Master Thesis MSc Supply Chain Management “Preparing for a zero-emission fleet at RET”

Student: Coach: Co-reader: Guillermo A. Beuchat Beroiza Dr. Pieter van den Berg Dr. Jan van Dalen Student number: 450432 [email protected] [email protected] [email protected]

June 15th 2017

“Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Preface

The copyright of this Master Thesis rests with the author. The author is responsible for its contents. RSM is only responsible for the educational coaching and cannot be held liable for the content.

This Thesis was written with the valuable collaboration of Planning Department of RET. They provided knowledge, data, and very detailed feedback during the writing process. Also, guidance from the Coach and the Co-reader and their feedback as part of the academic community was a key part of the process.

Executive Summary

RET provides public transport services in Rotterdam Metropolitan Region, operating Metro, Tram, and Bus networks. RET has a fleet of 278 buses running on diesel fuel, and in the following years, they will replace the diesel fleet with Zero-emission buses.

The Planning Department of RET is responsible for many activities of the Planning Process. One of the most important activities of the process is designing the vehicle schedules, which contain a detailed plan of all the trips that shall be executed by the bus fleet to fulfill the transport service. The current vehicle schedules are designed to be executed by the diesel fleet. The Planning Department of RET believes that moving to a Zero- emission fleet may have an impact on the Planning Process, since the vehicle schedules generated may be impossible to execute using Zero-emission buses given the technologies available in the market.

This thesis attends the above-mentioned problem by focusing on two objectives: to identify the aspects related to a Zero-emission fleet that may have an impact on the Planning Process, and to quantify those impacts on the vehicle schedules. To achieve both objectives, this thesis stands over a previous research conducted by the Planning Department of RET, in which a model was developed.

To identify the aspects related to a Zero-emission fleet that may have an impact on the Planning Process and were not considered in the previous model from RET, a literature review was conducted. This thesis found that the relevant aspects to be considered were those which impact the energy consumption, charging time and range of the bus. A relevant aspect which has a big impact on energy consumption is the Heating, Ventilation, and Air Conditioning unit. Studies suggest that these units can account for up to 40% of the total energy consumed by the vehicle. Another aspect is related the characteristics of the route. Contrasting results were found in literature regarding this topic; while some studies suggest that suburban routes consume much more energy than urban routes (differences as big as 50%), other studies suggest that there is no difference at all. Another aspect is the location, power output and capacity of charging stations, which is relevant for the range and charging time. Also, preparation for a Zero-emission fleet should consider being able to handle queues in charging stations and establishing certain rules for charging-related decisions.

To quantify the impact of the Zero-emission aspects on the vehicle schedules, the model developed by RET Planning Department has been enhanced by incorporating the Zero-emission aspects found in the literature review. Many studies focus on measuring the impact of these aspects on energy consumption, but only a few of them analyze the impact on vehicle schedules. The approach used by the latter consists of incorporating the Zero-emission conditions as variables and constraints into the schedule generation problem. In contrast, the approach followed by this thesis consists of evaluating the current schedules under the conditions imposed by the Zero-emission fleet.

2 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Vehicle schedules contain several trips to be executed by vehicles. These trips are organized using “blocks”, which are sets of trips to be executed by one vehicle. To evaluate the vehicle schedules under Zero-emission conditions, a simulation tool was developed and a set of “performance indicators” was proposed to assess the results. The main indicator is the number of “feasible blocks”, where “feasibility” is determined by the state of charge of the batteries of the vehicle. If a block of the vehicle schedule pushes the battery level below certain predefined parameter, the block is considered “infeasible”.

This study simulated a vehicle schedule with 1540 trips, allocated in 128 blocks. A base scenario with reasonable initial values based on literature was set, which resulted in 83 feasible blocks. Then, parameters were modified to measure their impact on the number of feasible blocks.

The Heating, Ventilation and Air Conditioning unit has a great impact on the schedule. If switched on, the number of feasible blocks decreases from 83 to only 28 blocks. The characteristics of the route are also relevant. If there is no difference between energy consumptions of urban and suburban, and the consumptions are close to 1 [kwh/km] (as observed in some studies), the number of feasible blocks increases to 124 (97% of the total blocks).

Other aspects were found to have a great impact as well. Size of the battery pack is critical, since installing batteries of 50 [kwh] capacity makes all the blocks infeasible, while batteries of 400 [kwh] allow 124 feasible blocks. Also, power output of the charging stations can make a big difference. If stations provide less than 150 [kw] of power, buses are not able to charge during the night and may begin the next day without full charge. When power is above 300 [kw], almost no improvement is observed in the number of feasible blocks.

Other aspects turned out to be less significant from the planning perspective. For example, the charging technology chosen and the addition of charging stations in certain places made almost no difference in the feasibility of blocks.

This thesis concludes that the current vehicle schedules of RET are not completely feasible under Zero- emission conditions, so modifications are required. It identifies some aspects of the Zero-emission fleet which have a big impact on the feasibility of the blocks and therefore should be considered by the Planning Department to adapt the vehicle schedules.

3 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Index

Preface 2 Executive Summary 2 Index 4 Figures Index 6 Tables Index 8 Abbreviations used 9 Units used 9 1. Introduction 10 Objectives and structure 11 2. Context description 11 2.1. Zero-emission technologies 11 2.1.1. Zero-emission technologies for buses 11 2.1.2. Support equipment technology 13 2.2. Experiences with Zero-emission bus fleets in other cities 16 2.3. Current fleet at RET 16 2.4. The Planning process in RET 17 2.5. Model developed by RET for Zero-emission project 19 3. Identifying relevant Zero-emission aspects not included in the current model (Phase I) 21 3.1. Heating, Ventilation, and Air Conditioning units (HVAC) 21 3.2. Characteristics of the route (driving cycle) 23 3.3. Other parameters 28 3.3.1. Efficiency of energy transfer during charging process 28 3.3.2. Transmission system 30 3.3.3. Regenerative braking 30 3.3.4. Queues handling 31 3.4. Overview of Enhanced model 31 4. Quantifying the impact of relevant Zero-emission aspects on schedules (Phase II) 33 4.1. Proposed methodologies to incorporate relevant Zero-emission aspects into the enhanced model 33 4.1.1. Methodology to incorporate HVAC unit into the enhanced model 33 4.1.2. Methodology to incorporate the driving cycles into the enhanced model 33 4.1.3. Methodology to incorporate the charging stations efficiency into the enhanced model 36

4 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 4.2. Simulation tool overview 36 4.3. Calculation of performance indicators for the schedule provided 37 4.4. Assumptions of the simulation 38 4.5. Charging rules 39 5. Impacts of Zero-emission aspects on vehicle schedules 39 5.1. Base run settings 40 5.2. Base run results 41 5.3. Impact of modifying the “Layover Time for Charging” parameter 43 5.4. Impact of deleting charging stations. 44 5.5. Impact of HVAC unit on schedule 46 5.6. Impact of consumptions of driving cycles on schedule 47 5.7. Impact of charging stations power on schedule 48 5.8. Impact of charging stations power on schedule 51 5.9. Impact of battery pack size on schedule 51 6. Conclusions 53 6.1. Main findings in Phase I 53 6.2. Main findings in Phase II 53 6.3. Discussions 55 6.4. Recommendations and follow-up research 55 References 57 Appendix 1: Current fleet of RET 61 Appendix 2: Inputs of the simulation tool 63 Implementation of enhanced model in MSEXCEL 63 “Parameters” worksheet 63 “ConsumptionPerRoute-Pattern” worksheet 64 Vehicle schedule in MSEXCEL format 65 Appendix 3: VBA program of the simulation tool 67 Development of simulation program in VBA. 67 Appendix 4 Outputs of the simulation tool 70 “Result_VehView” worksheet 70 “Result_ChgView” worksheet 71 “Performance” worksheet 72 “Graphs_VehView” worksheet 75

5 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figures Index

Figure 1: Pictures of the most common charging technologies 14 Figure 2: HFC bus in London (left). HFC bus being refilled with a hydrogen pump (right) in Aberdeen. 15 Figure 3: Conceptual summary of the PP 18 Figure 4: Conceptual structure of a vehicle schedule 19 Figure 5: Consumption in [kwh/km] due to different aspects of an electric bus fleet (Jobson 2015) 21 Figure 6: Energy consumed by electric bus operation with Air Conditioning system ON and OFF (Suh et al. 2014) 22 Figure 7: Energy consumed by type of bus for different average speeds based on (Lajunen 2013a) 23 Figure 8: Example of a test run containing the three driving cycles (Altoona 2015a). 25 Figure 9: Three electric bus models tested in Altoona (2014), (2015a), and (2015b) respectively. 25 Figure 10: Speed versus distance plots for SORT driving cycles as described in Goethem et al. (2013). 27 Figure 11: Speed versus time for MVEG-B cycle, as described by Goethem et al. (2013). 27 Figure 12: Energy used to charge Proterra BE40 and New Flyer XE40, based on (Altoona 2014) and (Altoona 2015a) 29 Figure 13: Structure of the simulation tool built in MSEXCEL. 37 Figure 14: Settings of base run of simulation tool. 40 Figure 15: Map of base run simulation, made of 30 screenshots from “Result_VehView” worksheet with 10% zoom. Each column represents a block (128 in total). Time begins at 5:21 (top of map) and ends at 5:20 of the next day (bottom of map). 41 Figure 16: Detail active blocks per status per minute for Base run 42 Figure 17: Modifications to base run parameter “Layover Time for Charging” for runs 2-7. 43 Figure 18: Impact of “Layover Time for charging” parameter on feasibility of blocks and trips. 43 Figure 19: Comparison of detail of active blocks per status, for different values of “Layover Time for charging”. 44 Figure 20: Deletion of 9 charging stations from Base run for Run 8. 45 Figure 21: Impact of deleting 9 charging stations considered by RETPD as less likely to be available. 45 Figure 22: Modifications to base run parameter “HVAC penalty” for runs 9-12. 46 Figure 23: Impact of the HVAC unit on the feasibility of the vehicle schedule. 46 Figure 24: Detail of active blocks per status for base run (HVAC penalty of 0%) and run 12 (HVAC penalty of +40%). 47 Figure 25: Power in [kw] required by base run and run 12 at every one of the 1440 simulated minutes. 47 Figure 26: Modifications to parameters for runs 16 to 22. 47

6 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Figure 27: Impact of equal consumptions between driving cycles on feasibility of blocks 48 Figure 28: Detail of active blocks per status for base run and runs 16, 19 and 22. 48 Figure 29: Modifications to power output of base run for runs 23-29. 49 Figure 30: Impact of the power output of charging stations on the feasibility of the vehicle schedule. Note that Base run (highlighted) is located between run 25 and run 26. 49 Figure 31: Power in [kw] required for Runs 29, 28, 27, 26, Base, 25, 24 and 23. 50 Figure 32: Detail of active blocks per status for base run and runs 24, Base, 27 and 22 50 Figure 33: Modifications to setup charging time of base run for runs 30, 31 and 32. 51 Figure 34: Impact of the setup charging time of charging stations on the feasibility of the vehicle schedule. 51 Figure 35: Modifications to setup charging time of base run for runs 32 to 40. 52 Figure 36: Impact of the setup charging time of charging stations on the feasibility of the vehicle schedule. 52 Figure 37: Overview of the “Parameters” worksheet from simulation tool developed in MSEXCEL. 63 Figure 38: Four areas of the “Parameters” worksheet from simulation tool developed in MSEXCEL. 63 Figure 39: Explanation of a “Minimum Layover” set at 2 [min] and a “Layover Time for Charging” set at 17 [min]. 64 Figure 40: Screenshot of an example of “ConsumptionPerRoute-Pattern” worksheet. 65 Figure 41: Screenshot of a vehicle schedule exported to MSEXCEL format. 66 Figure 42: Pseudocode of the main function run by the simulation program. 69 Figure 43: Screenshot of “Result_VehView” with zoom level of 25%. 70 Figure 44: Details on color formatting in “Result_VehView” worksheet. 71 Figure 45: Partial screenshot of “Result_ChgView” worksheet. 72 Figure 46: Example of block showing active period and feasible and clean trips. 73 Figure 47: Screenshot of “Performance” worksheet. 74 Figure 48: Example of first chart, showing SOC per minute per block, for a small simulation of four blocks. 75 Figure 49: Example of second chart, showing total power required per minute at all the charging stations. 75 Figure 50: Example of third chart, showing number of active blocks per minute. 76 Figure 51: Example of fourth chart, showing detail of active blocks per minute opened by status group. 76

7 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Tables Index

Table 1: Summary of ZE bus technologies 12 Table 2: Examples of four possible ZE technologies combinations (“C1”, “C2”, “C3” and “C4”) for BEB. Large battery packs suit well with low power chargers, while small battery packs suit well with high power chargers. 16 Table 3: Glossary with key concepts used to describe the planning process 17 Table 4: Electric parameters in the model developed by RETPD. 20 Table 5: Three driving cycles used by Thomas Larson Pennsylvania Transportation Institute (Altoona 2015a) 24 Table 6: Average energy consumption per driving cycle for BYD Electric Bus, Proterra BE40 and New Flyer XE40, based on measurements by Altoona (2014), (2015a), and (2015b) respectively. Highest consumptions are observed for “ART” driving cycle. 26 Table 7: Length, MBC fraction and energy consumption for six routes in Ohio State University (Filippo et al. 2014) 26 Table 8: Numeric details of the SORT cycles as described in Goethem et al. (2013). 27 Table 9: Energy consumption measured on the test over a 12 [m] long bus as shown in Goethem et al. (2013). 28 Table 10: Energy consumption for different types of transmission as shown in (Wang et al. 2016) 30 Table 11: Causes of energy losses in electric cars and energy recovered by regenerative brakes (Gonzalez 2016) 31 Table 12: Summary of aspects to be considered for the enhanced model by RETPD 32 Table 13: Methodology proposed for Route assessment on driving cycle. 33 Table 13: Steps 1, 2 and 3 of methodology to incorporate driving cycles into the enhanced model. 34 Table 14: Step 4 of methodology to incorporate driving cycles into the enhanced model. 34 Table 15: Steps 5, 6 and 7 of methodology to incorporate driving cycles into the enhanced model. 35 Table 16: Step 8 of methodology to incorporate driving cycles into the enhanced model. 35 Table 17: Arbitrary initial values for speed range, distance range and consumptions of driving cycles correspond to “worst case scenario” from Altoona (2014), (2015a) and (2016). 36 Table 18: Basic elements required to calculate performance of vehicle schedules under certain settings. 37 Table 19: Performance of first run of simulation tool (base run). 42 Table 20: Summary of most important arrays used by the simulation program to store data. 67 Table 21: Eight possible status for a block at any minute of the simulation. 68

8 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Abbreviations used

AC: Air Conditioning AMT: Automatic Mechanical transmission ART: Arterial drive cycle BEB: Battery Electric Bus CBD: Central Business District drive cycle COM: Commuter drive cycle HEB: Hybrid Electric Bus HFC: Hydrogen Fuel Cell HVAC: Heating, Ventilation, Air Conditioning KPI: Key Performance Indicator LEV: Low-emission vehicle OBL: Objective Battery Level (in [%]) PD: Planning Department PP: Planning Process RETPD: RET’s Planning Department RETPP: RET’s Planning Process RP: Route-Pattern SOC: State of Charge of batteries (in [%]) TP: Timing Point ZE: Zero-emission ZEV: Zero-emission vehicle

Units used

Battery capacity: [kwh] = Kilowatt-hour Consumption rate [kwh/km] = Kilowatt-hour per kilometer Charging rate [kwh/min] = Kilowatt-hour per minute Distance: [km] = Kilometer Distance: [m] = Meter Energy: [kwh] = Kilowatt-hour Number of passengers: [pass] = Passengers Power: [kw] = Kilowatt Speed [km/hr] = Kilometers per hour Speed [km/min] = Kilometers per minute Temperature: [C] = degrees Celsius Time: [yr] = Year Time: [hr] = Hour Time: [min] = Minute Time: [sec] = Second Weight: [g] = Gram Weight: [kg] = Kilogram Weight: [ton] = Tons

9 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 1. Introduction

RET is the main provider of public transport within Rotterdam metropolitan region. RET operates buses, metro, trams, and ferries, and moves in total around 600,000 passengers per day. In the coming years, RET is moving towards a Zero-emission (ZE) bus fleet which will replace the current diesel fleet. Based on previous knowledge and information from ZE bus manufacturers, RET believes that the mentioned replacement will have an impact on its planning process (PP).

