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Eindhoven University of Technology

MASTER

The strategy to increase the adoption rate of electric vehicle

Sari, Titi

Award date:

2017

Link to publication

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Eindhoven, August 2017

The Strategy to Increase

The Adoption Rate of Electric Vehicle

by Titi Sari

Student identity number 0981178

in partial fulfillment of the requirements for the degree of

Master of Science

in Operations Management and Logistics

Supervisors:

dr. Tarkan Tan, TU/e, OPAC

dr. Osman Alp, University of Calgary, Canada dr. Maximiliano Udenio, TU/e, OPAC

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TUE. School of Industrial Engineering.

Series Master Thesis Operations Management and Logistics.

Subject headings: electric truck, battery switching station, commercial distribution vehicle, linear programming, vehicle replacement model

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Abstract

This master thesis presents the study of the strategy to adopt electric truck as a commercial vehicle for the company to distribute demand in the Netherlands while considering the charging infrastructure investment. Battery switching station is chosen as a charging infrastructure for an electric truck for this project due to its characteristics that can provide a fully charged battery for electric vehicle (EV) fast, which is three until ten minutes. This adoption strategy provides the schedule when and how many electric trucks should be bought to replace diesel truck, when and how many battery switching stations should be built, also when and how many electric truck’s batteries should be owned. This strategy is developed based on vehicle replacement model, which is developed as a linear programming model and has the objective of minimization total cost of ownership. Several scenarios are examined to the model, and the result shows that oil price, weight demand, emission reduction targets, the limited distance range of electric truck, and EV’s emission can affect the electric truck adoption. The increase in oil price, emission reduction target, the limited distance range of electric truck, demand weight can improve electric truck adoption. Nevertheless, the reduction of emission generated by EV due to the increase of renewable energy resource to generate electricity or due to the improvement in EV’s technology can reduce electric vehicle adoption for this case. In addition to these result, the scenarios for the model also developed to investigate the effect of emission reduction strategy to the electric truck adoption pattern. There are four types of emission reduction target strategies investigated; those are emission reduction target described each year, total emission reduction at the end of the planning horizon, progressive emission reduction target based on the past emission generated, emission reduction target at the end year of the planning horizon. The result shows that all of this strategies have different electric truck adoption patterns. The first and third strategy give fully control to the company to its adoption plan as it can dictate the system to have electric truck each year. However, the second and fourth strategies give more flexibility to the system to decide when and how many electric trucks are owned. The second strategy also has the behavior to adopt electric vehicle in the latter years and might adopt many EV at the end of the planning horizon.

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Acknowledgment

Eindhoven, August 2017

This master thesis concludes my two-year-journey in TU Eindhoven, which is a remarkable experience.

I would like to first express my appreciation to my first supervisor, Tarkan Tan. My master study year would not be the same without direction from him. I would also like to thank Osman, my second supervisor. I thank you for your availability throughout this project although we separated by ocean. Your time, guidance and feedbacks are indispensable. Along with that, I would like to express my appreciation to Maxi for all of the advice and help. It surely has been a great experience to work with Tarkan, Osman and Maxi.

Lastly, but not the least, I would like to thank The Almighty and to the Indonesia Endowment Fund for Education (LPDP) for making my master program possible. My gratitude also goes to my parents, Titin Istrowati and Sukismo, my siblings, Mas Papang and Mbak Lia for their never- ending love and support; and to my best friends, Rianda and Fara for always being there; also for my friends in Eindhoven.

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Management Summary

This project is developed to give insight for a distribution company that commits to reduce its transportation emission due to demand distribution activity by shifting its delivery vehicles from diesel to electric trucks. This research focuses on economic analysis to plan the vehicle replacement while considering the needs for the company to build its charging infrastructure, which is battery switching stations (BSS). Battery switching station is chosen for this project as it allows the electric truck to have the shortest waiting time or idle time to get a fully charged battery compared to other charging infrastructure. This project aims to help the company in deciding when and how many electric vehicles should be bought and battery switching stations should be built.

One of the advantages of battery switching station system is its flexibility to decouple the life cycle of the electric vehicle and its battery. Therefore, for this project, the electric truck can be managed separately with its battery. Based on this, in addition to the decision of the schedule of replacing diesel with electric trucks and building battery switching stations (BSS), the company should also come up with the decision regarding when and how many batteries should be owned.

