electric motors as they can deliver torque from zero speed [54], and the converter and the motor can be combined in one unit [38]. Placing the on/off points on elevated above the ground would reduce the energy that is regenerated during the on/off point approach and thus the losses in the regeneration process are reduced. The track gradient during the following acceleration phase supports the acceleration and reduces the total motor power that has to be available. By those means, similar to the intelligent auxiliary power management, the safety power margin can be reduced. A measure to reduce energy usage that was not investigated in this thesis is the application of coasting at station approach. The motor can be switched off and the vehicle will decelerate according to its resistance forces, and the final braking can be done with the regeneration mode of the motor. Nevertheless, the advantages are considered minor due to short trips, low vehicle speeds and less regenerated energy.

62 Chapter 7

Conclusions and future work

The goal of this MSc thesis was to identify the relevant parameters that determine the overall energy usage of a novel transport system on basis of autonomous vehicles on a dedicated guideway network. The energy split was of interest and was investigated by setting up a simulation model, based on a case study of SkyCab and estimations of vehicle parameters. Various simulations were conducted, consisting of a reference calculation and 16 variations of key parameters. The relation to GHG emissions was determined and emissions were calculated for a conventional Nordic and a green electricity mix. Finally, suggestions were proposed to reduce the total energy usage by improving key properties of the vehicle. Results show that the energy needed to overcome rolling resistance is higher than for auxiliary power, but together they are accounting for more than 77 % of the total net energy in the reference calculation. The air resistance force is accounting for approx. 23 % of the total net energy. This indicates clearly the potential of a reduction of rolling resistance and auxiliary power, and parameter studies revealed a direct link of both to the energy usage in kWh/seat-km. Auxiliary power is strongly dependent on the ambient temperature and the comfort need of the passengers. The occupancy rate of the vehicle has not only a significant impact on the energy usage in kWh/pkm, but also on the vehicles total weight. A direct relation between increasing weight and related energy usage can be drawn. The efficiencies of the drive train are a reasonable way to improve energy usage, as they are affecting losses during propulsion and regeneration in the same way. From the results, the following conclusions can be drawn. The auxiliary power can be reduced by two means, an intelligent power management and high-efficiency components, and a total reduction of 15 % will reduce the total energy usage by around 5 %. This is especially important since the auxiliary power is using energy as long as the vehicle is in service, even during idling times. A second, enormous potential can be seen in the reduction of rolling resistance by using highly energy efficient tyres and pavements. The advantage of being able to influence both properties enables considerable energy saving. Additionally, larger tyres would improve energy efficiency significantly, although this optimisation is limited by the visual impact that the vehicle will have. Future developments of airless tyres with polyurethane composites promise very low rolling resistance coefficients combined with high levels of safety. For further development, a contact to tyre manufacturers is recommended to determine the smallest available tyre, that is energy efficient and economical for the SkyCab system. A reduction of the acceleration rate does not reduce the total energy usage as much as was expected. This is due to the smaller traction forces that are transmitted and

63 Chapter 7. Conclusions and future work the directly linked regenerated energy, making it possible to adjust the acceleration rate to passenger comfort nearly independently of the total energy usage. A change in the maximum speed has a significant influence on the regenerated energy, and an increase of speed offers more benefits than disadvantages from the perspective of energy usage. For a 2 km long trip, the trip time decreases by 16 % when the speed is raised by 20 %, and the total energy usage increases only by 5.5 %. This effect is due to the auxiliary energy linked as a constant power to the trip time and an increase of regenerated energy by over 43 %. From the results, it can also be concluded that longer trips are more efficient in terms of kWh/seat-km because of the decreasing share of acceleration phase compared to the total trip distance. It has to be clear that the simulations were made on the basis of a vehicle concept, and that estimations for key input parameters had to be made. Those include among other things the air drag coefficient, rolling resistance coefficient and auxiliary powers. For a further developed vehicle with defined design and power system, the simulation parameters are recommended to be adopted to minimise the inevitable uncertainties of those numbers. Due to missing data basis, a validation of the simulation for small vehicles could not be done in this thesis, although the software is validated for trains from which field of application it originates. Other surrounding factors related to the area of application such as headwinds, temperatures and precipitation were not regarded in this thesis, and for a detailed statement on the energy usage of a specific environment they have to be taken into account. Coasting was not applied during the simulations, and although this effect is considered to be a small energy saver, it is recommended to be tested in simulations or with a concept vehicle. The tyre temperature was assumed to be in the optimal working range, thus enabling the lowest rolling resistance possible. For a PRT system, short trips and following waiting times do not allow for tyre heating up during service, and this effect has to be accounted depending on the investigated scenario of future applications. The investigation in this thesis was limited to the vehicle in the operational phase, not regarding the energy that is used for stations, maintenance, control facilities, IT and track heating (if applicable) or energy for construction or recycling of system components. For a determination of the share of those energies, a full life cycle analysis for the SkyCab system should be conducted. Finally, the comparison in this thesis of the SkyCab results with competing PRT systems has to be seen with the uncertainties of the data bases in mind. On the one hand, the detailed vehicle parameters and operational conditions of the other systems are not known, and on the other hand, key values for the SkyCab concept had to be estimated due to the concept character of the vehicle. To conclude, energy usage is only one of the conflicting goals of a successful and environmental friendly transportation system, and the reduction of energy usage entails always a compromise with other factors.