The PP includes many activities, most of them executed by the Planning Department (PD) from RET. Specifically, the PD is responsible for the design of timetables, vehicle schedules, crew schedules and crew rosters, to make sure that the transportation service is executed properly. More details about the PP are given in Section 2.4. When designing the schedules, the PD works under conditions related to the diesel bus technology. For example, PD knows that a full tank of diesel gives enough range to a bus to complete a whole day driving without the need to refuel. They assume that refueling time is negligible compared to the travelling time, and they know that refueling can be done in every garage during the night. It is believed that migrating to a ZE fleet, given the available technologies in the market, will impose new conditions and constraints which may make the current vehicle schedules infeasible. For example, some ZE buses have much less range then diesel buses and charging may take a long time; so long that the new fleet cannot meet the current schedules without interruptions.

Many studies have been done on zero-emission vehicles (ZEV) which are relevant to the topic of this thesis. McCarthy and Yang (2009) and Ananthachar and Duffy (2004) give insights on the key characteristics that make ZEV different from diesel: energy consumption and total range. Some aspects of ZE technology may have a big impact on energy consumption: Hegar et al. (2012) and Suh et al. (2014) measure the impact of Heating, Ventilation, and Air Conditioning units onboard (HVAC), while EPA (2007), Lajunen (2013b) and Perrotta et al. (2014) establish a direct relation between consumption and sinuosity of the routes (related to average speed and number of stops). Furthermore, Altoona (2014), Altoona (2015a) and Altoona (2015b) measure the actual energy consumption and range of three different electric bus models on routes characterized by different average speeds and different distances between stops.

Also, there is literature regarding the main transport planning problem with ZE conditions. Chao and Xiaohong (2013) describe a case study from the city of Shanghai, and develop a multi-objective optimization problem that considers electric bus conditions (through adding constraints and variables). In the same way, Qin et al. (2016) and Mohamed et al. (2016) also include the queuing phenomena observed at charging stations.

It is important to notice the difference between the impact of ZE aspects on energy consumption and the impact of ZE aspects on the PP, particularly on vehicle schedules (one of the main outputs of the PP). It may happen that some aspects impact the energy consumption, but the current vehicle schedules are designed in such way that they are feasible anyway and there is no need to modify them. Studies mentioned above (explained with more details in Section 3) focus on the impact of ZE aspects on energy consumption but not on vehicle schedules. A few studies try to quantify the impact of ZE aspects on schedules, but they do it incorporating such aspects as variables and constraints of an optimization problem to generate new ZE-proof schedules. This thesis uses a different approach; it takes the current vehicle schedules generated under diesel conditions and evaluates their feasibility under the new ZE conditions.

10 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Objectives and structure

This thesis attends two research objectives: to identify the ZE aspects that may have an impact on the PP of Rotterdam bus fleet and to quantify those impacts to assist the PD on the schedules design. To attend the objectives, this thesis is divided in two phases. Phase I identifies the relevant ZE aspects, and Phase II quantifies the impact of those aspects on the PP.

Phase I aims to answer the following questions: What characteristics of the ZE buses have significant impact on energy consumption, charging times and range of the vehicles? What characteristics of the routes and the environment have significant impact on energy consumption, charging times and range of the vehicles? What parameters should be considered for ZE planning? How should those parameters be incorporated in the PP?

Method for Phase I consists of a literature review, described in Section 3.

Phase II aims to answer the following questions: Are the current schedules feasible given the ZE aspects? Why and when do the current schedules become infeasible given the ZE aspects? How sensitive are the schedules to each ZE aspect?

Method for Phase II consists of a simulation, which measures the impact of the relevant aspects (found in Phase I) on the feasibility of current vehicle schedules. The simulation is described in Section 4.

Main results of this thesis are presented in Section 5. Finally, Section 6 summarizes the findings and recommendations for RETPD. It also gives general suggestions applicable to other cities.

2. Context description

2.1. Zero-emission technologies

The European Commission defines “Low-emission vehicle” (LEV) as vehicles having tailpipe emissions below 50 g/km (some plug-in hybrids belong to this category), and “Zero-emission vehicle” (ZEV) as vehicles which produce no tailpipe emissions at all (EU 2016). In general pollutants are classified in two groups: “criteria” pollutants (which are regulated by the “Clean Air Act” (EPA n.d.a)) and “non-criteria” pollutants (which are not regulated by CAA but are commonly considered as harmful for the environment and human health) (AFDC n.d.). There are six defined “criteria” pollutants: Carbon Monoxide (CO), Ozone (O3), Lead, Nitrogen Oxides (NOx), Sulfur Oxides (SOx) and Particulate Matter (PM). These pollutants are very toxic and can produce serious harm to human health. The “non-criteria” pollutants include Carbon Dioxide (CO2), Organic gases, Methane, photoreactive organic compounds and other toxic gases (EPA n.d.b). Some vehicles emitting substances like water vapor or pure hydrogen are considered as ZEV if they do not emit tailpipe pollutants (Ananthachar and Duffy 2004).

2.1.1. Zero-emission technologies for buses

When referring to ZEV in general, the most popular technology is human-powered vehicles (e.g. bicycles). Although, when referring to passenger vehicles (like urban buses) human-powered vehicles are not a feasible alternative. In the last decades, a lot of research has been done by private companies, governments, universities, and NGOs aiming to develop ZE technologies for passenger vehicles. There are two technologies

11 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] that have proven to be successful real ZE alternatives for combustion engines: Full electric buses and Hydrogen Fuel Cells (HFC) buses. There are some other technologies that have been developed but are still not real ZE alternatives for diesel engines in a large scale, due to their high costs, low efficiencies or simply because of their too early stage of development. Solar buses and the “Gyrobus” are some examples.

See Table 1 for a summary of the most common ZE bus technologies available. Even the technologies are shown as alternatives, it is important to notice that some of them are closely related. For example, a Plugin Hybrid Electric Bus can work as a BEB when the diesel generator onboard is kept turned off. A Trolleybus could be considered as BEB if a small battery or capacitor is installed, which allows short unplugged drives to connect two routes or streets.

Table 1: Summary of ZE bus technologies Technology Emissions Propulsion Energy storage device? Comments on energy source Bycicle Zero Emission Human powered No (Non-autonomous)

Battery Electric Bus (BEB) Zero Emission Full electric Yes (Autonomous) Plug or connection for battery charging from external source.

Capabus Zero Emission Full electric Yes (Autonomous) Plug or connection for capacitor charging from external source.

Gyrobus Zero Emission Full electric Yes (Autonomous) Plug or connection for flywheel charging from external source.

Trolleybus Zero Emission Full electric No (Non-autonomous) Permanent phisical connection to power line (using a pantograph).

Online Electric Bus Zero Emission Full electric No (Non-autonomous) Permanent wireless connection to power line (using electromagnetic induction).

Hydrogen Fuel Cell bus Zero Emission Hydrogen Fuel Cell Yes (Autonomous) Fuel cell onboard generates electricity from chemical reaction using Hydrogen stored in tanks, usually above the ceiling.

Plugin Hybrid Electric Bus Low Emission Diesel Hybrid Yes (Autonomous) Batteries can be charged from diesel engine with alternator or from plug. If engine is switched off, it can work as a BEB.

Hybrid Electric Bus Low Emission Diesel Hybrid Yes (Autonomous) Batteries can only be charged from diesel engine with alternator.

Full electric buses use one or more electric motors for propulsion and do not generate the electric energy onboard (like, for example, hybrid diesel buses, which are not considered ZEV). Electric buses can be classified in two groups depending on their ability to store energy: Autonomous and Non-autonomous. Autonomous buses, on one hand, have onboard storage devices so they do not need permanent connection to external sources. Examples are: Battery electric bus (BEB) (loaded with lithium-ion batteries) and the “Capabus” (loaded with capacitors for energy storage). Non-autonomous buses, on the other hand, do not have energy storage devices onboard. They must be permanently connected to an external energy source. Examples are: Trolleybus (connected to a physical wire above the road through a pantograph) and Online Electric Vehicles (connected wirelessly through electromagnetic induction to a high frequency power line buried under the road).

HFC buses use one or more electric motors for propulsion and generate the electric energy onboard using a “Fuel Cell” which works on compressed liquid hydrogen. They may sometimes have batteries or capacitors

12 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] onboard for energy storage and backup. An HFC is a device that converts oxygen and hydrogen fuel into electricity, heat, and water, through chemical reactions. Oxygen is taken from the air and hydrogen must be externally sourced. Usually HFC buses are loaded with liquid hydrogen tanks.

Even though HFC buses are considered as ZEV, the process of manufacturing liquid hydrogen tanks consumes a lot of energy and may involve carbon emissions. Some experts consider HFC powered vehicles as ZEVs that simply push the emissions upstream in the production chain, so the final net environmental impact is not as clean as it claims considering the whole lifecycle (McCarthy and Yang 2009). The same debate occurs for BEB, since many countries still generate part of their energy burning oil or coal. Some positions consider ZEVs as such only when the energy consumed comes from renewable non-carbon sources (i.e. solar, wind, hydro, geothermal).

2.1.2. Support equipment technology

Support equipment refers to the equipment outside the vehicle required to make ZE fleet possible, for example: charging stations, infrastructure, road modifications, storage facilities, power lines, etc. Many support technologies have been developed for charging and refueling processes. Some of them suit better to certain bus technologies, so they must be considered and analyzed together.

For non-autonomous electric buses, the most common technology is the wired power source connected to the bus through a pantograph or inverted pantograph (Trolleybus). We will not focus on this kind of technology in this thesis.

For autonomous electric buses, we have identified five different technologies classified in two groups: “overnight charging” and “opportunity charging”.

Overnight charging consists of only one long charge sourced from a low-power electricity output (15 [kw]), to which the bus is plugged during the night. Many overnight charging stations can be installed together in “charging depots” where buses go after the last trip of the day. The buses then can stay there for about 6 hours until fully charged (depending on power of outlets and battery capacity). In the meanwhile, they cannot be used and stay out of service.

Opportunity charging consists of many quick charges throughout the day, which can be accomplished using different technologies such as Fast charging, In-motion charging, Inductive charging, and Conductive charging.

In Fast charging, energy is provided by a high-power charging station (between 50 [kw] and 150 [kw]), and takes less than 10 minutes to charge depending on the battery capacity. (SIEMENS 2015). This allows to charge the buses many times during the day and increases the range of the bus.

In-motion charging provides energy using wires above the road, connected to the bus through a pantograph (similar to trolleybuses) but only in some streets or sections of streets. The batteries are charged from the wire and give an autonomous range of about 10 [km] to the bus.

In Inductive charging, energy is provided wirelessly in bus stops. Over the pavement, a metal plaque or coil is installed and connected to electrical power. Beneath the bus floor, a similar plaque is connected to the batteries. When the bus arrives at the stop, the tension in the pavement’s plaque produces an electric current

13 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] in the bus’s plaque via electromagnetic induction, which charges the batteries. The power capacity for this technology varies between 60 [kw] and 300 [kw] (EMOSS n.d.).

In Conductive charging, energy is provided at every bus stop (or even every two stops). Very high power charging stations (between 500 [kw] and 1000 [kw]) can provide in only 15 seconds enough energy to allow the bus reach the next stop (probably 1 [km] or 2 [km] away). When the bus arrives at the station a robotic arm (or sometimes a pantograph or reversed pantograph) plugs the bus to the very high power source (Graham 2013).

Figure 1 shows pictures of the the most common charging technologies.

Overnight Charger In Motion Charging Fast Charging

Inductive Charging Conductive Charging Figure 1: Pictures of the most common charging technologies

Finally, regarding HFC buses, liquid hydrogen tanks must be kept above the bus’s roof and are refilled with a special pump similar to a diesel pump. It takes less than 10 [min] to refill the tanks with hydrogen (CHIC 2016). Figure 2 shows a picture of a HFC bus in London, where the hydrogen tanks on the roof are easily observable. It also shows the exact moment when a HFC bus is being refilled with hydrogen in Aberdeen. The refueling stations and the storage depots for hydrogen tanks must meet several security requirements and may involve some infrastructure investments.

14 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Additionally, most of the autonomous ZEVs have integrated regenerative brakes. This technology allows to recover part of the kinetic energy released when the vehicle decreases its speed or stops completely (due to conservation of energy principle) and recharge the batteries with such energy.

It is important to notice that some of these charging technologies may work better combined. For example, one may have Inductive Charging stations across the city but also Overnight Charging stations in central depots. It may be more efficient to implement a combined technology than a pure one.

Figure 2: HFC bus in London (left). HFC bus being refilled with a hydrogen pump (right) in Aberdeen.

For BEB there is a direct relation between the size of the battery packs onboard and the charging technology chosen. The larger the battery size, the lower charging frequency and power is required. One extreme alternative would be installing very large battery packs on the vehicles (enough for a whole day operation without recharge) which require a few low-powered overnight chargers, and another extreme alternative would be installing very small battery packs (or even capacitors) which require many high-power conductive chargers along the route. The best decision for a particular fleet may be somewhere in between these extremes, depending on other factors (like topography of the city, energy availability, operation times or budget).

Table 2 shows examples of possible combinations of technologies that suit well together for BEB, considering battery size, charging technology, charging power, required charging frequency, charging time and range. If a fleet with small battery was chosen, for instance, it is certainly not convenient to install only low-powered overnight chargers, because the small battery requires frequent charging which could take too long on a low- powered charger, reducing dramatically the working time of the buses and the overall efficiency of the system. If a large battery was chosen instead, it is certainly not convenient to install high-powered chargers along the routes, because the large battery stores enough energy for a whole day running and the chargers are hardly used during the day.

15 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 2: Examples of four possible ZE technologies combinations (“C1”, “C2”, “C3” and “C4”) for BEB. Large battery packs suit well with low power chargers, while small battery packs suit well with high power chargers. Charging Suitable size of Range with full charge Charging frequency Charger power Charging time technology battery pack Low power Large Whole day Low Long C1 Overnight charging (10 [kw] to 40 (250 [kwh]) (250 [km]) (once per day) (5 hours) [kw]) Moderate power Fast charging + Mid-size Half day Moderate Moderate C2 (50 [kw] to 150 Overnight charging (120 [kwh]) (100 [km]) (2 to 3 times per day) (1 to 2 hours) [kw]) High power Fast charging + Small One or two trips High Short C3 (60 [kw] to 300 Inductive charging (50 [kwh]) (50 [km]) (5 to 6 times per day) (10 minutes) [kw]) Very high power Conductive Very small One or two stops Very high Very short C4 (500 [kw] to 1000 charging (capacitor) (5 [km]) (every 2 stops) (10 seconds) [kw])

2.2. Experiences with Zero-emission bus fleets in other cities

Corazza et al. (2016) warn on their work about the real incentives for bus stakeholders in European experiences. Even though the environmental concern drives most of the green test, it is still not a powerful enough incentive to scale the tests. Miles and Potter (2014) describe the experience of the British town of Milton Keynes, where one line was completely migrated to electric technology. A remarkable experience in Europe was the CHIC plan. CHIC is a consortium of 23 partners from 8 European countries, which implemented a total of 62 HFC buses in Aargau (CH), Bozen (IT), London (UK), Milan (IT), Oslo (NO), Cologne (GE), Hamburg (GE) and Berlin (GE). A full report with key facts, results and recommendations was published (CHIC 2016). Chao and Xiaohong (2013) describe an experience in Shanghai where battery exchanging was used. Perrotta et al. (2014) describe an interesting experience in Oporto (Portugal) where they tested routes with different characteristics. Filippo et al. (2014) use simulations methods to test feasibility of replacing diesel buses with electric buses, and study the case of a 22 buses fleet used in the campus of Ohio State University.

2.3. Current fleet at RET

The current bus fleet of RET consists of 278 buses of which 274 are powered by regular diesel engines and four are powered by diesel hybrid systems (which are equipped with batteries and electric motors) (RET n.d.). Even though diesel hybrid technology is not considered as ZE, RET has some charging stations available for these hybrid vehicles and therefore has acquired some experience in battery performance and charging times. If the routes are properly planned and short trips are assigned to the hybrids, they can run entirely on electricity and turn on the diesel engine only in unplanned shortages or if some situation requires an extra boost of power. Appendix 1 shows the current fleet from RET and specifies some basic characteristics (manufacturer, capacity, propulsion system).