A linear programming mathematical model is built to generate a transformation schedule strategy from diesel to electric trucks for the logistics company that needs to deliver demand inside the Netherlands. The objective function of this model is to minimize total cost of ownership. The cost components of this model are purchasing cost of vehicle, salvage value of vehicle, maintenance and energy cost of vehicle, emission cost, battery switching station investment cost, battery switching station maintenance cost, purchasing cost of battery, salvage value of battery, maintenance cost of battery and waiting cost for electric truck to get a fully charged battery at BSS. The mathematical model is used to decide the electric adoption plan for 16 years of the planning horizon.

Several scenarios are developed to be inputted in the model and to give insights for the factors that are important to electric truck adopton in the company that also needs to build its battery switchign station. In addition to this insight regarding the factors that affect electric vehicle adoption, other scenarios are build to find out the emission reduction strategy that the company can implement.

For this thesis project, there are four types of emission reduction target strategies investigated;

those are emission reduction target described each year, total emission reduction at the end of the planning horizon, progressive emission reduction target based on the past emission generated and the emission reduction target at the end year of the planning horizon. The fourth strategy is similar to the emission reduction target used in the Netherlands. The emission reduction target can be decribed both by explaining the emission reduction percentage (%) want to be achieved or by describing the maximum allowable emission generated in the system (kg).

Both of the ways to dictate the emission reduction target have no difference in the electric truck adoption. Nevertheless, the four strategies of emission reduction target give different electric truck adoption pattern and total emission reduction percentage throughout planning horizon. Electric truck adoption pattern is described by the percentage of an electric truck in the company over all of the vehicles that company own over the planning horizon period and emission reduction percentage is calculated based on the real emission generated divided by the emission generated if the company only use diesel trucks to deliver demand.

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The first and third emission reduction target strategy dictated the system to have electric truck each year through control to emission generated each year. The second and fourth strategies give more flexibility to the system to decide when and how many electric trucks are owned. These two strategies also tend to adopt electric vehicle at the latter years and might adopt many EV at the end of the planning horizon.

The first strategy can provide the most emission reduction, but it requires much cost, while the fourth strategy generates the least emission reduction but requires the least total cost. For the company that only focuses on the cost consideration, the fourth strategy might be the most attractive strategy. However, the government might try to consider the first, second, and the third strategies since those strategies give higher total emission reduction throughout the planning horizon compared to the fourth strategy. Nevertheless, it is worth noting that the first, second, and third emission reduction target strategies require the company to commit to emission reduction plan from government since it require much more money compare to the fourth strategy.

Based on the total cost and total emission reduction percentage ratio, the second strategy gives the highest ration. Because the EV’s technology is still growing, the company might think that the second strategy that gives more flexibility to decide EV’s adoption and has a tendency to adopt at a later stage is more interesting.

Based on the experiment results to find out the factors that affect EV adoption in the company are oil price, weight demand, emission reduction targets, the limited driving distance range of electric truck, EV’s emission, and battery price. The increase in oil price, emission reduction target, and the limited distance range of electric truck will increase the EV’s adoption. However, the reduction of emission generated by EV due to the increase of renewable energy resource to generate electricity or due to the improve in EV’s technology can reduce electric vehicle adoption for this case. Because when the company has fulfilled its emission reduction target, that can succeed by less electric trucks, the company will not try to adopt more EV since more EV will lead to the need for building more BSS. However, it is worth noting that the reduction of the electric truck due to the reduction of emission generated by EV is only based on the reduction emission target and economic considerations. The company might decide to adopt more electric truck when the electric truck can generate less emission due to other considerations.

It is also concluded that the average demand weight can affect the electric truck adoption. The higher average demand weight, the more sensitive the system to the adoption of the electric truck due to the increase in oil price, emission tax price and limited distance range.

The reduction of battery price can also increase the EV adoption effectively, but only when the company does not need to build and manage its BSS. When the company needs to provide its BSS, the battery price reduction until it reaches $100/kWh can not overcome the high investment and managerial of BSS costs.