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70 Appendices

71 Appendix A

Data for energy usage of urban transport modes

Table A.1: Data for energy usage of urban transport modes. The conversion between diesel and kWh was made by the following conversion factor: 1 litre diesel is equivalent to 10.3668 kWh [10].

Transport Mode kWh/seat-km l/100 Category Year Seats Ref. km Ultra PRT (empty) 0.023 n/a PRT 2009 4 [1] Ultra PRT (full) 0.033 n/a PRT 2009 4 [1] Vectus PRT (emtpy) 0.038 n/a PRT 2012 6 [1] Vectus PRT (full) 0.040 n/a PRT 2012 6 [1] Stockholm metro 0.041 n/a Rail 2003 336 [10] 2getthere PRT (empty) 0.043 n/a PRT 2014 4 [1] 2getthere PRT (full) 0.048 n/a PRT 2014 4 [1] UK tram 0.050 n/a Rail 2003 n/a [69] UK metro 0.061 n/a Rail 2003 n/a [69] UK double-deck bus 0.061 n/a Bus 2003 n/a [69] UK single-deck bus 0.081 n/a Bus 2003 n/a [69] New cars UK 2013 0.093 4.2 Car 2013 5 [92] New cars UK 2003 0.122 5.6 Car 2003 5 [92] World avg. car (2011) 0.127 7 Car 2011 5 [35] Stockholm Diesel Bus 0.131 47.1 Bus 2005 37 [10]

72 Appendix B

Occupancy rate and energy usage per passenger kilometre

Table B.1: Occupancy rate and energy usage per passenger kilometre

Transport Mode kWh/seat- Occupancy kWh/pkm Reference km rate cocc UK tram 0.050 0.55 0.091 [69] Ultra PRT (full) 0.033 0.32 0.103 cocc on basis of car Stockholm metro 0.041 0.40 0.103 [10] UK metro 0.061 0.55 0.111 [69] Vectus PRT (full) 0.040 0.32 0.125 cocc on basis of car 2getthere PRT (full) 0.048 0.32 0.150 cocc on basis of car UK double-deck bus 0.061 0.35 0.174 [69] UK single-deck bus 0.081 0.35 0.231 [69] New cars UK 2013 0.093 0.32 0.295 [69] New cars UK 2011 0.122 0.32 0.387 [69] World avg. car (2011) 0.127 0.32 0.397 [35], [69] Stockholm diesel bus 0.131 0.28 0.468 [10]

73 Appendix C

Estimations of proportion of curves

Table C.1: Estimations for proportion of curves

Quantity Distance Assumed Assumed Total Total Total Prop. of of trips per trip number curve number curve trip curves [32] in m [32] of curves length1 of curves length in length in on trip per trip in m m m in % 8 350 0 41.88 0 0.00 2800 0.00 6 800 1 41.88 6 251.32 4800 5.24 3 1000 2 41.88 6 251.32 3000 8.38 8 2300 6 41.88 48 2010.62 18 400 10.92 Average 6.13

1Based on assumed 80 degrees curve angle and 10 m curve radius

74 Appendix D

Vehicle validation with coeﬃcients from coast down tests

400 Calculation 350 Rolling Resistance 300 Air Resistance Coast down test 250 200 150 100 Resistance force in force N Resistance 50 0 0 5 10 15 20 25 Vehicle speed in m/s (a) Validation for 2004 Toyota Prius

450 Calculation 400 Rolling resistance 350 Air resistance 300 Coast down test 250 200 150

Resistance force in force N Resistance 100 50 0 0 5 10 15 20 25 Vehicle speed in m/s (b) Validation for 2004 Honda Civic hybrid

Figure D.1: Comparison of calculations and coast down tests for two sedan cars (numbers for coast down tests from [65])

75 Appendix E

Auxiliary power as function of temperature

Figure E.1: Auxiliary power as function of temperature for the Nissan Leaf. 7375 driving cycles where measured throughout the United States, and the regarded temperature of 14.63 ◦C is marked with a red line [2].