In addition to the bus fleet, RET operates four garages (where refueling is done) and one maintenance facility. The service executed by RET consists of 72 bus lines, from which 58 run during the day and 14 run during the night. They cover many different areas within Rotterdam Metropolitan Region (including suburbs and rural areas), so routes differ a lot in length, driving time and number of stops. The total travelled distance by the whole bus fleet is around 15 Million kilometers per year ([km/yr]). The bus service moves around 30 Million passengers per year ([pass/yr]) which add up around 114 Million passenger-kilometers per year. The energy

16 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] consumption for the whole bus fleet is estimated 0.55 kilowatt-hour per passenger-kilometer ([kwh/pass- km]). The CO2 emissions coming from the bus fleet are estimated 165.9 grams per passenger-kilometer ([g/pass-km]) (RET n.d.).

2.4. The Planning process in RET

The PP is divided in six main activities or steps: Initial setup (optional), Trip creation, Vehicle Scheduling, Crew Scheduling, Crew Rostering, and Daily Assignments. The Crew Scheduling, Crew Rostering and Daily Assignment activities do not concern this thesis. The main outputs of the PP are the timetables containing trips, the vehicle schedules containing blocks, the crew schedules containing duties and the crew rosters. The PD in RET is responsible for the design of the mentioned outputs, to make sure that the service is executed properly.

For better understanding of the PP, Table 3 shows a glossary containing key concepts used in explanations.

Table 3: Glossary with key concepts used to describe the planning process CONCEPT EXPLANATION GARAGE Site where buses spend the night and are maintained and refueled. There are four Garages in Rotterdam operated by RET. STOP Site where there is a bus stop. Distance in kilometers is known between pairs of Stops. TIMING Important Stop in which the arrival time of each bus is registered. Travel time in minutes PLACE is known between pairs of Timing Places. ROUTE Also called “Line”, is an ordered set of Timing Places and Stops. Each Route is defined to belong to a specific Garage. TRIP Represents the physical movement of a vehicle along multiple Stops belonging to the same Route, at a certain time in a certain direction. PATTERN Each Trip, has a unique Pattern in it, which defines a unique way in which the whole Route or part of it can be covered by the Trip. LAYOVER Buffer time at the end of every Trip. A “Minimum Layover Time” is defined by RET to TIME handle possible delays due to traffic or other external uncertainties. BLOCK Ordered set of Trips which can be executed by a vehicle. Blocks are assigned to actual vehicles daily in Step 5 of the PP (out of scope of this thesis). INTERLINING Condition of the model which allows a Block to contain Trips covering different Routes. Since Blocks are assigned to actual vehicles later, Interlining means that a single vehicle can cover many Routes in the same day. As Routes belong to only one Garage, it is highly likely that Interlining occurs between Routes from the same Garage. TIMETABLE Output of Step 1 of PP. It contains all the Trips created by PD, which are used to generate a Vehicle Schedule. VEHICLE Output of Step 2 of PP. It contains the optimal allocation of Trips into Blocks, given the SCHEDULE Timetable, parameters and adjustments provided by PD. PIECE OF Set of one or more Trips belonging to a Block, to be covered by a Duty. WORK

17 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

DUTY Set of Pieces of Work that complete a whole workday of a bus driver. One Duty is assigned to one actual driver daily in Step 5 of the PP (out of scope of this thesis). CREW Set of Duties, output of Step 3 of PP. SCHEDULES

Figure 3 shows a conceptual summary of the six main activities of the PP and highlights which of the activities are in scope of this study.

Figure 3: Conceptual summary of the PP

Step 0 is only executed if necessary. Actions included in this step are the adjustments and/or creation of new routes, adjustments on driving time between timing places (for example, significant change in traffic) or adjustments on frequency of service (due to significant change in demand). If there is no need for Step 0, the existing setup is used for the following steps.

In step 1, the planner creates all the required trips, providing initial and final stops and times. The output is a timetable. Note that trips can cover complete routes or only parts of them. Different directions within the same route are identified using different patterns. There may be “roundtrips”, where initial stop is the same as final stop and only one direction is covered. Enough Trips are created to meet the service level of the route (frequency).

In step 2, the Planning Department generates blocks containing trips from the timetable. The output is a vehicle schedule. Figure 4 shows the conceptual structure of a vehicle schedule. Basically, it consists on a grid, where each row is a block and each column represents a period of time. Trips are allocated in the grid, according to their initial and final time. Each trip is performed at a specific time, so trips cannot overlap in the same block (as a block is executed by a real vehicle). In the example of Figure 4, the trip called “TRIP1-5” is the fifth trip of block 1, and covers the route 100 driving the pattern 11. The periods of time between trips within a block are called “Layover time”.

It is common to observe some trips “paired” to other trips, covering different patterns of the same route. Using the example in Figure 4, the trip “TRIP1-2” (in block 1) follows immediately after “TRIP1-1”, covering

18 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] different patterns from “route 50”. This usually occurs (not always) when patterns represent different directions of the route, so both trips together constitute a “roundtrip”.

Figure 4: Conceptual structure of a vehicle schedule

A set of trips may belong to certain unique Route-Pattern. Blocks also allow “Interlining”, which means that trips covering different routes can be allocated in the same block.

To compute the generation of trips and blocks generation the PD uses a software tool called “HASTUS”.

Step 2 of the PP can be executed considering the whole network at once, or only part of the network. The PD decides what is the most convenient execution.

In step 3, the PD generates pieces of work containing trips from the vehicle schedule and allocates those pieces of work into duties. To compute the pieces of work allocation, the PD uses HASTUS software. The output is a crew schedule.

In step 4, the Planning Department creates rosters for drivers according to the Crew Schedule and several legal requirements. This step does not concern this thesis.

Finally, in step 5 another department assigns daily the Blocks (contained in the Vehicle Schedule) to actual vehicles. They also deal with everyday situations regarding the crew and reassign crew rosters if necessary. Step 5 does not concern this thesis.

2.5. Model developed by RET for Zero-emission project

Even though the final decision about the technologies is not yet made, most of the research done so far by RETPD considers autonomous battery electric buses (BEB). There is no clear preference for a specific charging technology. RETPD developed a model in MSEXCEL format (RET 2017) which helps to quantify the impact of some parameters (related to electric buses and equipment technologies) on the fleet size, distribution of vehicles between lines, and the charging times. The model considers parameters related to electric buses (or “electric parameters”) and parameters related to routes and characteristics of the transport service. They calculate fleet size and charging times for each route using initial values for parameters.

The “electric parameters” considered in the current model are: Size of battery pack (in [kwh]), average consumption (in [kwh/km]), charging speed (in [km/min]), objective battery level (OBL) (in %), full range (in

19 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] [km]) and available range (in [km]). A summary of “electric parameters” and their explanations is shown in Table 4.

Table 4: Electric parameters in the model developed by RETPD. Parameter Unit Formula Definition

Size of Battery [kwh] Independent Capacity of the battery pack onboard pack

Consumption [kwh/km] Independent Average energy consumed by the buses per kilometer travelled

Number of kilometers that can be travelled with the energy provided Charging speed [km/min] Independent by one minute of charge

SOC at which the battery performs in an optimal way. When a bus runs Objective Battery % Independent below OBL, it should go to a charging station as soon as possible. A bus Level (OBL) shall not begin a new trip with its SOC below the OBL.

Number of kilometers the bus can travel beginning with a full charged Full Range [km] battery (SOC = 100%) until it runs out of energy (SOC = 0%).

Number of kilometers the bus can travel beginning with a full charged Available Range [km] battery (SOC = 100%) until it reaches OBL (SOC = OBL).

Apart from the six parameters related to electric fleet, there are many other parameters related to each route and the current service executed by diesel fleet, such as: direction, total distance of the route (in [km]), average travel time of the route (in [min]) and headway of service (in [min]).

Direction refers to the order in which a route is covered (if a route includes places between A and B, it can be covered in two directions: from A to B, or from B to A).

Headway of service refers to the number of minutes elapsed between two consecutive departures from the same place for a specific route or line. For example, if a headway of 15 [min] was set for a route, it means that every 15 minutes a new bus departs from the terminal to cover the route.

The calculated results in the model are: charging time required per trip, number of electric buses required per route, minimum number of electric buses required per route per day, additional vehicles required per route, total roundtrips without need for charging per route per size of battery pack and total stop time required for charging per route per roundtrip.

Even though this model covers the basic aspects of an electric bus fleet, and helps to visualize the impact of those aspects on the fleet size (number of buses) and battery size per route, it does not predict the impact on the vehicle schedules. The vehicle schedules contain many trips and not all of them are expected to begin with a full battery charge. Furthermore, the vehicle schedule contains blocks which are defined for a specific time, and the model does not consider the “time” dimension, so it cannot predict properly what would happen with a schedule at any specific moment.

Section 3 identifies ZE aspects that may have a significant impact on the vehicle schedules and proposes methodologies to incorporate those aspects to the current model so that they can be quantified using the simulation tool presented in Section 4.

20 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 3. Identifying relevant Zero-emission aspects not included in the current model (Phase I)

This section aims to identify in existing literature new aspects related to ZE fleets that may have a significant impact on the current schedules, and propose methodologies to incorporate them to the model. As introduced in Section 1, most of the literature focuses on the impact on energy consumption. Although this is not exactly equivalent to identifying the impact on schedules, this thesis uses the available literature on impact on consumption as a guide to make sure that no important topics are left out.

The research done by RETPD (described in Section 2.5) mentions two aspects believed to be relevant which were not considered in the model for simplification purposes: heating/cooling systems of buses and characteristics of the route. Additionally, Figure 5 shows the impact of some aspects on energy consumption according to the Environmental Director of Volvo Buses (Jobson 2015). He states that the greatest energy consumers, apart from driving, are “climate”, “topography” and “speed”. The total energy consumed in the worst-case scenario, more than triples the total energy consumed in the best-case scenario.

Figure 5: Consumption in [kwh/km] due to different aspects of an electric bus fleet (Jobson 2015)

Based on but not limited to the above-mentioned sources, this section analyzes the relevant aspects that should be considered by the PD, and proposes methodologies to incorporate those aspects to the current model. It begins with heating/cooling systems to attend the “Climate” aspect, follows with characteristics of the routes to attend the “Topography” and “Speed” aspects, and ends with other important aspects found during the literature exploration.

3.1. Heating, Ventilation, and Air Conditioning units (HVAC)

Hegar et al. (2012) determine energy consumption of bus HVAC units for all kinds of buses, measuring the differential in fuel consumption of the bus when turning on the HVAC unit. Specifically, for Electric HVAC units without dedicated alternator (which they mention as “applicable on hybrid and fully electric buses”) they state that there is no relationship between HVAC compressor speed and bus engine speed, because the HVAC unit is supplied directly from the battery. This makes the mathematics behind much simpler than for diesel buses. The consumption is directly related to the power of the unit by design. Instead of measuring

21 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] the differential in fuel consumption (fully electric vehicles do not consume fuel), they establish the following mathematical relation: 𝑄̇푈 퐻푉퐴퐶 = 푈 where 퐻푉퐴퐶 : Coefficient of Performance of the Unit 𝑄̇푈 : HVAC unit cooling capacity in [kw] 푈 : Input power of the unit in [kw]

Coefficient of Performance represents “efficiency” in converting input power into heating or cooling capacity, so theoretically should be a number between 0 and 1. However, it is usually bigger than 1 because not only the input power is used as source, but also external sources (i.e. ambient, heat recovery systems, or heat pumps). Suh et al. (2014), state that typical COPs for petrol-based automotive industry are 1.3 for cooling and 2.3 for heating. They test an electric bus with HVAC unit and achieve levels of 1.6 and 2.6 for cooling and heating. An interesting difference between diesel buses and BEBs is that electric motors in BEBs do not emit waste heat (Göhlich et al. 2015) so heating units need to source exclusively from batteries.

Suh et al. (2014) introduce their work mentioning that, under conditions of very high ambient temperatures and high solar radiation, the HVAC unit for cooling the cabin is the second largest onboard energy consumer. They tested an integrated HVAC unit, driving an electric bus on a 1 [km] long test track with dynamic wireless charging equipment installed (inductive charging). The bus had 45 seats and followed the FTP-75 driving cycle characterized by an average speed of 34.1 [km/h] and a circuit length of 17.7 [km] (Ecopoint n.d.). They found that when heating the HVAC unit consumed 21.4% of the total energy consumed by vehicle operation. Similarly, when cooling the HVAC unit consumed 18.8% of the total energy consumed. Figure 6 shows the cumulative energy consumed [in kwh] for every second of time. After 1200 [s] (or 20 [min]) the bus has consumed around 18 [kwh] when the AC system is turned on, compared to only 14 [kwh] when AC system is turned off.

Figure 6: Energy consumed by electric bus operation with Air Conditioning system ON and OFF (Suh et al. 2014)

Farrington and Rugh (2000), in their conference at the earth technologies Forum in Washington DC, conclude that air conditioning loads can reduce range of Electric Vehicles by 40% depending on the size of the HVAC unit and the driving cycle. They suggest for places with extreme temperatures some techniques that may help to reduce the need for regulating temperature: advanced glazing on windshields (which reflect the sun radiations out of the vehicle, improving 3% the consumption) and recirculate air with ventilators (keeping comfort temperature in warm places when 100% of the air is recirculated consumes about a third of the energy compared to 0% recirculation, even though they suggest levels between 70% and 80% are convenient). They define an average 21 [C] as comfort temperature.

22 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] In another research done by EPA (2007), the HVAC together with the hydraulic lifts and lightning are mentioned as significant loads for the energy consumption of the vehicle.

Zhou et al. (2016) tested two different 12 [m] long BEBs in the Chinese city of Macao, one manufactured by BYD and the other by Ankai. They followed a route of 8.8 [km] with average speeds of 15.8 [km/h] and 13.2 [km/h] respectively, with outside temperatures between 15 [C] and 25 [C]. They found that when the AC unit was turned on, the BEBs consumed between 10% and 17% more energy than equivalent conditions with AC turned off.

All these findings confirm the importance of HVAC units, which have a significant impact in energy consumption when turned on. Therefore, HVAC consumption should be added to the model as a parameter. This thesis proposes to incorporate a “HVAC penalty” parameter as a percentage, which increases the average energy consumption considered by the model.

Finally, this thesis recommends RET to be aware of the HVAC unit onboard when purchasing the electric buses and to ask for detailed technical specifications of the HVAC unit to the bus supplier.

3.2. Characteristics of the route (driving cycle)

The term “Driving cycle” usually refers to a set of speed points of a vehicle across a period of time. It can be represented in a speed-versus-time plot. Characteristics of the route like the “sinuosity” and average distance between stops have a direct impact on the speed of the vehicle. Different driving cycles are defined to measure emissions under different conditions. Urban driving cycles are characterized by low speeds and short distances between stops, while suburban driving cycles are characterized by high speeds and long distances between stops.

It is a common belief that a vehicle can achieve high levels of efficiency (low energy consumption) when traveling at a moderate but constant speed. Lajunen (2013a) found that energy consumption decreases with higher speeds for all kinds of vehicles, with a much higher impact for conventional diesel buses compared to electric buses. This situation can be observed in Figure 7.

Figure 7: Energy consumed by type of bus for different average speeds based on (Lajunen 2013a)

23 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Lajunen elaborated in another research (2013b) a method to define energy-optimal velocity profiles using dynamic programming. He tested an electric bus in an urban route in Braunschweig and concluded energy- optimal driving can improve efficiency about 17%.

The US Environmental Protection Agency (EPA 2007) proposed two representative driving cycles: Manhattan cycle (for urban routes) and Orange county cycle (for suburban or rural routes). Manhattan cycle is characterized by frequent stops and low speed (average and maximum speeds around 11 [km/h] and 40 [km/h] respectively), following the driving patterns of buses in Manhattan core of New York City. Orange county cycle follows the driving patterns of Los Angeles, with less frequent stops and higher speed (average and maximum speeds around 20 [km/h] and 65 [km/h] respectively). The two most relevant components of the driving cycle are, according to EPA (2007), average speed and frequency of stops.

Qin et al. (2016) worked on a simulation for electric bus fleet in Tallahassee, Florida (US). When calculating the total energy consumption, they considered the “Manhattan cycle” by the calculation of a “cycle index”, which takes into account the average speed of the vehicle and the fraction of the time it is not moving (called “Percent Idle”). For a fleet of five buses EcoRide BE35 manufactured by Proterra, Qin et al. observed an average consumption of 1.68 [kwh/km].