From the result, it can also be concluded that electric vehicle is interesting to be adopted in the company. Nevertheless, its limited distance range and the needs of the company to provide its charging infrastructure eliminates its adoption potential.

Based on this result, it can be concluded that to improve EV adoption as a company vehicle, the improvement in battery technology and support from government to build EV’s charging infrastructure and the subsidy support to reduce battery price are important. Nevertheless, these plans might give too much burden for the government since the government needs to provide

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much money. Else, the logistics companies might try to collaborate to build BSS together to reduce the government burden.

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Table of Contents

Abstract ... i

Acknowledgment ... ii

Management Summary ... iii

1. Introduction ... 1

1.1. Problem statement ... 1

1.2. Case description ... 3

1.3. Literature review ... 3

1.3.1. Electric Vehicle ... 3

1.3.2. Battery Switching Station As Charging Infrastructure for Electric Vehicle ... 6

1.3.3. Vehicle Replacement Model ... 8

1.4. Research questions ... 9

1.5. Methodology ... 10

2. Assumption and Explanation of The System ... 11

2.1. Analysis of The Possible Numbers of Battery Switching Station (BSS)... 11

2.1.1. Transportation Operation Assumptions and Frequency to Visit BSS (𝒇𝒇𝒇𝒇) ... 12

2.1.2. Possible Numbers of BSS ... 14

2.2. Battery Switching Station System ... 15

2.2.1. Waiting Time ... 15

2.2.2. Minimum Numbers of Battery in The Inventory of BSS ... 19

2.3. Minimum Numbers of Battery in The System ... 19

2.4. Annualized Investment Cost for Battery Switching Station ... 19

2.5. Salvage Value and Maintenance Cost for Vehicle ... 20

2.6. Potential Annual Distance Travelled ... 20

3. Mathematical Modelling ... 21

3.1. Mathematical Model ... 21

3.2. Linearization Process for Non-Linearity in The Model ... 27

3.2.1. Linearization of Minimum Numbers of Battery Needed in The System ... 27

3.2.2. Linearization of Waiting Time ... 27

3.2.3. Validation of Linearization Approximation ... 28

4. Data and Scenarios ... 29

4.1. Data ... 29

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4.1. Scenarios ... 31

5. Result And Analysis ... 32

5.1. Electric Vehicle Adoption and Emission Reduction Strategy ... 32

5.1. Factors That Affect EV Adoption in High and Low Daily Demand ... 38

6. Conclusion and Recommendations ... 42

6.1. Conclusions and Discussions ... 43

6.2. Research Contribution ... 45

6.3. Research Limitations and Future Research Recommendation ... 46

References ... 47

Appendix ... 51

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Tables

Table 1. Altenative Possible Numbers of BSS in The Netherlands ... 15

Table 2. Average Percentage Error of Linearization Approximation ... 28

Table 3. Input Data for The Project ... 29

Table 4. Emission Reduction Target Stratecy ... 31

Table 5. Percentage of Emission Reduction Target ... 32

Table 6. Ration of Total Cost and Total Emission Reduction Percentage ... 37

Table 7. Salvage Value of Diesel and Electric Vehicle ... 43

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Figures

Figure 1. Global Market Shares of BEVs and PHEVs (EVVolumes, 2017) ... 2

Figure 2. The BEV’s Battery Price, Goals, and Estimations (Based on the data from DOE (2016), GM (2017), HybridCARS (2017), Electrek (2017), and Cleantechnica (2017) ... 6

Figure 3. A Switching Battery Station System (Avci, Girotra, and Netessine, 2015) ... 7

Figure 4. Two-Level Model ... 11

Figure 5. Battery Swapping Station with Ample System ... 16

Figure 6. Scenario Plan ... 31

Figure 7. Scenario Plan to Investigate Factors That Affect EV Adoption ... 32

Figure 8. Percentage of EV in The System and Yearly Emission Reduction Target Based on Percentage and Numerical Strategy ... 33

Figure 9. Percentage of EV in The System and Total Emission Reduction Target Based on Percentage and Numerical Strategy ... 33

Figure 10. Percentage of EV in The System and Progressive Emission Reduction Target Based on Percentage and Numerical Strategy ... 34

Figure 11. Percentage of EV in The System and The Emission Reduction Target at The End Year of Planning Horizon ... 34