76 Appendix F

Monthly temperatures for investigated cities

Table F.1: Monthly temperatures and year averages for Stockholm, Delhi and Beijing

Monthly mean temperature in ◦C for Month Stockholm [66] Delhi [17] Beijing [95] Average temperature in ◦C Jan −2.80 14.25 −3.90 2.52 Feb −3.00 17.00 −1.45 4.18 Mar 0.10 22.55 5.35 9.33 Apr 4.60 28.80 13.55 15.65 May 10.70 33.00 19.80 21.17 Jun 15.60 33.85 24.25 24.57 Jul 17.20 31.15 26.20 24.85 Aug 16.20 30.00 24.95 23.72 Sep 11.90 29.35 20.00 20.42 Oct 7.50 26.15 13.15 15.60 Nov 2.60 20.40 4.85 9.28 Dec −1.00 15.55 −1.80 4.25 Average 6.63 25.17 12.08 14.63

77 Appendix G

Simulation parameters and results

Calculation number 1 2 3 4 5 6 7 8 9 f f P P A A a a 0 0 0 max max + 15% - 15% aux aux D D + 15% - 50%

- 15%

- 15% - 30% + 15% calculation description Parameter Parameter Reference Symbol

f0 Basic rolling resistance coefficient 0.01207 0.01388 0.01026 0.01207 0.01207 0.01207 0.01207 0.01207 0.01207

fs Speed dependent rolling resistance coef. 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 2 A D Cross sectional area in m 2.89 2.89 2.89 2.89 2.89 2.4565 3.3235 2.89 2.89

c D Air drag coefficient 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 ρ Air density in kg/m3 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 m Vehicle mass in kg 1000 1000 1000 1000 1000 1000 1000 1000 1000 g Gravity constant in m/s 2 9.81 9.81 9.81 9.81 9.81 9.81 9.81 9.81 9.81 2 al Lateral acceleration in m/s 1 1 1 1 1 1 1 1 1

c occ Occupancy rate in % 0.00 % 0.00 % 0.00 % 0.00 % 0.00 % 0.00 % 0.00 % 0.00 % 0.00 %

c 1 Polynomial coefficient 1 119.96 136.92 101.4 119.96 119.96 119.16 119.16 119.96 119.96

c 2 Polynomial coefficient 2 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002 -0.002

c 3 Polynomial coefficient 3 0.597 0.597 0.597 0.597 0.597 0.508 0.686 0.597 0.597 Additional values

P aux Auxiliary power in W 1080 1080 1080 1242 918 1080 1080 1080 1080 a Acceleration rates in m/s 2.5 2.5 2.5 2.5 2.5 2.5 2.5 1.75 1.25 κ Rotating mass coefficient in % 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 s Track length in km 2 2 2 2 2 2 2 2 2

v max Maximum operational speed in m/s 10 10 10 10 10 10 10 10 10

ηprop /ηbrake Overall efficiency in % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 %

ηaux Auxiliary efficiency in % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % µ Adhesion coefficient (traction/braking) 0.255 0.255 0.255 0.255 0.255 0.255 0.2550.19/0.1750.135/0.12 Results t Trip time in seconds 204.05 204.03 204.06 204.05 204.05 204.05 204.04 205.61 208.15

Frr,max Maximum vehicle resistance force in N 178.8390 196.5970 161.0810 178.8390 178.8390 169.9390 187.7390 178.8390 178.8390 Results in kWh

E gross Gross energy in kWh 0.1999 0.2119 0.1878 0.2090 0.1907 0.1939 0.2059 0.1998 0.1998

E regen Regenerated energy in kWh -0.0111 -0.0111 -0.0112 -0.0111 -0.0111 -0.0112 -0.0111 -0.0109 -0.0105