The Thomas Larson Pennsylvania Transportation Institute (lead by PennState University), has a Bus Testing and Research Center in Pennsylvania, United States. In this facility, they perform several tests on different bus models for all kinds of organizations (like city councils, public transport companies, Federal Transit Administration, etc). One of the tests performed is the “Fuel Economy Test” in which they drive the bus following three different patterns and take measures of fuel consumption (energy consumption in case of electric buses). The patterns (properly explained by Altoona (2015a)), are described in Table 5.

Table 5: Three driving cycles used by Thomas Larson Pennsylvania Transportation Institute (Altoona 2015a) Pattern Length of Distance Number of stops Maximum Average Representative of (driving cycle) segment between stops per segment speed speed

CBD Central Business District 3 [km] 200 [m] 15 32 [km/h] 19 [km/h]

ART Arterial 3 [km] 750 [m] 4 64 [km/h] 42 [km/h]

COM Commuter 6 [km] 6000 [m] 1 (at the end) 64 [km/h] 61 [km/h]

An example of a test run containing all the cycles is shown in Figure 8. The different driving cycles can be traveled many times in one single run. Figure 8 shows a test run which contains six CBD segments, four ART segments and two COM segments.

24 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 8: Example of a test run containing the three driving cycles (Altoona 2015a).

This thesis describes three tests: BYD Electric Bus (Altoona 2014), Proterra BE40 (Altoona 2015a), and New Flyer XE40 (Altoona 2015b). Figure 9 shows pictures of the three models tested. All of them correspond to standard 40-feet long (12 [m]) urban buses.

BYD Electric Bus Proterra BE40 New Flyer XE40 Figure 9: Three electric bus models tested in Altoona (2014), (2015a), and (2015b) respectively.

The test on BYD Electric Bus (Altoona 2014) considers a total weight of 18 [ton] equivalent to the weight of the vehicle plus full load of average 70 [kg] passengers (50 [pass]). It is important to notice that BYD Electric Bus has a 324 [kwh] battery onboard according to information provided by the manufacturer and presumes a range bigger than 250 [km] (BYD n.d.), however the vehicle tested had a smaller battery (280 [kwh]). During the test, it was charged with a 40 [kw] charger provided by manufacturer. BYD Electric Bus is designed to suit an Overnight Charging mode (only one long charge enough for a whole day operation). A picture of the bus and the average consumption per driving cycle is shown in Table 6. The COM driving cycle was the less consuming, while the ART cycle was the most consuming cycle.

The test on Proterra BE40 (Altoona 2015a) considers a total weight of 18 [ton] equivalent to the weight of the vehicle plus full load of average 70 [kg] passengers (80 [pass]). The vehicle tested had a battery of 74 [kwh] capacity, and during the test was charged with a 160 [kw] charger provided by manufacturer. Proterra BE40 is designed to suit a Conductive Charging mode (many short charges enough for reaching the next charging station).

The test on New Flyer XE40 (Altoona 2015b) considers a total weight of 20 [ton] equivalent to the weight of the vehicle plus full load of average 70 [kg] passengers (76 [pass]). The vehicle tested had a battery of 208 [kwh] capacity and during the test was charged with an 80 [kw] charger provided by manufacturer. New Flyer

25 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] XE40 is designed to suit a Fast Charging mode (one or two charges throughout the day, enough for a few hours’ operation).

Looking at the three models tested, consistently the ART driving cycle consumed more than the CBD, while the COM was always the less consuming cycle.

Table 6: Average energy consumption per driving cycle for BYD Electric Bus, Proterra BE40 and New Flyer XE40, based on measurements by Altoona (2014), (2015a), and (2015b) respectively. Highest consumptions are observed for “ART” driving cycle.

Pattern Consumption Consumption Consumption (driving cycle) BYD Electric Bus Proterra BE40 New Flyer XE40

CBD 1.24 [kwh/km] 0.96 [kwh/km] 1.09 [kwh/km]

ART 1.58 [kwh/km] 1.34 [kwh/km] 1.43 [kwh/km]

COM 0.89 [kwh/km] 0.90 [kwh/km] 0.93 [kwh/km]

Overall Average 1.24 [kwh/km] 1.03 [kwh/km] 1.1 [kwh/km]

Another study using different driving cycles is Filippo et al (2014). They modeled a test drive of a Proterra BE35 (10 [m] long, slightly smaller than the tested in Altoona (2015a)), within a campus of Ohio State University. They used an “urban” cycle, identified by the acronym MBC (for Manhattan Buc Cycle) and a “suburban” cycle, identified by the acronym CSC (for City Suburban Cycle). They followed six different routes, and calculated what fractions of each route can be considered as MBC and CSC. Then calculated the energy consumption for each one of the routes. The results are shown in Table 7.

Table 7: Length, MBC fraction and energy consumption for six routes in Ohio State University (Filippo et al. 2014)

In contrast to the tests by Altoona (2014), (2015a) and (2015b), there is no clear relation between driving cycle (given by MBC fraction) and consumption in [kwh/km] in Filippo et al (2014).

Another similar methodology to differentiate between driving cycles is the SORT procedure, developed by the International Organization for Public Transport (UITP 2009). It was used by the Dutch company TNO to test 12 [m] BEBs (Goethem et al. 2013). The SORT procedure identifies three driving cycles, called “SORT1”, “SORT2” and SORT3”, which represent “heavy urban”, “easy urban” and “easy suburban” respectively. Figure 10 shows each one of the three cycles in a graph speed versus distance. Table 8 shows the numeric details of the cycles: length, speed, number of stops, idle time.

26 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 10: Speed versus distance plots for SORT driving cycles as described in Goethem et al. (2013).

Table 8: Numeric details of the SORT cycles as described in Goethem et al. (2013). Average Number of stops Average distance Stop time as fraction Cycle Meaning Total length speed per kilometer between stops of total time

SORT 1 Heavy urban 520 [m] 12 [km/h] 6 172 [m] 40%

SORT 2 Easy urban 920 [m] 18 [km/h] 3 303 [m] 33%

SORT 3 Easy suburban 1450 [m] 25 [km/h] 2 476 [m] 25%

The same study also describes the methodology regulation by United Nations Economic Commission for Europe (UNECE), where a driving cycle called “MVEG-B” (also known as “NEDC”) is required for the measurements of fuel consumption, energy consumption and pollutant emissions. The detailed procedure can be found in UNECE (n.d.). It is important to notice that this regulation was initially intended for “light duty” vehicles (small cars) and not urban buses. A brief description is given in Goethem et al. (2013), as shown in Figure 11:

Figure 11: Speed versus time for MVEG-B cycle, as described by Goethem et al. (2013).

Goethem et al (2013) combine the SORT and the UNECE procedures to assess the BEB. They keep the HVAC unit turned off during the tests. Their test track is located in Lelystad, The Netherlands. Weather conditions during the tests were more severe to the prescribed for the SORT procedure (wind speed far above maximum allowed, and temperatures very close to the minimum allowed). The results are shown in Table 9:

27 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 9: Energy consumption measured on the test over a 12 [m] long bus as shown in Goethem et al. (2013).

It is interesting to notice that almost no difference in consumption was measured in this experiment.

In addition to the evidence in literature presented in this thesis, previous knowledge of RETPD supports the relation between driving cycle and energy consumption where urban routes consume less energy than suburban routes.

Summarizing, the literature regarding driving cycles is not conclusive. On the one hand, Lajunen (2013a), Qin et al. (2016) and Altoona (2014), (2015) and (2015a), as well as previous knowledge of RETPD, support the idea that driving cycles can make a significant difference in energy consumption. But on the other hand, Filippo et al. (2014) and Goethem et al. (2013) support the idea that differences in consumption of driving cycles are negligible.

This thesis suggests that RETPD should incorporate in their model the possibility to differentiate consumptions of driving cycles, and calculate a base consumption per route using initial values for consumptions. This requires a database where every route is assessed to the extent of its “urban” or “suburban” condition. The methodology proposed to build the assessment is shown in Section 4.

Also, this thesis suggests RETPD to do a follow-up research to calibrate the initial values for consumptions using real measurements in the city of Rotterdam.

Finally, this thesis suggests that RETPD should be aware of the average consumption presumed by manufacturers when purchasing the electric buses and should ask for detailed technical specifications and conditions (driving cycle) in which such consumption was measured.

3.3. Other parameters

During the literature exploration, looking for information about HVAC and driving cycles, other aspects were found that seem relevant for the model: efficiency of energy transfer during charging process, transmission form of the electric bus, regenerative break efficiency and handling queues in charging stations.

3.3.1. Efficiency of energy transfer during charging process

Tests carried out by Altoona (2014), (2015a) and (2015b) (shown in Section 3.2), began with SOC = 100% and were followed by a documented charging process to recover the full charge. At some moments during the

28 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] charging processes, the power output provided by the stations was not equivalent to the power measured at the battery pack. This suggests that some energy is lost in the charging process, therefore an efficiency rate should be considered.

The BYD Electric Bus consumed 256 [kwh] in the test, and the charging process to full charge took almost 7 hours, connected to a 40 [kw] charger provided by the manufacturer. The nominal energy used to recover the 256 [kwh] consumed was 280 [kwh] (Altoona 2014). That gives an efficiency of 256/280 = 91%.

The Proterra BE40 consumed 67 [kwh] in the test, and took 28 minutes connected to a 160 [kw] to reach 100% charge. The nominal energy used to recover the 67 [kwh] consumed was 74 [kwh] (Altoona 2015a). That gives an efficiency of 67/74 = 90%.

The New Flyer XE40 behaved in a different way. While the BYD and Proterra buses showed a linear increase state of charge until they reached 100%, the New Flyer behaved linear only until 82% charge. After that it begun sucking much less energy from the output and took very long to reach 100%. Figure 12 compares the charging process between the Proterra and the New Flyer. In the end, the New Flyer consumed 157 [kwh] in the test, and the energy used to reach 100% charge was 208 [kwh]. The process took 17 hours, but the 82% was reached in only 2.5 hours. (Altoona 2015a). The efficiency of the whole process was 157/208 = 75%.

Figure 12: Energy used to charge Proterra BE40 and New Flyer XE40, based on (Altoona 2014) and (Altoona 2015a)

Also, Prohaska et al. (2016) tested the electric bus Proterra BE35 equipped with an 88 [kwh] battery, and charged it using a high-power charging station provided by the manufacturer. The average power delivered by the charging station during the charging process was 360 [kw], while the power measured at the battery pack was only 319 [kw] in average. This suggests an efficiency rate of 319/360 = 89%.

This thesis proposes to add a “charger efficiency” parameter to the model with a reasonable initial value of 90%, which multiplies the nominal power (in [kw]) provided by the charger. This thesis also encourages RET to ask the manufacturer when purchasing the buses if the charging process behaves approximately linear and the efficiency rate it presumes.

29 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 3.3.2. Transmission system

Wang et al. (2016) built a simulation to compare the energy consumption of an electric bus using Direct Drive transmission and Automatic Mechanical transmission (AMT). Different transmission forms are explained by Ehsani et al. (2010). Wang et al. found that there are important efficiency gains when adding an AMT to the vehicle. They also modeled an “Intelligent” AMT system which implements an “Economic shift schedule”. Table 10 shows the main findings.

Table 10: Energy consumption for different types of transmission as shown in (Wang et al. 2016)

This thesis suggests that there is no need to add this parameter to the model because transmission is already incorporated in the bus and included in the consumption presumed by manufacturers, but encourages RET to look for bus models which include any kind of Economic AMT transmission for better energy efficiency.

3.3.3. Regenerative braking

Ning and Guo (2012) give a detailed explanation on the different mechanisms for energy recovery when braking or decreasing speed. They did a simulation and found that under typical conditions, the driving range of an electric bus equipped with hydraulic regenerative braking system increases by 8.2% compared to the equivalent situation without regenerative braking. Gonzalez (2016) identified causes of energy loss in electric cars, and found that regenerative braking helps to recover around 75% of energy loss due to braking, which represents around 15% of the total energy loss due to all causes. Table 11 shows his results.

Even though the study by Gonzalez (2016) was on cars, the regenerative braking technology is similar for buses. This thesis suggests that there is no need to add this parameter to the model because regenerative braking is already incorporated in most of the electric bus models and considered in the consumption presumed by manufacturers.

30 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 11: Causes of energy losses in electric cars and energy recovered by regenerative brakes (Gonzalez 2016) Energy Loss City Highway Combined Charging inefficiency 12% 15% 14% Electric drive 14% 13% 14% Parasitic Losses 3% 2% 3% Wind resistance 22% 42% 31% Rolling resistance 19% 21% 20% Braking 30% 7% 20% Total 100% 100% 100%

Energy recovered by -24% -6% -15% regenerative brakes

3.3.4. Queues handling

This thesis found that it is critical for the model to consider the capacity of charging stations. If each route is considered independently and assumed that all the trips begin with a SOC = 100%, queues may be observed in the stations and the whole plan can be ruined. The current model of RETPD calculates the time required for charging for each route assuming unlimited capacity of the stations. Qin et al. (2016) did a simulation of charging strategies, and found that many “overlap” events occurred in the charging stations (two or more buses required charging at the same time). They call “Charging Threshold” to the SOC at which buses should charge as soon as possible (equivalent to the OBL). They found that for a fleet of five buses using only one charging station, an OBL of 60% generates no overlapping events and minimizes peak demand for electricity. Mohamed et al. (2016) also simulated an electric bus fleet on a full transit network. He considered the queues on his simulations and adopted what he calls a “Lowest Attribute Value” (LAV) queuing policy. This means that in case of a queue at a charging station, priority is given to the bus with lowest SOC. He also included calculations to manage the station queues which consider the distance to the closest available charging station and the length of the routes of buses in the queue to assign priorities.

Based on the presented experiences, this thesis proposes two important actions. First, the model should consider the “time” dimension, since queues are originated when buses arrive at the same time to the station (thus depend on times of the trips of the schedule). Second, this thesis proposes to predefine, as parameters, the places where charging stations are located, their capacities, and characteristics (like power and efficiency). Power output depends on the purchasing decision of RET.

3.4. Overview of Enhanced model

To wrap up all the topics addressed in Sections 3.1 to 3.3, Table 12 shows the elements that the enhanced model used by RETPD should consider for the PP with ZE fleet.

31 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 12: Summary of aspects to be considered for the enhanced model by RETPD Parameter to consider Aspect covered Explanation Size of Battery pack Range Capacity of battery pack onboard, in [kwh].

Objective Battery Level Range SOC at which the battery performs in an optimal way, in %.

Minimum Layover Time Range Buffer time at the end of trips, to handle possible delays due to traffic or other external uncertainties, in [min]. Layover for charging Range Minimum length of a layover to allow charging the batteries, in [min].

HVAC penalty HVAC unit Additional consumption due to HVAC unit, in [%], to be multiplied by the base consumption. For details on methodology see Section 4.1.1.

Consumption Driving cycle Consumption for each driving cycle, in [kwh/km]. It requires a database per driving cycle of all Route-patterns considered by the model with basic data (like length, speed, number of stops) and also average speed and average distance between stops for each driving cycle. For details on methodology see Section 4.1.2. Location (Place) Charging capacity and Each charging station must be located in certain place of the network. per charging station queue handling

Power Charging capacity and For each charging station the power output must be defined, in [kw]. per charging station queue handling

Charging efficiency Charging capacity and For each charging station the efficiency in energy transfer between per charging station queue handling station and battery must be defined, in [%]. For details on methodology see Section 4.1.3.

Setup charging time Charging capacity and For each charging station the setup time must be defined, in [min]. per charging station queue handling Depends on the technology adopted.

This meets the first objective of this thesis: to identify relevant ZE aspects that may have an impact on the PP. Section 4 quantifies such impacts simulating the current vehicle schedules under ZE conditions.

32 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 4. Quantifying the impact of relevant Zero-emission aspects on schedules (Phase II)

The generation of vehicle schedules is explained in Section 2.4.3. Vehicle schedules are the result of a complex optimization problem, which considers all the parameters and constraints defined by RETPD. As already introduced in Section 1, this thesis identifies two different approaches to prepare for the ZE fleet. The first one is the optimization approach, which consists of the design of new optimization problems adding parameters and constraints related to ZE conditions. The second one is the simulation approach, which consists of simulating the current vehicle schedules under ZE conditions.