Figure 12. Percentage of EV in The System and Emission Reduction Target Strategies ... 35

Figure 13. Emission Reduction Each Year and Total Emission Reduction throughout Planning Horizon ... 36

Figure 14. Total Cost and Total Emission Reduction throughout Planning Horizon ... 36

Figure 15. Percentage of Cost Component ... 37

Figure 16. Relation between Percentages of EV Adoption with Oil Price ... 38

Figure 17. Relation between Percentages of EV Adoption with Emission Tax Price ... 39

Figure 18.Relation between Percentage of EV Adoption with Limited Range Distance ... 40

Figure 19. EV Adoption and BSS Investment ... 40

Figure 20. Relation between Percentage of EV Adoption with EV's Emission ... 41

Figure 21. Battery Price Reduction and EV Adoption ... 42

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1. Introduction

This section starts with the description of the problem statement and the aim of this research. It is then followed by the description of the case that is the interest of this project. The literature study on intermodal transport and environmental sustainability in transports are then discussed, which leads to the identification of research gaps and the associated research questions.

Following that, this chapter is concluded by the description of the methodology used in this research.

1.1.

Problem statement

The issues related to the deplete on of fossil fuel and increased greenhouse gasses (GHG) emissions have become a worldwide concern. To address emissions problems, The International Energy Agency set a goal to reduce CO2 emissions by 2050 to half the emissions of 2005 (IEA, 2011). As the continuation of this goal, in 2015, the 21st Conference of Parties of IEA (COP21) in Paris resulted in another goal to limit the increase of the global average temperature bellow 2°C during a century (UNFCCC, 2017).

To achieve these goals, the transportation sector needs to reduce emission because transportation sector alone generates 25% of total worldwide GHG emissions (IEA, 2012). If the current trends do not change, the transportation sector's energy demand and emissions are predicted to keep increasing and are expected to double by 2050 (IEA, 2012). One way to achieve such a reduction of emissions in the transportation sector is through the use of electric vehicles (EVs) substitution of internal combustion engine (ICE) vehicles. This idea is also relevant for the deplete on of fossil fuel problem, given that the energy demand of transportation is otherwise predicted to be doubled by 2050 (IEA, 2012).

Based on this idea, IEA set an objective in the Paris Declaration of Electro-Mobility and Climate Change and Call to Action to sell more than 100 million electric vehicles and 400 million 2-and-3- wheelers electric by 2030 (IEA, 2016). Furthermore, IEA also made BLUE Maps scenario as a strategy roadmap for increasing the adoption of Battery Electric Vehicles (BEVs) and Plug-In Electric Vehicles (PHEVs). BLUE Map Scenario emphasizes that three aspects need to be considered to increase the adoption of Electric Vehicles. Those aspects are R&D in battery technology, infrastructure, and government policy in term of tax and incentives.

These actions have made an electric vehicle to receive much interest in automotive market and research (Pelletier, Jabali, and Laportie, 2016). This interest and effort successed to increase the adoption of EVs that lead to increase the numbers of electric vehicle on the road every year, which can be seen in Figure 5. Nevertheless, the number of electric vehicles on the road, which consists of Battery Electric Vehicles (BEVs), Plug-In Hybrid Electric Vehicles (PHEVs), and Fuel Cell Electric Vehicles (FCEVs), were only 1.26 million units in 2015 (IEA, 2016). These numbers were very far from a 100-million-target of electric cars on the road by 2030. Therefore, based on this condition, the current pace will not ensure the success of IEA goal.

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Figure 1. Global Market Shares of BEVs and PHEVs (EVVolumes, 2017)

One way to increase EV adoption is by encouraging companies or organization to use EVs as commercial vehicles. This is because most of the electric vehicle sold are private cars, and the adoption of an electric vehicle for heavy and medium duty vehicle is still lower compared to light duty vehicle, except for electric bus (IEA, 2016). This is because electric vans and trucks as commercial distribution vehicles are still not widely accepted (Pelletier, Jabali, and Laportie, 2016). Furthermore, logically, organizations or companies can be considered as good candidates for adopting EVs because organizations use transportation in high frequency. This means, if organizations are willing to adopt EVs, there is also a good chance to reduce high emissions and fossil fuel demand.