E net Net energy in kWh 0.1887 0.2008 0.1766 0.1979 0.1795 0.1827 0.1947 0.1889 0.1894

E R Energy for rolling resistance in kWh 0.0834 0.0956 0.0713 0.0834 0.0834 0.0834 0.0834 0.0833 0.0834

E D Energy for air resistance in kWh 0.0423 0.0422 0.0423 0.0423 0.0423 0.0362 0.0483 0.0420 0.0418

E aux Auxiliary energy in kWh 0.0630 0.0630 0.0630 0.0722 0.0538 0.0630 0.0630 0.0634 0.0642 Results in kWh/seat-km

E gross Gross energy in kWh/seat-km 0.0250 0.0265 0.0235 0.0261 0.0238 0.0242 0.0257 0.0250 0.0250

E regen Regenerated energy in kWh/seat-km -0.0014 -0.0014 -0.0014 -0.0014 -0.0014 -0.0014 -0.0014 -0.0014 -0.0013

E net Net energy in kWh/seat-km 0.0236 0.0251 0.0221 0.0247 0.0224 0.0228 0.0243 0.0236 0.0237

E R Energy for rolling resistance in kWh/seat-km 0.0104 0.0119 0.0089 0.0104 0.0104 0.0104 0.0104 0.0104 0.0104

E D Energy for air resistance in kWh/seat-km 0.0053 0.0053 0.0053 0.0053 0.0053 0.0045 0.0060 0.0053 0.0052

E aux Auxiliary energy in kWh/seat-km 0.0079 0.0079 0.0079 0.0090 0.0067 0.0079 0.0079 0.0079 0.0080 Comparison of results to reference calculation ∆t Trip time 0.00 % -0.01 % 0.01 % 0.00 % 0.00 % 0.00 % 0.00 % 0.77 % 2.01 %

∆Frr,max Maximum vehicle resistance force 0.00 % 9.93 % -9.93 % 0.00 % 0.00 % -4.98 % 4.98 % 0.00 % 0.00 %

∆E gross Gross energy (kWh/seat-km) 0.00 % 6.02 % -6.02 % 4.59 % -4.59 % -3.00 % 3.00 % -0.03 % -0.01 %

∆E regen Regenerated energy (kWh/seat-km) 0.00 % -0.67 % 0.67 % 0.00 % 0.00 % 0.18 % -0.17 % -2.50 % -5.94 %

∆E net Net energy (kWh/seat-km) 0.00 % 6.42 % -6.42 % 4.87 % -4.87 % -3.19 % 3.19 % 0.12 % 0.34 %

∆E R Energy for rolling resistance (kWh/seat-km) 0.00 % 14.55 % -14.55 % 0.00 % 0.00 % 0.01 % -0.01 % -0.09 % -0.07 %

∆E D Energy for air resistance (kWh/seat-km) 0.00 % -0.08 % 0.08 % 0.00 % 0.00 % -14.27 % 14.27 % -0.58 % -1.14 %

∆E aux Auxiliary energy (kWh/seat-km) 0.00 % -0.05 % 0.06 % 14.58 % -14.58 % 0.01 % -0.01 % 0.65 % 1.88 %

Figure G.1: Input and results of the simulations (part 1)

78 Calculation number 1 10 11 12 13 14 15 16 17 25 % 50 % 75 % 100 % Track length Track length Track length Occupancy rate Occupancy rate Occupancy rate Occupancy rate v v - 50% + 50% max max - 20% + 20% calculation description Parameter Parameter Reference Symbol s s

c c c c occ occ occ occ occ

f0 Basic rolling resistance coefficient 0.01207 0.01207 0.01207 0.01207 0.01207 0.01207 0.01207 0.01207 0.01207

fs Speed dependent rolling resistance coef. 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 2 A D Cross sectional area in m 2.89 2.89 2.89 2.89 2.89 2.89 2.89 2.89 2.89

c occ Occupancy rate in % 0.00 % 0.00 % 0.00 % 25.00 % 50.00 % 75.00 % 100.00 % 0.00 % 0.00 %

c 1 Polynomial coefficient 1 119.96 119.96 119.96 128.63 138.1 147.57 157.04 119.96 119.96

c 2 Polynomial coefficient 2 -0.002 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003 -0.002 -0.002

c 3 Polynomial coefficient 3 0.597 0.597 0.597 0.597 0.597 0.597 0.597 0.597 0.597 Additional values