This thesis aims to measure the impact on the PP using the simulation approach. For the city of Rotterdam, which has a very complex transportation network, the optimization approach would require many software modifications and a high amount of resources, which may cost a lot of time and money. The simulation approach is much simpler and can be developed in a short time with little resources. Eventually, once the ZE fleet is implemented in the city, the software modifications will become necessary; but at the moment this thesis was conducted the simulation approach covered very well the needs of RETPD. This thesis could also be useful to other cities with similar PP and facing a similar ZE fleet project.

4.1. Proposed methodologies to incorporate relevant Zero-emission aspects into the enhanced model

Before explaining the simulation tool, this thesis proposes some methodologies to incorporate into the model the relevant ZE aspects found in Section 3.

4.1.1. Methodology to incorporate HVAC unit into the enhanced model

As stated in Section 3, This thesis proposes to incorporate a “HVAC penalty” parameter as a percentage, which increases the average energy consumption considered by the model, according to the formula:

푊 ℎ퐻푉𝐴 = 푊 ℎ 퐻푉𝐴 + 퐻푉𝐴

The city of Rotterdam rarely presents extreme temperatures (below 0 [C] or above 30 [C]). Average minimums oscillate between 0 [C] in January and 12 [C] in August, while average maximums oscillate between 5 [C] in January and 21 [C] in August (Weather and Climate, n.d.). Based on literature and the moderate climate of Rotterdam, a reasonable initial value for the HVAC penalty is +20% on consumption. This value should be calibrated in a follow-up research with temperature measurements inside Rotterdam buses for different environmental conditions.

4.1.2. Methodology to incorporate the driving cycles into the enhanced model

The methodology proposed includes eight steps, described in Table 13.

Table 13: Methodology proposed for Route assessment on driving cycle. STEP EXPLANATION STEP 1 Define 3 driving cycles, called “Urban”, “Suburban” and “Commute”. STEP 2 Define a range of speeds in [km/h] and a range of distances between stops in [km] for each driving cycle.

33 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

STEP 3 Define a consumption in [kwh/km] for each driving cycle STEP 4 Divide each Route-Pattern in segments, given by the timing places of the route. STEP 5 Evaluate each segment by speed: if the average speed is within the range of speeds defined for certain cycle, assign the consumption of such cycle to the segment. STEP 6 Evaluate each segment by distance: if the average distance between stops is within the range of distances defined for certain cycle, assign the consumption of such cycle to the segment. STEP 7 From the two segment evaluations, keep the most pessimistic (highest consumption) STEP 8 Assess each route using a weighted average of the consumptions of segments, where each weight is given by the fraction of the route contained in each segment.

As an example, consider the following driving cycles (steps 1, 2 and 3) with arbitrary initial values for speed range, distance range and consumption, shown in Table 13:

Table 13: Steps 1, 2 and 3 of methodology to incorporate driving cycles into the enhanced model. Limits speed range Limits distance range Consumption [km/h] [km] [kwh/km] min max min max Urban - 15.0 - 0.4 1.10 Suburban 15.0 30.0 0.4 1.5 1.40 Commute 30.0 - 1.5 - 1.00

Now consider the sample Route-Pattern called “R10P1”, with a total length of 9.9 [km] divided in five segments defined by six “Timing Points” (TP) (step 4), shown in Table 14:

Table 14: Step 4 of methodology to incorporate driving cycles into the enhanced model. Number of Average speed Average distance From To Length [km] Time [min] stops [km/h] between stops [km] TP1 TP2 0.5 2 3.0 10.0 0.3 TP2 TP3 0.9 5 4.0 13.5 0.2 TP3 TP4 2.0 1 3.0 40.0 2.0 TP4 TP5 1.5 5 4.0 22.5 0.3 TP5 TP6 5.0 3 21.0 14.3 1.7

The evaluation of each segment according to speed and distance, keeping the most pessimistic of both as final evaluation (steps 5, 6 and 7), is shown in Table 15:

34 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 15: Steps 5, 6 and 7 of methodology to incorporate driving cycles into the enhanced model. Average speed Average distance Evaluation on Evaluation on Final Consumption From To [km/h] between stops [km] speed distance Evaluation [kwh/km] TP1 TP2 10.0 0.3 Urban Urban Urban 1.1 TP2 TP3 13.5 0.2 Urban Urban Urban 1.1 TP3 TP4 40.0 2.0 Commuter Commuter Commuter 1.0 TP4 TP5 22.5 0.3 Suburban Urban Suburban 1.4 TP5 TP6 14.3 1.7 Urban Commuter Urban 1.1

The final assessment for R10P1 would then be the weighted average of consumptions of segments, where the weight is given by the fraction of the total length that each segment contains (step 8), shown in Table 16:

Table 16: Step 8 of methodology to incorporate driving cycles into the enhanced model.

The assessment in this example gives a consumption of 1.13 [kwh/km] for Route-Pattern R10P1. This same exercise should be replicated for every Route-Pattern to be considered in the model. The result of assessing all the Route-Patterns shall be stored in a database or table.

The most difficult part of the methodology is not doing the calculations, but choosing appropriate initial values for the consumptions, speed range limits and distance range limits. As mentioned in Section 3, results observed in literature are not conclusive. Just as arbitrary initial values, this thesis proposes the values shown in Table 17, which are based on the simulations by Altoona (2014), (2015a) and (2015b). Particularly, the consumptions used for Urban and Suburban driving cycles (1.24 [kwh/km] and 1.58 [kwh/km] respectively) correspond to CBD and ART measurements of BYD electric bus (Altoona 2014) and the consumption for Commute driving cycle (0.93 [kwh/km]) correspond to COM measurements of New Flyer XE40 electric bus (Altoona 2015b). The high-speed limit for Urban range (equivalent to the low speed limit of Suburban range) was set at the midpoint between the average speed of CBD test of BYD electric bus, and the average speed of ART test of BYD electric bus. The high-speed limit for Suburban range (equivalent to the low speed limit of Commute range) was set at the midpoint between the average speed of ART test of BYD electric bus, and the average speed of COM test of New Flyer XE40 electric bus. The same methodology was applied to set the limits of distance ranges. The reason behind choosing from different bus models, is just to consider the “worst case scenario”, with most pessimistic (highest) consumptions.

35 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 17: Arbitrary initial values for speed range, distance range and consumptions of driving cycles correspond to “worst case scenario” from Altoona (2014), (2015a) and (2016).

Limits speed range Limits distance range Consumption [km/h] [km] [kwh/km] min max min max Urban - 31.2 - 0.5 1.24 Suburban 31.2 51.5 0.5 3.5 1.58 Commute 51.5 - 3.5 - 0.93

Finally, this thesis remarks the need to design a follow-up research to calibrate these initial values using real measurements in the city of Rotterdam.

4.1.3. Methodology to incorporate the charging stations efficiency into the enhanced model

As stated in Section 3.3.4, this thesis proposes to predefine, as parameters, the places where charging stations are located, their capacities, and characteristics (like power and efficiency). Power depends on the purchasing decision of RET. Each charging station should be given an initial value for efficiency which takes an initial value of 90% and multiplies the nominal power (in [kw]) of the charger. This thesis also encourages RET to ask the manufacturer when purchasing the buses if the charging process behaves linearly and what efficiency does the charging process presume.

The charging speed in [km/min], as considered in the previous model by RETPD (see table 4), cannot be calculated directly with the above-mentioned parameters because it includes an implicit energy consumption value in [kwh/km]. But the charging speed in [kwh/min] does not depend on consumption, and can be easily calculated using the following formula:

4.2. Simulation tool overview

The simulation tool used by this study to quantify the impact of ZE aspects on vehicle schedules was developed in a single MSEXCEL workbook. Some worksheets are used as templates to provide inputs to the simulation, while others are filled by the tool with the results. MSEXCEL was chosen as environment due to its user-friendly interface which allows to adjust the parameters of the model very easily, its compatibility with the previous model from RETPD, its compatibility with other software tools used by RETPD and the fact that it is already installed in computers of RET (no additional software licenses are required).

The simulation presented in this thesis considers two main inputs. The first input is the enhanced model, summarized in Section 3.4, and the second input is the vehicle schedule, introduced in Section 2.4.3. A total of four worksheets were used as inputs, even though only three are effectively read by the simulation and the other one is used for supporting calculations. For a detailed description of the input worksheets see Appendix 2. The simulation itself is carried on by a program written in Visual Basic for Applications (VBA) language, within the same workbook. For a detailed description of the VBA program see Appendix 3. The

36 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] outputs of the simulation are shown in four different worksheets. For a detailed description of the output worksheets see Appendix 4. The general structure of the tool is shown in Figure 13.

Figure 13: Structure of the simulation tool built in MSEXCEL.

4.3. Calculation of performance indicators for the schedule provided

To quantify the impact of ZE aspects on a vehicle schedule provided, the simulation measures to the extent of “feasibility” of the schedule given certain settings for the parameters. This thesis defines some basic elements shown in Table 18 and proposes some “performance indicators” based on them.

Table 18: Basic elements required to calculate performance of vehicle schedules under certain settings. ELEMENT EXPLANATION NUMBER Total number of trips contained in the vehicle schedule provided. It does not depend on OF TRIPS the simulation, only on the schedule. CLEAN TRIP A trip that begins and ends with a SOC higher than the Objective Battery Level (OBL). If one or both conditions are not met, the trip is considered “unclean”. When a trip is being executed, it is said to “become unclean” as the moment SOC drops below OBL. FEASIBLE A trip that begins with a SOC higher than the OBL. If this condition is not met, the trip is TRIP considered “infeasible”. Note that a clean trip is always feasible, but a feasible trip may not always be clean. NUMBER Total number of blocks contained in the vehicle schedule provided. It does not depend OF BLOCKS on the simulation, only on the schedule. CLEAN A block which contains only clean trips. If a block contains one or more unclean trips, it is BLOCK considered an unclean block. When a trip of the blocks is being executed, the block is said to “become unclean” as the moment SOC drops below OBL.

37 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

FEASIBLE A block which contains only feasible trips. If a block contains one or more infeasible trips, BLOCK it is considered an infeasible block.

The performance indicators of the schedule are simply the total number of clean trips, total number of feasible trips, total number of clean blocks and total number of feasible blocks.

Note that given the definitions and structure of the schedules, the following relations are always true:

≤ 𝑏 ≤ 𝑇

𝑏 ≤ 𝑏 𝑏 ≤ 𝑇 𝑏

𝑇 𝑏 ≤ 𝑇

𝑏 𝑏 ≤ 𝑏

𝑏 ≤

Additionally, a block is considered to be “active” between the time at which it first trip begins and the time at which its last trip ends. It may happen that not all the blocks are active at the same time.

4.4. Assumptions of the simulation

The simulation tool assumes that any new or adapted infrastructure required in the charging stations can be provided. Infrastructure adaptations may include building more parking space in some places, installing special equipment to handle high power electric chargers, among others. Technical and financial analysis of such modifications are not part of this thesis.

The simulation tool assumes that routes and patterns provided are correct and do not change. It does not do a geographical check of the distances, or logical analysis of the routes. It does not suggest new routes or modifications to the routes.

The simulation tool assumes that all the blocks begin with a SOC=100%. Each block is treated as one bus.

The simulation tool assumes that each charging station can charge only one bus at a time. If RETPD wants to simulate charging many buses in the same place simultaneously, they must add more charging stations in the place using the “Charging stations Area” in the “Parameters” worksheet of the tool.

The simulation tool assumes that a bus cannot charge during the “Minimum layover” (set by user in the “Parameters” worksheet). The simulation cannot rely on this time because it is not always available and depends on external events (heavy traffic, accidents, traffic lights, closed streets, among others).

The simulation tool assumes that the last trip of a block always ends in the garage. The simulation then tries to charge the batteries in the garage until they reach 100%. It may not always be possible for some blocks, if there are too many blocks in the garage waiting for charge and charging stations are not enough for all. More details on the charging rules (or charging strategy) are given in Section 4.5.

38 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] The simulation tool assumes that there is no energy consumption during layover times. When beginning a new trip, the SOC of the block is the same as the SOC when the previous trip ended if there is no charging during the layover.

4.5. Charging rules

When a block arrives to a place after a trip (which implies SOC<100%) and the layover time ahead is longer than the “Layover Time for charging” parameter (input from the user), the simulation program will always try to charge the block. If there is a charger available at the place, the block will take it. If there are multiple chargers available, it will take the fastest one. If there are no chargers available (because the chargers are all busy or simply because no chargers were created for that place), it will remain waiting until one of the chargers becomes available or the next trip begins (whatever happens first). Once the block took a charger, it remains using it until SOC=100% is reached or the next trip begins (whatever happens first).

In one of the first steps of the simulation, the program sorts the trips of the schedule by ascending start time (see Appendix 3). This establishes the order followed by the simulation when calculating each block. If two blocks arrive at the same time to a place and both layovers are longer than the “Layover for charging” parameter, the first one to be analyzed by the program following the mentioned order is going to take the charger. The same thing happens if two blocks are waiting (queueing) and a charger becomes available: only the first block to be analyzed (following the same order) is going to take the charger, even if such block arrived later than the other one or has a higher SOC.

Other rules, not implemented for this thesis, are First-Come-First-Served (FCFS) and Priority ranking. In FCFS, when two or more blocks are waiting in the same place and a charger becomes available, the block which arrived first to the place takes the charger. In Priority ranking rule, the queue is not served by order of arrival but by priority. Priorities can be defined by several criteria. One example is using SOC as criteria; the lower the SOC, the higher the priority. Another example is using the length of next trip; the greater the length of next trip, the higher the priority. Mohamed et al. (2016) assign a “Charging Priority” which can take the values 1 (high), 2 (mid) or 3 (low) if the following conditions are met: not enough SOC to fulfill the next trip, not enough SOC to end the next trip above the minimum allowed, and SOC lower that maximum (respectively). When a charger becomes available, the block with highest priority takes it. If two buses with the same priority require charging simultaneously, the “Low Attribute Value” (LAV) rule applies (bus with lowest SOC gets the charger).

Also, additional criteria can be added to attend specific situations. For example, if a block is already charging and a block with higher priority arrives to the place, such high-priority block may be allowed to evict the low- priority block from the charger.

5. Impacts of Zero-emission aspects on vehicle schedules

The impact of the ZE aspects can be measured running the simulation under different settings (changing parameters in “Parameters” worksheet) and then comparing the results. For this thesis, several instances of the simulation were run. A standard schedule provided by RETPD with 128 blocks was used as input. The settings for all the runs are seen in Appendix 3. The first run was established as “base scenario” so it was used as reference to compare results of different settings.

39 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

5.1. Base run settings

Settings for the base run are described in Figure 14.

Figure 14: Settings of base run of simulation tool.

The battery size was set at 200 [kwh], similar to the medium-sized battery of the New Flyer XE40 electric bus described in Section 3.2. The OBL was set at 50% as suggested by RETPD. Layover for charging was set at 17 [min] and minimum layover was set at 2 [min], as suggested by RETPD. The HVAC penalty was set at 0% to simulate favorable weather conditions.

Regarding the RP assessment, the limits for speed ranges and distance ranges, as well as consumption per driving cycle, were set according to explanation in Section 4.1.2.

Regarding the charging stations, all were considered to provide 200 [kw] power output, have an efficiency of 90% in energy transfer and require a 1 [min] Setup Time. 32 stations were considered in total, from which 9 are located in the garage and 23 are located in different places visited by the blocks. These stations are the minimum required given all the other parameters to allow the simulation to run without any queue. If one of these stations was deleted, queues would be observed. And if another station was added to the list in a place different from the garage, it would not be used at all.To determine this set of stations a very simple methodology was followed; first, a lot of charging stations were installed in every place of the schedule. Then

40 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] the simulation was run. Finally, all the stations which were not used during the whole simulation were taken out from the initial set.

Note that queues are, in general, undesired results, but from the planning perspective they may not have direct impact on the feasibility of the blocks. Queues may be an issue for other departments of RET (for example, they require that the driver stays in the bus during the whole layover to move it when a charger becomes available).

5.2. Base run results

A map with the overview of the whole simulation can be seen in Figure 15. It was generated using 30 screenshots of “Result_VehView” worksheet with 10% zoom (see Appendix 4 for more details on the worksheet). Even though no data can be read at this zoom level, the 128 blocks are clearly visible. The simulation begins at 5:21 (top of map) and ends 24 hours later, at 5:20 of the next day (bottom of map). Each column of the map represents a block. Approximately half of the blocks begin during the morning (left columns of map) and the other half during the afternoon (right columns of map). There are short blocks, which are active for a few hours, and long blocks which are active for more than 12 hours. The vertical red lines that can be seen represent infeasible trips (which began with a SOC below 50%, considered as OBL for base run). Most of the infeasible trips belong to the long blocks.