The lack of acceptance of electric vehicle as a company’s commercial vehicle is due to several factors, and one of these factors is the “range anxiety” as EVs has limited energy storage. To address this issue, companies that use EVs as distribution vehicles need to prepare the infrastructure or stations to recharge EVs’ battery.

There are three types of recharging stations; those are standard charging stations, rapid charging stations, and battery switching stations. At the standard charging station, BEVs need to be charged for six until eight hours to have a fully charged battery while at the rapid charging station, it needs 20 until 30 minutes to be charged, and at a switching station, it only needs three minutes to get the fully charged battery (Liu, 2012).

Battery switching station (BSS) allows electric vehicle does not need to be idle while charging process happens and reduce waiting time for the electric vehicle to get a fully charged battery.

This BSS’s characteristic makes the battery switching station seems to be the most suitable charging system for commercial vehicles since it needs to get fully charged battery fast while doing the delivery.

A company that wants to adopt electric vehicle needs to plan well since the investment cost to adopt electric vehicle is expensive, especially, if the company needs to provide the charging infrastructure as there are not many charging infrastructure available right now. This investment problem becomes even more challenging when we consider that the electric truck is a new development in automotive industry. The new product development often has several issues related to rapid technological development, which affects long term planning decision. This is

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because the rapid development can change several important characteristics, such as distance range, and vehicle purchasing cost, in unpredictable time.

Based on the high investment cost and the rapid technology development, changing all of the vehicles to the electric truck at the same time seems not to be a reasonable decision. Therefore, there is a need for a distribution company to plan its electric truck adoption strategy that includes the decisions of when and how many electric trucks should be purchased by the company while considering the charging infrastructure investment. Based on this motivation, this thesis project aims at getting insights regarding:

1. The time and capacity-wise planning of vehicle replacement decision of diesel trucks by electric trucks while considering the investment of battery switching stations

2. Finding out the factors that affect the adoption of electric vehicle in the company 1.2.

Case description

The case study analysis is developed to fulfill the aims of this project. The analysis of this case study is limited for a distribution company that has delivery route inside the Netherlands. Due to limited information and source, several assumptions regarding the company’s information are developed. Those assumptions are:

- The company is a logistic company type that delivers variety demand type. For this project, weight of demand (kg) is the only thing that is considered without taking into account the demand type

- The company has the interest to reduce its transportation emission by substituting its diesel trucks with electric trucks that only have power source from electric grid

- The company uses medium duty vehicle type to deliver the demand

- Company has a policy to use all of the vehicles owned to deliver demand everyday

More detailed assumptions regarding transportation operation activity, such as the daily transportation activity hours, and frequency to visit BSS are explained in chapter 2.

Because this research is a research desk type, which means the research done not in the company, the data needed for this research is found out through public information, such as journal, newspaper, report, and news. This data is presented in chapter 4.

This research focuses on economics analysis to plan the vehicle replacement from diesel truck to electric truck as a delivery vehicle while considering the needs for the company to build its charging infrastructure, which is battery switching stations. The company should come up with the decision when and how many electric vehicles should be bought, when and how many electric vehicle’s batteries should be purchased, and battery switching stations should be built.

1.3.

Literature review

This section has three parts. In the first part, the literature regarding electric vehicle topic such as its technology development. After that, the second part discusses the battery switching station literature. Following that, the literature about vehicle replacement takes place.

1.3.1. Electric Vehicle

Electric vehicles are not new. They exist since the 1900s, nearly 40% of vehicles sold in 1900 were electric (Hidrue, Parsons, Kempton, & Gardner, 2011). However, they lost the market to ICE vehicles in the decades following. EVs started to gain attention again since an oil crisis in the

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1970s, which made people consider alternative vehicles that do not use fossil fuel as its power.

At around that period, several countries, such as Japan and US, identified EVs as a promising solution, and have started to support the development of EV.

However, this did not translate into adoption of EV in the 1970s because of several technology barriers from the electric vehicles, such as a high price and limited driving distance because of battery capacity. Moreover, during the 1970s until 1980s, people considered cutting oil dependency was not urgent (Ahmad, 2006). Following years, as the concern for reducing oil dependency and emissions became bigger and the electric vehicle’s technology has advanced, the automotive manufacturers started to see the prospect of electric vehicle market and decided mass produce EV. The first mass-production of the hybrid electric vehicle, Toyota Prius, launched in 1997 (Chan, 2007).