P aux Auxiliary power in W 1080 1080 1080 1080 1080 1080 1080 1080 1080 a Acceleration rates in m/s 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 κ Rotating mass coefficient in % 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 s Track length in km 2 1 3 2 2 2 2 2 2

v max Maximum operational speed in m/s 10 10 10 10 10 10 10 8 12

ηprop /ηbrake Overall efficiency in % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 % 81.00 %

ηaux Auxiliary efficiency in % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % 100.00 % µ Adhesion coefficient (traction/braking) 0.255 0.255 0.255 0.255 0.255 0.255 0.255 0.255 0.255 Results t Trip time in seconds 204.05 104.05 304.05 204.31 204.67 204.92 205.28 253.17 171.54

Frr,max Maximum vehicle resistance force in N 178.8390 178.8390 178.8390 188.3100 197.7710 207.2420 216.7140 157.3510 205.1030 Results in kWh

E gross Gross energy in kWh 0.1999 0.1085 0.2912 0.2076 0.2154 0.2231 0.2309 0.1940 0.2150

E regen Regenerated energy in kWh -0.0111 -0.0111 -0.0111 -0.0119 -0.0128 -0.0135 -0.0143 -0.0072 -0.0160

E net Net energy in kWh 0.1887 0.0974 0.2800 0.1956 0.2026 0.2095 0.2165 0.1869 0.1990

E R Energy for rolling resistance in kWh 0.0834 0.0426 0.1243 0.0900 0.0967 0.1032 0.1099 0.0827 0.0842

E D Energy for air resistance in kWh 0.0423 0.0218 0.0627 0.0423 0.0424 0.0425 0.0426 0.0271 0.0606

E aux Auxiliary energy in kWh 0.0630 0.0330 0.0930 0.0631 0.0634 0.0636 0.0639 0.0770 0.0540 Results in kWh/seat-km

E gross Gross energy in kWh/seat-km 0.0250 0.0271 0.0243 0.0259 0.0269 0.0279 0.0289 0.0243 0.0269

E regen Regenerated energy in kWh/seat-km -0.0014 -0.0028 -0.0009 -0.0015 -0.0016 -0.0017 -0.0018 -0.0009 -0.0020

E net Net energy in kWh/seat-km 0.0236 0.0243 0.0233 0.0245 0.0253 0.0262 0.0271 0.0234 0.0249

E R Energy for rolling resistance in kWh/seat-km 0.0104 0.0106 0.0104 0.0112 0.0121 0.0129 0.0137 0.0103 0.0105

E D Energy for air resistance in kWh/seat-km 0.0053 0.0054 0.0052 0.0053 0.0053 0.0053 0.0053 0.0034 0.0076

E aux Auxiliary energy in kWh/seat-km 0.0079 0.0041 0.0077 0.0079 0.0079 0.0079 0.0080 0.0096 0.0068 Comparison of results to reference calculation ∆t Trip time 0.00 % -49.01 % 49.01 % 0.13 % 0.30 % 0.43 % 0.61 % 24.07 % -15.93 %

∆Frr,max Maximum vehicle resistance force 0.00 % 0.00 % 0.00 % 5.30 % 10.59 % 15.88 % 21.18 % -12.02 % 14.69 %

∆E gross Gross energy (kWh/seat-km) 0.00 % 8.60 % -2.87 % 3.87 % 7.77 % 11.62 % 15.52 % -2.91 % 7.56 %

∆E regen Regenerated energy (kWh/seat-km) 0.00 % 100.00 % -33.33 % 7.24 % 14.43 % 21.57 % 28.65 % -35.74 % 43.30 %

∆E net Net energy (kWh/seat-km) 0.00 % 3.21 % -1.07 % 3.67 % 7.38 % 11.04 % 14.75 % -0.97 % 5.45 %

∆E R Energy for rolling resistance (kWh/seat-km) 0.00 % 2.03 % -0.68 % 7.85 % 15.87 % 23.71 % 31.72 % -0.85 % 0.94 %

∆E D Energy for air resistance (kWh/seat-km) 0.00 % 3.13 % -1.04 % 0.06 % 0.44 % 0.49 % 0.88 % -35.93 % 43.35 %

∆E aux Auxiliary energy (kWh/seat-km) 0.00 % -47.63 % -1.58 % 0.24 % 0.71 % 0.94 % 1.41 % 22.27 % -14.23 %

Figure G.2: Input and results of the simulations (part 2)