Figure 15: Map of base run simulation, made of 30 screenshots from “Result_VehView” worksheet with 10% zoom. Each column represents a block (128 in total). Time begins at 5:21 (top of map) and ends at 5:20 of the next day (bottom of map).

At 5:21, first minute of the simulation, only one block is active (the first column on the left of the map). Since every one of the blocks is executed by one vehicle, at that time there is only one vehicle on the road. But in the following minutes, more vehicles are required as more blocks become active. At some minute during the morning a maximum number of active blocks is reached, before the short morning blocks end. Once the short blocks end, the number of active blocks decreases. At 11:45, right before the afternoon blocks begin, many short blocks of the morning already ended. But around that time the number of active blocks begin increasing again. This situation can be clearly seen in Figure 16, which shows the total number of active blocks at every minute of the simulation and details for their status.

41 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 16: Detail active blocks per status per minute for Base run

Between 7:45 and 9:15 there is a peak of 71 simultaneous active blocks. Right after 11:00 there is a minimum of 50 blocks which lasts for about half hour, and then the number increases again with afternoon blocks. Between 16:45 and 17:45 a new peak of 72 simultaneous active blocks is reached. After that the number decreases again until it reaches zero at 1:23.

During the morning peak, around 60 out of the 71 active blocks are travelling clean (with SOC above OBL of 50%), while the rest are either waiting or charging. Almost no “unclean” blocks (with SOC below OBL of 50%) are observed. This is a desirable for RETPD, since there is a low risk of interrupting trips because of low battery level. But in the afternoon peak, less than 50 out of the 72 active blocks are travelling clean, and sometimes more than 10 are travelling unclean. This is not desirable for RETPD, since there is a higher risk of interrupting trips because of low battery level.

The performance of the schedule in the base run, in terms of the performance indicators defined previously, is shown in Table 19.

Table 19: Performance of first run of simulation tool (base run). Concept Number Total Blocks 128 100.0% Feasible Blocks 83 64.8% Clean Blocks 72 56.3%

Total Trips 1540 100.0% Feasible Trips 1340 87.0% Clean Trips 1251 81.2%

42 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] With the mentioned settings, 64.8% of the blocks of the schedule (83 out of 128) are feasible, while 56.3% (72) are not only feasible but also clean. Regarding trips, 87% are feasible and 81.2% are clean.

Since 83 blocks are feasible, there are 45 unique blocks which contain an infeasible trip at some moment of the simulation. Furthermore, four infeasible blocks drop their SOC below 0% (which is impossible in reality, but the simulation allows it to avoid interruptions in execution).

As mentioned in Section 5.1, during the base run no queues were observed, and all the charging stations were used at some moment.

5.3. Impact of modifying the “Layover Time for Charging” parameter

The first six runs of the simulation keep all the settings of the base run, but consider increasing “Layover for Charging” parameter (explained in Section 3.4 and Section 4.2.1), between 9 and 21 minutes. The modifications are shown in Figure 17.

Figure 17: Modifications to base run parameter “Layover Time for Charging” for runs 2-7.

The impact of changing this parameter on the number of feasible and clean blocks and trips is shown in Figure 18. The shorter the layover time allowing charging, the more feasible blocks are observed. Having 9 minutes of layover time for charging (RUN 2) increases the number of feasible blocks from 83 to 104.

Figure 18: Impact of “Layover Time for charging” parameter on feasibility of blocks and trips.

However, a different problem is observed when a shorter layover time for charging is allowed. Allowing 9 [min] of layover time for charging (run 2) produced a total of 14 queues during the simulation, with a total waiting time of 107 [min] (in average, each wait takes 7.6 [min]). Allowing 11 [min] (run 3) produced 13 queues with a total waiting time of 99 [min], and allowing 13 [min] (run 4) produced only 1 queue of 11 [min]. For values of 15 [min] and above for the parameter, no queues are observed.

Figure 19 shows the detail of active blocks per status, for runs 2, 4, base and 7. It is clear that for run 2 most of the travelling blocks are “clean” (blue dominates orange) and most of the layovers times are spent charging

43 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] (yellow dominates gray). On the contrary, for run 7 almost half of the afternoon blocks travel “unclean” (with SOC below OBL) which represents a big risk, while most of the layover times are spent doing nothing (grey dominates yellow). At the afternoon peak, from the 72 active blocks, around 60 are travelling, and around 20 of those 60 are travelling with SOC below OBL. If RETPD requires preventing this situation, given the conditions in Run 7, they need approximately +30% additional vehicles to cover the blocks during the peak.

Figure 19: Comparison of detail of active blocks per status, for different values of “Layover Time for charging”.

5.4. Impact of deleting charging stations.

A key decision for ZE fleet implementation in Rotterdam is the location and characteristics of the charging stations. The reasons and calculations supporting the purchasing decision do not concern this thesis, but the consequence of such decision may have an impact on the schedules. For this thesis, RETPD provided a list of places where it is highly likely that one or more charging stations will be installed. Run 8 of the simulation keeps the same settings of the base run but deletes 9 charging stations from different places (not from the garage) to adjust to the more probable scenario suggested by RET. Figure 20 shows the base run setting, the charging stations deleted and the new setting.

44 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 20: Deletion of 9 charging stations from Base run for Run 8.

Even though the modification looks very aggressive (9 out of 32 charging stations are not available anymore) the impact is not so significant compared to other aspects. Figure 21 shows the main results.

Figure 21: Impact of deleting 9 charging stations considered by RETPD as less likely to be available.

Only 2 additional blocks become infeasible compared to the base run. In other words, the nine charging stations deleted from the original set have a very little positive impact on the feasibility of the schedule. It is important to notice that for Run 8, queues are observed (29 queues in total), which add up 260 minutes of waiting time.

45 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 5.5. Impact of HVAC unit on schedule

To measure the impact of switching on the HVAC unit onboard, runs 9, 10, 11 and 12 keep the same settings as the base scenario, but consider increasing HVAC penalties to measure its impact on the feasibility of the vehicle schedule. The modifications are shown in Figure 22. The range of increasing values goes from +10% for run 9 following the findings of Zhou et al. (2016), and +40% for run 12 following the findings of Farrington and Rugh (2000). Recalling from Figure 14, the consumptions for base case scenario are 1.24 [kwh/km], 1.58 [kwh/km] and 0.93 [kwh/km] for Urban, Suburban and Commute respectively.

BASE RUN RUN 9 RUN 10 RUN 11 RUN 12 Parameter Unit Explanation Parameter Parameter Parameter Parameter 200 [kwh] Battery Size 200 200 200 200 50 % Objective Battery Level 50 50 50 50 17 [min] Minimum Layover Time for Charging 17 17 17 17

0 % HVAC Penalty 10 20 30 40 2 [min] Minimum Layover 2 2 2 2 Figure 22: Modifications to base run parameter “HVAC penalty” for runs 9-12.

The results are shown in Figure 23 and Figure 24. Feasibility of current schedule is very sensitive to variations in HVAC unit consumption.

Figure 23: Impact of the HVAC unit on the feasibility of the vehicle schedule.

46 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 24: Detail of active blocks per status for base run (HVAC penalty of 0%) and run 12 (HVAC penalty of +40%).

An interesting result of testing the impact of HVAC penalty is that almost no queues are produced at the charging stations. For run 11, only one queue of 3 [min] was observed, while for run 12 only one queue of 5 [min] was observed. This means that adding additional charging stations does not solve the feasibility problems generated by HVAC unit consumption. But the utilization of existing charging stations does increase. For the base run each station was used 267 [min] in average, while for run 12 the average utilization was 371 [min]. Figure 25 shows the power in [kw] provided by the stations for base run and run 12.

Figure 25: Power in [kw] required by base run and run 12 at every one of the 1440 simulated minutes.

5.6. Impact of consumptions of driving cycles on schedule

According to the results of Goethem et al. (2013), shown in Table 9, the energy consumptions for different driving cycles are equivalent, around 1.15 [kwh/km]. To assess this situation, in which all the consumptions are the same, run 16 keeps the settings from the base run but considers 0.9 [kwh/km] for all the driving cycles and the following runs increase this unique consumption by 0.1 [kwh/km] for each run. A summary of the modifications is shown in Figure 26.

Figure 26: Modifications to parameters for runs 16 to 22.

The results are shown in Figure 27 and Figure 28.

47 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 27: Impact of equal consumptions between driving cycles on feasibility of blocks

Figure 28: Detail of active blocks per status for base run and runs 16, 19 and 22.

No queues were observed for runs 16 to 22. The total utilization of charging stations varies between an average of 179 [min] per station for run 16, to 287 [min] per station for run 22.

5.7. Impact of charging stations power on schedule

Runs 23 to 29 keep the base settings but consider increasing power output between 50 [kw] and 400 [kw]. The modifications for each run are shown in Figure 29.

48 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 29: Modifications to power output of base run for runs 23-29.

The feasible and clean blocks for these runs are shown in Figure 30.

Figure 30: Impact of the power output of charging stations on the feasibility of the vehicle schedule. Note that Base run (highlighted) is located between run 25 and run 26.

Something interesting can be observed in Figure 30. Even though it makes total sense to see an improvement in performance with higher power outputs of the charging stations, above certain level (300 [kw]) the improvement in feasibility of the schedule is almost inexistent. This means that infeasibility is due to other reasons at this point.

From the 32 charging stations required in the base scenario, 2 from the garage and 2 from other places become unnecessary when power is set at 400 [kw] (they are not used in the whole simulation). The average utilization of charging stations is 142 [min]. Charging process is so quick that stations are used less time and so availability increases. For the 50 [kw] scenario, the average utilization is 482 [min] per station.

Queues were not a big issue for these runs. Run 23 produces 2 queues with 10 [min] of total waiting time. Run 24 produces 2 queues with 9 [min] of total waiting time. Run 25 produces only one queue with 3 [min] of total waiting time. For power outputs of 200 [kw] and above, no queues are observed.

49 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Figure 31 shows the power in [kw] required for runs 23-29 and Figure 32 shows the detail of active blocks for some of the runs.

Figure 31: Power in [kw] required for Runs 29, 28, 27, 26, Base, 25, 24 and 23.

Figure 32: Detail of active blocks per status for base run and runs 24, Base, 27 and 22

The right side of Figure 31 (highlighted) shows that Runs 23 and 24 require a constant power supply in the garage until the simulation is over. Furthermore, many blocks are still below 100% SOC at the end of the simulation. This makes the whole schedule problematic, because in reality the schedule is supposed to be repeated every day and it requires all the blocks to begin the day fully charged.

50 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 5.8. Impact of charging stations power on schedule

Runs 30 to 32 keep the base settings but consider increasing setup charging time between 0 [min] and 3 [min]. The inductive charging technology explained in Section 2.1.2 allows a setup charging time of 0 [min] (the energy transfer to the battery begins immediately and wirelessly as the bus approaches the charging area), while the automatic or manual plug-in preparation take between one and three minutes. The modifications for each run are shown in Figure 33.

Figure 33: Modifications to setup charging time of base run for runs 30, 31 and 32.

The impact of modifying this parameter on the feasibility of the blocks is shown in Figure 34.

Figure 34: Impact of the setup charging time of charging stations on the feasibility of the vehicle schedule.

The impact of different setup charging times on the feasibility of the schedules is small compared to other parameters. No queues are observed in any of these runs, and all the blocks reach 100% SOC before the simulation ends.

5.9. Impact of battery pack size on schedule

An aspect that certainly has an impact on range of the vehicles is the battery pack size. The base run considers a 200 [kwh] battery, similar to the mid-size battery of the New Flyer XE40 bus. But some bus models like the Proterra BE40 have a much smaller battery (less than 100 [kwh]), while models like the BYD electric bus designed for an overnight charging strategy can be loaded with battery packs larger than 300 [kwh]. An

51 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] interesting question can be formulated at this point: What is the minimum battery pack size required to make all the blocks of the schedule feasible? Runs 33 to 40 keep the base settings but simulate an increasing battery pack size from 50 [kwh] to 600 [kwh]. The modifications for each run are shown in Figure 35.

BASE RUN RUN 33 RUN 34 RUN 35 RUN 36 RUN 37 RUN 38 RUN 39 RUN 40 Parameter Unit Explanation Parameter Parameter Parameter Parameter Parameter Parameter Parameter Parameter 200 [kwh] Battery Size 50 100 150 250 300 400 500 600 50 % Objective Battery Level 50 50 50 50 50 50 50 50 17 [min] Minimum Layover Time for Charging 17 17 17 17 17 17 17 17

0 % HVAC Penalty 0 0 0 0 0 0 0 0 2 [min] Minimum Layover 2 2 2 2 2 2 2 2 Figure 35: Modifications to setup charging time of base run for runs 32 to 40.

The impact of modifying this parameter on the feasibility of the blocks is shown in Figure 36.

Figure 36: Impact of the setup charging time of charging stations on the feasibility of the vehicle schedule.

It is clear that the size of the battery pack has a big impact on the feasibility of the blocks of the schedule. Given the base settings, installing 50 [kwh] batteries results dramatic for the schedule, since none of the blocks is feasible. For batteries above 400 [kwh] there is almost no contribution, because 97% of the blocks are feasible with 400 [kwh] batteries. The 100% feasibility is reached somewhere between 500 [kwh] and 600 [kwh]. The greatest contribution to feasibility of the schedule is observed when increasing from 100 [kwh] to 150 [kwh] and from 150 [kwh] to 200 [kwh].

52 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] 6. Conclusions

This thesis has two objectives: first to identify Zero-emission (ZE) aspects that have an impact on the planning process (PP), and second to quantify such impact. To attend both objectives, this thesis was divided in two phases. Phase I reviews literature to find all the aspects of ZE fleets that can be relevant for the PP. Phase II enhances the current model used by RET planning department by incorporating the ZE aspects into it, and simulates the current vehicle schedules considering a ZE fleet scenario. Finally, it measures to what extent the feasibility of the vehicle schedules is affected by ZE aspects found in Phase I.

6.1. Main findings in Phase I

Phase I of this thesis aimed to answer the following questions: What characteristics of the ZE buses have significant impact on energy consumption, charging times and range of the vehicles? What characteristics of the routes and the environment have significant impact on energy consumption, charging times and range of the vehicles? What parameters should be considered for ZE planning? How should those parameters be incorporated in the PP?

Key characteristics to consider for a ZE fleet from the planning perspective are energy consumption, charging time and range of the vehicles. This thesis found in literature some ZE aspects which have an impact on these characteristics.

One important aspect that should be considered for ZE planning is the HVAC unit in the electric buses. Studies presented in this thesis state that HVAC units onboard can represent up to 40% of the total energy consumption of the bus. This thesis proposed to incorporate this aspect into the PP adding a “penalty factor” to the energy consumption.

Another ZE aspect that seems relevant is the driving cycle. Literature is not conclusive at all; while some studies presented in this thesis found differences of more than 50% in energy consumption between different driving cycles, others found negligible differences. This thesis suggested incorporating the possibility to differentiate between driving cycles, and designing a follow-up research which takes real measurements of consumptions at different driving cycles in the city of Rotterdam.

The aspects which have an impact on the range of the vehicles and charging times are the size of the battery, the characteristics of the charging stations (like power output, location and energy transfer efficiency) and the queue handling at the stations. Studies presented in this thesis observed efficiency rates around 90% for the charging process (applied to the nominal power output of charging stations).

Other aspects like the energy recovery of regenerative braking or transmission systems may be relevant as well, but they are already considered in the technical specifications of battery electric buses (BEB) and most manufacturers include them on their vehicles, so can be omitted for planning purposes.

6.2. Main findings in Phase II

Phase II of this thesis aimed to answer the following questions: Are the current schedules feasible given the ZE aspects? Why and when do the current schedules become infeasible given the ZE aspects? How sensitive are the schedules to each ZE aspect?

53 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] One of the main outputs of the PP is the vehicle schedule, which is an allocation of trips into blocks to be executed by vehicles. The approach followed by this thesis, was to test the current schedules under new conditions imposed by the ZE fleet. To test the schedules, this thesis presented a simulation which followed the SOC of each block at each minute and measured to what extent the blocks of the schedules were feasible.

Using the number of feasible blocks of the schedule as main indicator, this thesis made a sensitivity analysis over the ZE aspects to understand the magnitude of the impacts.