Now days, there are four types of electric vehicles; those are Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Fuel Cell Electric Vehicle (FCEV) and Plug-in Hybrid Electric Vehicle (PHEV) as the fourth type. Battery Electric Vehicle (BEV) is a vehicle that gets energy for mechanical propulsion from a rechargeable electric power storage device, which is a battery (EC, 2007). This device gets energy from external energy sources, which is the electric grid; therefore, BEV is considered in the plug-in vehicle cluster. To have a fully charged battery, a BEV needs to be charged for six until eight hours with standard charging (Burke, 2007). There are several advantages of BEV compare to internal combustion engine (ICE) vehicle as general; those advantages are the ability of a BEV to generate less emission, requires less operational cost and maintenance cost (MITElectricVehicleTeam, 2017; GreenOptions, 2017; Lee, Thomas, and Brown, 2013).

Hybrid-Electric Vehicles (HEVs) combine ICE vehicle's operation system and electric motor operation system. An ICE vehicle is powered by gasoline, and the electric motor is powered by a storage battery, which is charged from the regenerative braking system. This operation system implies that, in contrast with BEVs, HEVs do not need to get energy from the electric grid or external electric energy source.

HEVs are considered to be to be less clean compared to BEVs since HEVs are still very depend on gasoline. To reduce oil dependence on HEVs, PHEVs were developed. PHEV has the main system of HEV, but PHEV can recharge its battery from an external power source, such as electric grid (Weiss, et al., 2012). This condition implies that PHEV can get energy from both gasoline and the electric grid. Therefore similar with BEV, PHEV is also considered in the plug-in vehicles (PEV) cluster.

FCEV uses hydrogen as a power source. FCEV is considered as zero pollutant technology because it does not generate emissions but water as a result of the isothermal reaction of hydrogen (Chan, 2007). Therefore, FCEV is considered as one of the good solutions for the long- term vehicle and is predicted to be the future interest of the European research in the horizon of 2020-2040 (Mierlo, Maggetto, and Lataire,2006). Nevertheless, FCEV is less accepted compared to BEV and HEV right now because the technology of FCEV is still less mature and much more expensive compared to those of BEV and HEV. For a more in -depth explanation of each electric vehicle types, the reader is recommended to explore the literature study by Sari (2016).

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1.3.1.4. Factor Affecting Electric Vehicle Adoption As A Company Vehicle

Some studies have been conducted for analysing EVs adoption by a company as a commercial vehicle. Sierzchula et al. (2014) investigated the factor that makes company willing to adopt EVs and expand this adoption further. The study was done in six public and eight private organizations from the Netherlands and United States that adopted electric vehicles. Based on the analysis, it seems that the government policy in term of subsidies and tax is an important factor for a company to adopt EVs as EVs has high capital costs. Moreover, the government also needs to educate companies and people about EVs, as there is a lack of information and confusion regarding the technology of EVs, EV market, and EV safety.

The importance of government incentive to attract the companies to adopt EVs is also mentioned by Pelletier, Jabali, and Laportie (2016). In this study, the authors explain that Electric vans and trucks for commercial distribution are still not widely accepted due to high capital cost, driving range, payload, reliability, availability, and a high cost for EV's battery that was considered to only has a short lifetime. Nevertheless, the authors argue that if the government gives enough incentives for the electric truck, the electric truck can be an attractive option for companies’

vehicle. Furthermore, another factor that needs to be considered for using electric truck is utilization. Electric truck needs to be operated in high utilization to make it profitable and can overcome high capital cost (Brian & Miguel, 2013).

Based on the studies about the factors that affect EVs’ adoption, there is a conflict between the goals of customers and the goals of governments. Although one of the main reasons EVs have gained interest from the government is because of the urgency to reduce emissions, this environmental concern is, for firms, less important compared to other factors, such as financial consideration. Hidrue et al. (2011) mention that people are more attracted to fuel cost saving opportunity compare to the reducing emissions opportunity. In addition to this finding, Rowe et al.