The “layover time for charging” parameter turned out to have a big impact on feasibility of blocks. For a 9 [min] layover 104 out of 128 blocks were feasible, while for 21 [min] only 49 were feasible. Also, 14 queues were observed for a 9 [min] layover. This suggests that RET should try to use all the layover times for charging, even the short layovers.

HVAC units can also have a great impact on feasibility of blocks. Switching on the HVAC unit can reduce the number of feasible blocks dramatically. The schedule simulated had 83 feasible blocks (out of 128) when the HVAC unit was off, and only 28 of them remained feasible when the HVAC unit was switched on. This suggests that RET could have different feasibility levels depending on temperature conditions. In winter, for example, when heaters are heavily used, the feasibility of the schedules may decrease significantly.

Simulations which included significant reductions in the power output (in [kw]) of charging stations, generated an undesired situation. Some blocks stayed the whole night waiting to be charged in the garage and could not reach full charge at the end of the simulation. Schedules are designed to be executed every day, so if a schedule has feasible blocks but such blocks cannot reach SOC=100% before the simulations ends, then the schedule cannot be properly executed the next day. This situation was observed for stations providing less than 150 [kw] of power. This suggests that RET should consider enough time and/or charging stations in the garage to ensure full charge during the night.

Simulations where the power output of charging stations was increased, show that there is a small impact on feasibility of blocks for power above 300 [kw].

Another important aspect tested was the size of the battery pack onboard. Given the basic settings of the simulation, described in Section 5.1, batteries of 50 [kwh] capacity made all the blocks infeasible. But for batteries of 400 [kwh] capacity, around 97% of the blocks were feasible. For bigger batteries, the difference in feasibility is very small, and 100% feasibility is reached between 500 [kwh] and 600 [kwh]. Even though the battery capacity is an “input” for this thesis (result of a purchasing decision), RETPD should be aware of the battery size chosen when designing the schedules.

Driving cycles also have a great impact on feasibility of current vehicle schedules. When considering a unique consumption of 1 [kwh/km], almost all the schedule simulated could be executed without problems (124 out of 128 blocks were feasible) while when considering a consumption of 1.5 [kwh/km] only 73 blocks remained feasible. As stated in Section 6.1, this thesis strongly encourages RETPD to take real measurements in the city of Rotterdam, because the literature is not conclusive about the consumptions of different driving cycles and the impact on the feasibility of schedules can be significant.

Other aspects turned out to be less significant from the planning perspective. For example, purchasing an inductive charging system which reduces the setup time from 2 or 3 [min] to 0 [min] produces small improvements on feasibility of vehicle schedules. The simulation showed an improvement of 6 additional feasible blocks (out of 128) when reducing from 3 [min] to 0 [min] the setup charging time.

54 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

This thesis defined a base simulation with 32 charging stations which were the minimum to run without queues in the stations. But for one subsequent run, 9 charging stations were deleted (28%) and the result regarding feasibility of vehicle schedules was almost the same (only 2 blocks became infeasible out of the 83 initially feasible). This suggests that, depending on investment costs, it may be more efficient to do other investments than adding charging stations in certain places.

Also, this study suggests RET to be aware of the fact that queues are generated at charging stations under certain settings, so the departments involved shall communicate and make decisions together.

Finally, this study suggests that shorter blocks are, in general, more feasible than larger blocks. If shorter blocks were designed and enough time was considered after their last trips, feasibility may improve.

6.3. Discussions

This thesis presented a way of measuring the impact of a ZE fleet on the Planning process, specifically on the current vehicle schedules. It considered the number of “feasible blocks” as key indicator of such impact. As seen in the results, many aspects of a ZE fleet have a great impact on the feasibility of the blocks. In fact, only one setting was observed to allow a 100% feasibility, and included installing 600 [kwh] batteries which, given the bus models available in the market, seems not realistic. In other words, it is highly likely that the ZE fleet will require changes in the planning process of RET. This result confirms the belief that planning department of RET had from the beginning; aspects related to ZE fleets do have an impact on the Planning process.

Of course, the definition of “feasible block” depends on the OBL parameter and the level of risk that RET Planning department is willing to accept. If experience from RET allows to reduce the risk of running out of energy, some infeasible blocks could be allowed.

There is a very important aspect of migrating to a ZE fleet that was left out by this thesis, related to operation costs and investments. RET could use the results of this thesis to support purchasing decisions, but not as the only relevant component to take into account. From the perspective of this thesis, it may be a “good result” to have a few more feasible blocks, but if the investments required for those additional feasible blocks are huge, there may exist alternative, cheaper solutions.

6.4. Recommendations and follow-up research

This thesis approached to the problem of planning ZE fleets by the analysis of academic literature and simulating the current vehicle schedules under ZE conditions. It recognizes that simulating schedules shall not become the core activity for the PD in the future, because at some point the ZE conditions should be incorporated as constraints and parameters of the schedule generation problem, but it provides useful insights on the possible changes and adaptations required.

Whatever the solution adopted is, it should consider: the ability to “add” or “delete” charging stations to different places of the network, including characteristics of the charging station like power output in [kw], setup time and efficiency; the size of the battery pack in [kwh]; the option to “switch on” or “switch off” the HVAC unit; the chance to define certain parameters (OBL and Layover for charging) to determine feasibility levels and manage risk; the ability to handle queues at charging stations and implement different rules for charging; the possibility to differentiate energy consumption per driving cycle (urban and suburban routes) and the ability to compute the SOC of each vehicle at each moment of the schedule.

55 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Finally, this thesis suggests that a follow-up research should be conducted by planning department at RET, aiming to take real measurements of energy consumption for different driving cycles in the city of Rotterdam.

56 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] References

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60 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Appendix 1: Current fleet of RET

Manufacturer: Mercedes Benz Origin: Germany Fleet: 165 buses Bought: 2006 Capacity: 34 passengers sitting / 64 passengers standing Propulsion: regular diesel engine

Manufacturer: MAN Origin: Turkey Fleet: 89 buses Bought: late 2012 Capacity: 43 passengers sitting / 45 passengers standing Propulsion: regular diesel engine

(RESERVE BUSES) Manufacturer: Den Oudsten Origin: The Netherlands Fleet: 20 buses Bought: late 2012 Capacity: 43 passengers sitting / 45 passengers standing Propulsion: regular diesel engine

Manufacturer: Mercedes Benz Origin: Germany Fleet: 2 buses Bought: 2009 Capacity: 45 passengers sitting / 81 passengers standing Propulsion: diesel hybrid system. A diesel engine charges batteries onboard.

61 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Manufacturer: VDL Origin: The Netherlands Fleet: 2 buses Bought: 2010 Capacity: 33 passengers sitting / 27 passengers standing Propulsion: diesel hybrid system. Batteries can be charged directly from an external electricity source through a plug. Diesel engine onboard as backup.

62 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Appendix 2: Inputs of the simulation tool

Implementation of enhanced model in MSEXCEL

The enhanced model, which considers many ZE aspects and conditions described in Section 3, was implemented in MSEXCEL format using basically two worksheets: the “Parameters” worksheet and the “ConsumptionPerRoute-Pattern” worksheet. The simulation takes the required data from these worksheets. In addition, an auxiliary worksheet was used during development to support calculations.

“Parameters” worksheet

An overview of the “Parameters” worksheet is shown in Figure 37. It has four areas, each one containing different elements of the model, shown in Figure 38. These elements are explained in Section 3.4.

Figure 37: Overview of the “Parameters” worksheet from simulation tool developed in MSEXCEL.

Main Area Buttons Area Battery Size parameter in [kwh] "Clean" button: triggers a macro which erases the results from previous simulations. Objective Battery Level (OBL) in [%] "General Run" button: runs the simulation. Minimum Layover in [min] HVAC penalty in [%] Layover for charging in [min] Charging stations Area Route-Pattern assessment Area Location per charging station Consumption per driving cycle in [kwh/km] Name of each charging station Limits in average speed range per driving cycle in [km/hr] Power output in [kw] per charging station Limits in average distance between stops range per driving cycle in [km] Charging efficiency in [%] per charging station Setup Charging Time in [min] per charging station Figure 38: Four areas of the “Parameters” worksheet from simulation tool developed in MSEXCEL.

63 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] All the elements shown in Figure 38 can be modified by the user. Some parameters may be difficult to understand at this point, so examples are given below.

Something that should be avoided is a bus running out of energy in the middle of a trip, with passengers on board. This is a major issue for RET. The OBL parameter is considered by the simulation tool as a “safe” SOC to prevent the risk of empty batteries. For example, if OBL is set at 50%, the tool warns using colors when the SOC drops below 50%. Trips are not allowed to start with a SOC below OBL, so the ones that do are considered infeasible.

Another major issue for RET are the delays. If all the trips of a block were scheduled without layover windows in between, a small delay in one of the trips can cause increasing delays in all the following trips of the block. That is why the “Minimum Layover” parameter is important. Regardless the Minimum Layover, RETPD must set a “Layover Time for Charging” parameter. If a layover is longer that this parameter, it is treated by the tool as a “charging layover” (see Section 4.5). Figure 39 shows a conceptual explanation for a “Minimum Layover” set at 2 [min] and a “Layover Time for Charging” set at 17 [min].

Figure 39: Explanation of a “Minimum Layover” set at 2 [min] and a “Layover Time for Charging” set at 17 [min].

When a layover of the schedule is shorter than “Layover Time for Charging” parameter, the simulation tool does not try to charge batteries in such layover. But when a layover is longer than “Layover Time for charging” parameter, the tool always tries to charge the batteries in such layover. The charging process remains active until a SOC 100% is reached or a new trip begins. More details on the charging rules (or charging strategy) are given in Section 4.5.1.

“ConsumptionPerRoute-Pattern” worksheet

This worksheet contains a small database of all the RP considered in the model and their consumption in [kwh/km]. The consumption for each RP is the result of the assessment proposed in Section 3.2, considering as parameters the consumption, speed range and distance range defined in the Route-Pattern assessment Area of “Parameters” worksheet (see Section 4.2.1). Figure 40 shows a screenshot of an example of the worksheet “ConsumptionPerRoute-Pattern”.

64 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] The database has four columns. The “Route” column which identifies the route, the “Trip Pattern” which identifies the pattern, the “Concat” column which is a simple concatenation of the first two columns (used by the simulation to identify each unique RP combination), and the “Assessment per Route-Pattern” column which contains the result of the assessment in [kwh/km] for each RP following the methodology proposed in Section 3.2.

Since the RP assessment methodology proposed in Section 3.2 evaluates the “segments” of each RP, the model implementation in this thesis used an auxiliary worksheet with a database of segments. Each segment belongs to a RP combination (identified by the “Concat” column), and can be evaluated according to its average speed and average distance between stops using the parameters in “Parameters” worksheet. The use of an auxiliary worksheet with segments allows to update the assessment automatically when changing the related parameters in “Parameters” worksheet.

Figure 40: Screenshot of an example of “ConsumptionPerRoute-Pattern” worksheet.

Vehicle schedule in MSEXCEL format

The conceptual structure of a vehicle schedule is explained in Section 2.4.3, while Figure 4 shows a common visualization (in a two dimensions’ grid) which is easy to understand. However, the grid is only a visualization; the basic vehicle schedule consists of a table with data, which can be exported from the software used by RETPD to a MSEXCEL format. Figure 41 shows a screenshot of an example of vehicle schedule exported to MSEXCEL and kept in a dedicated “schedule” worksheet.

65 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 41: Screenshot of a vehicle schedule exported to MSEXCEL format.

Each row of the “schedule” worksheet represents a Trip. The columns “Route” and “Pattern” (columns A and O respectively) define the RP covered by each trip (concatenated). The simulation combines this information with the “ConsumptionPerRoute-Pattern” worksheet to figure out what is the consumption in [kwh/km] for each trip of the schedule. Column “Type” (B) contains information for internal purposes and is not relevant for the simulation. Columns “From” and “To” (C and F respectively) contain the place of origin and destination of each trip. This information is relevant for the simulation, because the charging stations are created in specific places. Columns “Start” and “End” (D and E respectively) contain the exact time of departure from origin and arrival to destination. Column “EndLay” (G) contains the layover in [min] considered after each trip in the destination place. Columns “Next”, “Nxt Rte” and “Trp (next) end place” (H, I, and J respectively) contain information about the trip that follows after each trip is ended. Column “Trip” (K) contains a unique identification for each trip. Column “Block” (L) contains the block to which each trip belongs (see Section 2.4.3 and Table 3 for an explanation on blocks). Columns “Distance” and “Duration” (M and N respectively) contain the distance in [km] and duration in [min] of each trip.

Using as example the row 108 (first one visible) of the worksheet in Figure 41, a correct interpretation of the data would be the following: “The trip 123 belongs to the block B173-004 and covers the Route-Pattern B173P86 traveling between BlwKor and StRdr. The trip begins at 6:54 and ends 24 [min] later at 7:18 after a 10.681 [km] drive. It stays 7 [min] in StRdr before departing again with destination CentWe”.

66 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Appendix 3: VBA program of the simulation tool

Development of simulation program in VBA.

As stated in Section 4.4, every one of the blocks is assumed to be executed by a different bus (one block – one bus). The base of the simulation program is a two-dimensional array of variables called “SOC array”, where one dimension represents time and the other dimension contains all the blocks considered. The program iterates using “for” loops over all the minutes of a 24-hours period (1440 iterations) and computes the SOC for each block at the beginning of each minute. It stores each computation result in the SOC array. Conceptually, this array of variables is similar to the “grid” visualization of the schedule shown in Figure 4.

In addition to the main array containing the SOC for each block at each minute, another six arrays are created with the same dimensions, to store relevant information. Also, another array is used to store the status of each charging station at each minute. It has 1440 rows and as many columns as charging stations are considered in the simulation. Many other variables are created to allow the execution of the program. They are used with different purposes: as counters, to carry information between different worksheets, or even as auxiliary variables to support calculations. A summary of the arrays is shown in the Table 20.

Table 20: Summary of most important arrays used by the simulation program to store data. Array Data type Dimensions Data stored:

SOC (main) Numeric Rows: 1440 minutes The SOC in [%] for each block at the beginning of each minute. Columns: as many as blocks

Route-Pattern Text Rows: 1440 minutes The RP which is being covered by each block each minute. Columns: as many as blocks

Consumption Numeric Rows: 1440 minutes The consumption in [kwh/min] of each block each minute. It can Columns: as many as blocks be a negative number, when the block is charging.

Place Text Rows: 1440 minutes The name of the Place where each block is located each minute. If Columns: as many as blocks a bus is moving at a certain moment, the word "Travelling" is stored for that block.

TripID Text Rows: 1440 minutes The trip being covered by each block each minute. If at certain Columns: as many as blocks moment the block has a Layover, the TripID is left empty during the Layover time.

Status Text Rows: 1440 minutes Status of each block each minute. Columns: as many as blocks

ChgName Text Rows: 1440 minutes The name of the charging station being used by each block each Columns: as many as blocks minute. If a block is not using a charging station at a certain moment, ChgName is left empty.

ChargerUsed Text Rows: 1440 minutes The name of the block which is using each charging station each Columns: as many as Charging minute. If the charging station is free at a certain moment, stations ChargerUsed is left empty.

Any block at any specific minute has a “Status”. The simulation tool considers eight possible status for a block, which are explained in Table 21. The main function run by the simulation program is described in this Section using a pseudocode, shown in Figure 42.

67 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Table 21: Eight possible status for a block at any minute of the simulation. Value of Explanation STATUS(MIN,BLOCK)

"Start" Means a new trip belonging to block "BLOCK" begins at minute "MIN". This status only lasts for one minute, because for minute "MIN+1" the status of "BLOCK" will become either "Travelling" (if the trip is longer than one minute) or "End"/"EndForChg" (if the trip takes only one minute).

"Travelling" Means block "BLOCK" is executing a trip at minute "MIN". In other words, the bus assigned to BLOCK is actually moving.

"End" Means a trip belonging to block "BLOCK" ends at the beginning of minute "MIN", and layover time ahead is not long enough to allow charging. This status only lasts for one minute, because for minute "MIN+1" the status of "BLOCK" will become either "Layover" (if there are no new trips at "MIN+1") or "Start" (if a new trip begins at "MIN+1").

"Layover" Means block "BLOCK" already ended a trip before minute "MIN" and is waiting for the next trip. It is not looking for a charging station, because either has SOC=100% or the total layover time is not long enough to allow charging.

"EndForChg" Means a trip belonging to block "BLOCK" ends at the beginning of minute "MIN", and layover time ahead is long enough to allow charging. This status only lasts for one minute, because for minute "MIN+1" the status of "BLOCK" will become "LayoverForChg".

"LayoverForChg" Means block "BLOCK" already ended a trip before minute "MIN" and is waiting for the next trip. The total layover time is long enough to allow charging, so the block is also waiting for a charging station to become available in the place where it is.

"Charging" Means block "BLOCK" found an available charging station and is using it during minute "MIN" to recharge batteries.

"Available" Means block "BLOCK" already completed all the trips, arrived back at the garage and recharged batteries until SOC 100% was reached at minute "MIN".

1 Store all the parameters in variables 2 Count number of unique blocks found in the schedule 3 Initialize arrays with 1440 rows, and as many columns as unique blocks found 4 5 For each MIN from 1 to 1440 do: 6 7 For each BLOCK 8 Look in schedule for a trip belonging to BLOCK that ends at current minute MIN. 9 If found, do: 10 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) - energy consumed in previous minute. 11 Check in schedule the “time to next trip” information 12 If “time for next trip” > “Layover Time for charging” parameter, do: 13 STATUS(MIN,BLOCK) = “EndForChg”. Populate other arrays with data. 14 Else, do: STATUS(MIN,BLOCK) = “End”. Populate other arrays with data. 15 Else, do: leave arrays empty. 16 Next BLOCK 17 18 For each BLOCK 19 Look in schedule for a trip belonging to BLOCK that starts at current minute MIN. 20 If found, do: 21 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) + energy charged in previous minute (if any) 22 STATUS(MIN,BLOCK) = “Start”. Populate other arrays with data from schedule.

68 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

23 Else, do: leave arrays empty. 24 Next BLOCK 25 26 For each BLOCK 27 If SOC(MIN,BLOCK) is empty at this point, do: 28 If STATUS(MIN-1,BLOCK) = “Layover” or STATUS(MIN-1,BLOCK) = “End”, do: 29 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) 30 STATUS(MIN,BLOCK) = “Layover”. Populate other arrays. 31 If STATUS(MIN-1,BLOCK) = “EndForChg”, do: 32 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) 33 STATUS(MIN,BLOCK) = “LayoverForChg”. Populate other arrays. 34 If STATUS(MIN-1,BLOCK) = “Travelling” or STATUS(MIN-1,BLOCK) = “Start”, do: 35 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) - energy consumed in previous minute. 36 STATUS(MIN,BLOCK) = “Travelling”. Populate other arrays. 37 If STATUS(MIN-1,BLOCK) = “LayoverForChg”, do: 38 Look for an empty charger in the current Place PLACE(MIN-1,BLOCK). 39 If found, do: 40 Take the charger doing: CHARGERUSED(MIN,CHG) = BLOCK 41 STATUS(MIN,BLOCK) = “Charging”. Keep same SOC. Populate other arrays. 42 Else, do: STATUS(MIN,BLOCK) = “Layover for charging” 43 If STATUS(MIN-1,BLOCK) = “Charging” and SOC <100%, do: 44 SOC(MIN,BLOCK) = SOC(MIN-1,BLOCK) + energy charged in previous minute. 45 STATUS(MIN,BLOCK)= “Charging”. Populate other arrays. 46 If STATUS(MIN-1,BLOCK) = “Charging” and SOC >100%, do: 47 SOC(MIN,BLOCK) = 100% 48 Release charger doing: CHARGERUSED(MIN,CHG) = empty 49 STATUS(MIN,BLOCK) = “Layover”. Populate other arrays. 50 Next BLOCK 51 Next MIN Figure 42: Pseudocode of the main function run by the simulation program.

After filling ending and starting trips with data from the schedules, the remaining “empty” cells of the array are treated with pseudocode between lines 26 and 50. If a cell is empty at this point, the options are: the block is either in the middle of a layover (“Layover” status), or in me middle of a trip (“Travelling” status), or waiting for an available charger (“LayoverForChg” status) or is being charged in a charging station (“Charging” status). Depending on what is the status of the previous minute, the simulation knows which is the status for the current minute and can fill the arrays without any additional data from the schedule.

Even though the pseudocode shows the four most important “for” loops of the program, some lines require many small cycles to be executed. For example, lines 8 and 19 require a loop to explore the schedule and look for ending or starting trips. The same happens to line 38; a loop is required to look for an empty charger in the “ChargerUsed” array.

In addition to the pseudocode presented, the tool includes other functions executed beforehand, which check all the data to make sure everything is ok. If an error is found, the simulation is aborted and the user is warned about the errors in input data. Other functions are required at the end as well. After the simulation is ready, a function is required to “print” the arrays in the output worksheets and another function is required to add format to the worksheets and make them easy to read.

69 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] Appendix 4 Outputs of the simulation tool

The results of the simulation are recorded in four worksheets. The most important is the “Result_VehView” worksheet, which is printed by the simulation with data of all the arrays in Table 20 except for the ChargerUsed array. After the data is printed, format is added to improve visualization. Data from ChargerUsed array is printed in a different worksheet, called “Result_ChgView”. The worksheet “Graphs_VehView” is also printed with data from SOC array. Finally, “Performance” worksheet is calculated using data and format of “Result_VehView” as basic information. More details are given in following sections.

“Result_VehView” worksheet

This worksheet shows the data from most of the arrays after the simulation is run. Following the structure of the arrays, each row of the worksheet represents a minute of time and each block is represented by a set of six columns. The size of the grid can be quite big. For example, if a schedule with 20 blocks was run by the simulation, the worksheet would contain 120 columns and 1440 rows of data. Hence, the formatting is key to make it easy to read. Figure 43 shows a screenshot of “Result_VehView” worksheet, taken with a zoom level of 25%. It includes 10 blocks and a period of time of 110 minutes (between 6:50 and 8:40). Even though the data cannot be read in the screenshot, it is easy to identify the blocks and trips by colors. Each block is represented by a set of columns with blue header and a grey column next to them. From the set of columns with blue header, cells belonging to trips have white background color and cells belonging to layovers have grey background color. Yellow and red colors are added when the SOC of a block drops below a dangerous level, defined by the OBL parameter. Green color is added in layover periods where the batteries are charged. More details on this color formatting are given in Figure 44.

Figure 43: Screenshot of “Result_VehView” with zoom level of 25%.

The partial view of the “Result_VehView” worksheet seen in Figure 19 shows the simulation of a block named “B032-002”, between 8:30 and 9:52. Note that some periods of time were “hidden” for visualization purposes (between 8:40 and 9:09 and between 9:16 and 9:22).

70 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] The first column next to the time shows the data from SOC array. The second column shows data from Consumption array.

The third column shows the data from TripID array, but it also shows the data of the ChgName array. This helps to save space and no conflict occur because they cannot have data for the same block for the same period (a block cannot recharge batteries and execute a trip at the same time).

The fourth column shows the data from Route- Pattern array, but it also shows the value of “Setup Charging Time” parameter when the block is charging. Again, there is no conflict between these two arrays.

The fifth column shows the data from the Place array. And the sixth column (grey) shows the data from the Status array. Note that when the status is “Travelling”, the place is also “Travelling”, so

status is omitted. Figure 44: Details on color formatting in “Result_VehView” worksheet.

Following the example on Figure 44, the block B032-002 was travelling the trip 119 at 8:30, covering the Route-Pattern B032P45, with a SOC of 51%. At 8:33 the SOC dropped below 50% (defined in this instance as the OBL parameter in the “Parameters” worksheet), so the simulation colored the cells in yellow. At 8:35 the trip ended at place “StZuid”, where a 2-minute layover elapsed. Then at 8:38 a new trip began (1407), covering the RP B032P35. Since trips are not allowed to begin with SOC below OBL, this trip was considered as “not feasible”, and was colored in red (see Section 4.6.4 for explanation on “feasible trips” and “clean trips”). At 9:12 the infeasible trip arrived at place “NwykSt”. Since the layover there was longer than the “Minimum Layover for Charging” parameter of 17 [min] (defined as such in this instance in “Parameters” worksheet), the simulation began looking for an empty charger after the 2 [min] of “Minimum Layover” (also a parameter). It immediately found an empty charger called “Mid2”, which had a “Setup Charging Time” of 1 [min] and enough power to provide 1.8 [kwh/min] to the battery. During the charging time, the cells of the second column were colored in green and the SOC went up again to levels above OBL. The charging ended at 9:29, when a new trip should begin. Note that the trip starting at 9:29 (TripID=128) covers the Route- Pattern B038P79, while the previous trip covered the RP B032P35. The route covered changed from B032 to B038 within the same block, so this is an example of interlining.

“Result_ChgView” worksheet

This worksheet shows the data from the ChargerUsed array. Following the structure of the array, each row of the worksheet represents a minute of time and each column represents a charging station. Figure 45 shows a screenshot of “Result_ChgView” worksheet. It includes 10 charging stations. Three of them are located at place “RtCbus”, two at “GarKlw”, two at “StRdr”, one at “StSchB”, one at “NwykSt” and one at “CentWe”. The period of time shown includes 23 minutes (between 6:50am and 8:40am). Apart from the place, the

71 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] headers of the columns contain the name of the charging station, the charging speed in [kwh/min] and the Setup Charging Time (all taken by the simulation program from the “Parameters” worksheet). Each cell of the grid contains the name of the block which is using each charging station at each minute. For example, the station called “Fast1”, located in RtCbus, which is powerful enough to provide 3 [kwh/min] and takes 2 [min] to setup, was used by block B040-006 between 9:11 and 9:28.

Figure 45: Partial screenshot of “Result_ChgView” worksheet.

Note that the station “Mid2” (fifth column), located in “NwykSt”, was used by block B032-002 between 9:14 and 9:28. This information is coherent with the information shown in Figure 44, where the charging station “Mid2” was assigned to block B032-002 in the same period. At 9:29 the station was released by the block and immediately taken by another block (B032-006).

The first row visible in Figure 45, contains the “Total Utilization [min]” indicator. This indicator counts the number of minutes each charging station was used during the simulation. For example, station “Slow1” located in RtCbus was used only 22 [min] in total (out of 1440 [min] run by the simulation), while charging station “Fast3” located in StSchB was used 710 [min] (almost half of the total time). This gives a hint on the importance of considering extra charging stations in some places and the impact of taking them out.

“Performance” worksheet

This worksheet shows performance indicators than calculated with the data from the simulation. These indicators constitute the way of measuring the impact of the ZE parameters on the schedule. The following paragraphs define a few concepts which must be clarified before checking the “Performance” worksheet. Figure 46 shows an example of a block to support the explanations.

72 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

For each minute of the simulation, every one of the blocks is defined to be “Active” or “Inactive”. As example, considering the block of Figure 46 which begins at 6:37 and arrives to the garage at 9:54, such block would be declared as “Active” for the 197 minutes between 6:37 and 9:54, and “Inactive” the rest of the time. While being active, a block can have any status (“Travelling”, “Layover”, “Charging”, etc). Even though the simulation considers a charging time in the garage after the last trip of the block ended, this charge is not considered to be active. Only the period between the first minute of the first trip of the block, and the last minute of the last trip of the block is active.

“Infeasible trip” refers to a trip which begins with SOC below OBL, and is colored red by the simulation. “Feasible trip” then refers to a trip which begins with a SOC above OBL.

If SOC drops below OBL in the middle of a trip, the trip is colored yellow by the simulation. “Clean trip” refers to a trip which does not have any yellow nor red cell. Note that all clean trips are feasible, but not all feasible trips are clean. Figure 46: Example of block showing active period and feasible and clean trips.

“Feasible block” refers to a block which has only feasible trips (it allows yellow cells, but not red cells). “Clean block” refers to a block which has only clean trips (it does not allow any yellow nor red cell). Note that all clean blocks are feasible, but not all feasible blocks are clean.

A “queue” refers to the situation in which a block is looking for a charging station in a certain place, but there is no station available, so it must wait.

Figure 47 shows a screenshot of the “Performance” worksheet. Although very simple, this worksheet is very important. One of the objectives of this thesis is to quantify the impact of the ZE aspects in the PP. The “Performance” worksheet shows very clearly how the ZE aspects (identified in the enhanced model and implemented in the “Parameters” worksheet of the simulation tool) impact the feasibility of the vehicle schedule.

73 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

Figure 47: Screenshot of “Performance” worksheet.

The “Performance” worksheet has three areas: “Feasibility area”, “Queue area” and “Places area”.

The feasibility area contains six basic indicators: total number of blocks simulated, number of feasible blocks, number of clean blocks, total number of trips simulated, number of feasible trips and number of clean trips. Also shows the proportion of feasible and clean blocks relative to the total number of blocks simulated, and feasible and clean trips relative to the total number of trips simulated. The higher the proportions, the more “feasible” (less risky) is the vehicle schedule. If a block has many feasible trips and only one infeasible trip at the end, the block is counted as infeasible. This means that the block-related indicators are much more sensitive to infeasibility than the trip-related indicators.

The queue area contains a big table, where each row represents a block. The first column shows the number of times each block had to wait for an available charging station (number of queues), and the second column shows the total time in minutes that each block spent in queues. The third column shows the total time in minutes that each block was active, which is data taken directly from the schedule. In other words, the third column is not precisely an output of the simulation, but just a visualization of the schedule (input).

The places area also contains a big table. The data contained in this table comes directly from the schedule and parameters, so it is not an output of the simulation but a visualization of the inputs. The rows here represent minutes simulated and the columns represent the places where charging stations are installed. The first blue row contains the names of places. The second blue row shows the number of charging stations for each place (which gives the “charging capacity” of the place). Each white cell of the table represents the number of blocks that were at that place at that minute (including “charging” blocks and “layover” blocks). Above the names of places there is a white row with numbers. These numbers represent the maximum number of simultaneous blocks that stopped at that place during the simulation. Taking as example the screenshot of Figure 47, at 6:24 there was only one block in place “StSchB” but a second one arrived there at 6:25. The charging capacity of “StSchB”, given by the number of charging stations, is 4. However, at some

74 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected] moment of the simulation there were 7 blocks at that place, but those blocks were not waiting in a queue because the queue area shows no queues.

“Graphs_VehView” worksheet

This worksheet shows four charts which help to visualize the results of the simulation. The first one is a line chart of the SOC of every block in time, compared to the OBL. The simulation program uses the data from the SOC array to build the chart. Figure 48 shows an example of the chart generated for a simulation of four blocks with an OBL of 50%.

Figure 48: Example of first chart, showing SOC per minute per block, for a small simulation of four blocks.

This gives a great overview of how did the SOC behave during the simulation, with many of them following a “saw” shape. The SOC decreases during trips, but increases during charging time. The increasing rate (given by the steep slope of the line) is higher than the decreasing rate, because the charging process adds provides more energy per minute (in [kwh/min]) than the energy per minute consumed during trips. Note that if too many blocks are simulated the chart may become saturated. The tool does not apply any criteria regarding this situation.

The second chart shows the total power in [kw] required at the charging stations at each minute for all the blocks. Figure 49 shows an example of the chart. Even though this information is not critical for the PP, it may be useful for RETPD to make sure that enough electric power is available in the places where they think charging stations could be located.

Figure 49: Example of second chart, showing total power required per minute at all the charging stations.

75 “Preparing for a zero-emission fleet at RET” Guillermo A. Beuchat Beroiza | [email protected]

The third chart shows the total number of active blocks at each minute of the simulation. An example of this chart is shown in Figure 50. Since the number of active blocks at each minute does not depend on any parameter, but only on the vehicle schedule provided by RETPD, this chart remains fixed while the schedule simulated is always the same. In other words, this chart is not precisely an output of the simulation, but just a visualization of the schedule.

Figure 50: Example of third chart, showing number of active blocks per minute.

The fourth chart also shows the number of active blocks, but it also gives details of the status of active blocks at each minute. An example is shown in Figure 51. Blocks with status “charging” are shown in yellow, as “Charging Blocks”. Blocks with status “Layover”, “Layover for charging”, “End” or “End for charging” are shown in grey, as “Layover Blocks”. Blocks with status “Travelling” or “Start” are shown in orange or blue, depending on the SOC. If the SOC of a block is below OBL, the block is shown in orange as “Travelling unclean Blocks”, and if the SOC of a block is above OBL, it is shown in blue as “Travelling clean Blocks”. This chart does change depending on parameters. The total number of active blocks is fixed, but the status may change.

Figure 51: Example of fourth chart, showing detail of active blocks per minute opened by status group.

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