(2012) mention that people tend to prioritize distance range compare to the need to reduce emissions. Therefore, if EVs can not give more flexibility for distance range, it seems hard to make EVs as a substitution for internal combustion engine (ICE) vehicles.

Based on those factors that affect EVs adoption, two factors are highly related to battery technology; which are driving range and purchase price. The reason of a high upfront cost for EVs is due to the battery cost. The battery cost is calculated based on the price per kWh. To reduce the electric vehicle cost, the battery technology needs to improve. Nowadays, EVs use lithium-ion (Li-ion) battery because it has rapid technology development and considered as a good choice for EV's battery. IEA (2011) stated that among other existed battery's technology until now, lithium-ion batteries offer the best option when optimizing both energy and power density of the battery.

As there is an increased interest in electric vehicle research in the past decade, there has been an improvement in the battery technology of electric vehicle that increases the limited driving distance of electric vehicles. The BEV's range used to be only around 100 miles, but in early 2017, Tesla, a car manufacturer, has introduced a new battery option with a distance range of 335 miles, which makes Tesla has the longest distance range battery for now. The current technology of battery for an electric vehicle has a distance range from around 100 until 300 miles, for example, Chevy Bolt has 238 miles of distance range, Ford Focus Electric has 115 miles of distance range, and Nissan Leaf has 107 miles of distance range (Fortune, 2017). While for the electric truck, the limited driving distance for electric truck ranges from 100 km to 250 km (Emoss.nl, 2017).

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Figure 2. The BEV’s Battery Price, Goals, and Estimations (Based on the data from DOE (2016), GM (2017), HybridCARS (2017), Electrek (2017), and Cleantechnica (2017)

The improvement in battery technology leads to the battery price reduction. Figure 2 shows that the goals to reduce battery cost for BEVs have been set by many automotive manufacturers and governments. The US government announced a goal to reduce battery cost from $500/kWh to

$125/kWh (DOE, 2017). Automotive manufacturers, such as General Motor (GM) and Tesla, also set its goals to reduce BEV’s battery cost. GM, which had succeeded to reduce battery cost until it reached $145/kWh in 2015, declared its target to make its battery cost reach $100/kWh by 2022 (GM, 2017). Similar to GM, Tesla also sets a goal to reduce its battery cost until it reaches $ 100/kWh by 2020 (HybridCARS, 2017).

Several projections for battery cost of BEVs have been made. IEA (2012) estimates the battery cost for BEVs to reach $325/kWh or less by 2020, while McKinseyandCo (2017) has a more optimistic projection of battery cost based on the analysis of data from EU stakeholders.

McKinseyandCo (2017) predicted that battery cost would be $236 /kWh in 2020.

Figure 2 also indicates that the average battery price of BEV shows significant reduction since 2010 until 2015 (Electrek, 2017 and Cleantechnica, 2017). Several estimations seem to have pessimistic prediction compared to this reduction trends. Nevertheless, based on this trends, the goal to reach battery cost until battery cost reaches $100/kWh by early 2020 seems to be not easy, except for the leader in automotive manufacturer, such as General Motor.

1.3.2. Battery Switching Station As Charging Infrastructure for Electric Vehicle

Recharging or charging station is an important infrastructure to support EVs. There are three types of recharging stations; those are a normal or standard charging station, rapid charging station, and battery switching station. At the standard charging station, BEVs need to be charged for six to eight hours to have a fully charged battery while at the rapid charging station, it needs 20 to 30 minutes to be charged, and at switching station, it only needs three minutes to get the fully charged battery (Liu, 2012).

There is a distinct difference between switching battery station with other two station types. In the switching station, the BEVs can get a fully charged battery within three minutes since BEVs only come to switch its used battery with the fully charged battery. This fully charged battery has been

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charged before BEVs come, and the driver only needs to pay for miles driven based on the used battery.

Switching battery station has same advantages. It can help to decouple the battery and EV which means it can reduce the upfront cost because the customer does not need to buy the battery but pay for miles used (Mak, Rong, & Shen, 2013). The battery is still be owned by the company, therefore, in other words, the company is leasing the battery to the customer. By this decoupling battery and EV that have a different life cycle, it will be easier to take advantage of the future improvement in battery technology since the customer only needs to change its battery without the need to change EV (Mak, Rong, & Shen, 2013).

Based on these advantages, several organizations, such as Better Place and Tesla, want to adopt battery switching station. Better Place Company had tried to build and made a pilot project in Denmark, Australia, and Israel before it was bankrupt due to poor financial planning (Avci, Girotra,

& Netessine, 2015). In addition to these companies, China's government also has chosen a switching battery station as its core infrastructure option to support EV adoption (Mak, Rong, &

Shen, 2013).

The interest to battery swithcing station also occurs in the reasearch area. Mak, Rong, and Shen (2013) and Avci, Girotra, and Netessine (2015) consider battery swithcing station in their studies.

Mak, Rong, and Shen (2013) develop two optimization models that aim to help the planning process for development of battery-swapping infrastructure in a freeway in the US. For this study, the authors focus on the financial analysis of investment in switching station. The first model is a cost-concerned model that assumes the service provider is mainly concerned about cost and minimizes an expected building and operating costs and the second model is a goal-driven model that assumes the service provider is concerned about meeting certain return-on-investment (ROI) target and attempts to maximize an estimate of the probability of achieving ROI target.

Avci, Girotra, and Netessine (2015) are inspired by the switching station model of Mak, Rong, and Shen (2013). However, instead of focusing on financial analysis, Avci, Girotra, and Netessine (2015) try to observe the impact of switching station to find out the effect of switching battery station to the adoption of EVs.

Figure 3. A Switching Battery Station System (Avci, Girotra, and Netessine, 2015)

The switching battery station system is depicted in Figure 9. Avci, Girotra, and Netessine (2015) adopted METRIC system (Multi-Echelon Technique for Recoverable Item Control) by assuming the used battery as a broken part, charging process as repair process and charged the battery as the stock of spare part. Based on METRIC theory, Avci, Girotra, and Netessine (2015) also adopt M/G/ꚙ queue system for charging system by assuming the servers or chargers are an ample system of has infinite servers.

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Both of of the studies that were done by switchign station model that Avci, Girotra, and Netessine (2015) and Mak, Rong, and Shen (2013) assume that battery switching station has the charging process as a first-in-first-out system. The first-in-first-out system for battery switching station might not be the optimal policy because, logically, the first out battery should be the battery with the highest of state of charge (SOC). Nevertheless, the first-in-fist-out policy has been proven by Mak, Rong, and Shen (2013) to have the similar result of the highest-SOC-first-out system; therefore first-in-first-out policy can be used as policy system for charging the battery. In addition to this, similar with that Avci, Girotra, and Netessine (2015), Mak, Rong, and Shen (2013) also analyze battery switching station by assuming that the station has an ample system or infinite servers or chargers; this means that battery switching station has M/G/∞ queue system. Other studies that have been conducted to analyze battery switching station and other charger types can be read in the Sari (2016).

It is also worth noting that all of the analysis for battery switching station that has been done by Avci, Girotra, and Netessine (2015) and Mak, Rong, and Shen (2013) only for single battery switching station system. This single battery switching station system will affect the numbers of battery in the inventory. Both of the studies calculate the battery inventory based on the centralized inventory system logic. For Avci, Girotra, and Netessine (2015), it is clear in their study that they used the single station in their system as they mentioned it clearly. Nevertheless in Mak, Rong, and Shen (2013) study it is quite umbigous whether they used single or centralized station system or not in their study since they tried to analyzed a system to build many battery switching stations but they used single or centralized inventory formula to calculate battery inventory level.

On the contrary with the studies that have been done by Avci, Girotra, and Netessine (2015) and Mak, Rong, and Shen (2013), the decentralized battery inventory system for many battery switching stations is used in this thesis project.

Although several studies have been done in battery switching station, all of the studies focus on the analysis of battery switching station as charging infrastructure for the private electric vehicle.

There has not been an analysis for battery switching station as charging infrastructure option for a commercial vehicle, which is discussed in this thesis project.

1.3.3. Vehicle Replacement Model

Based on Hartman (2000), vehicle replacement model is a mathematical model developed to define the replacement schedule for a fleet of vehicles. He also mentions that this model aims to find out the optimal replacement age and capacity that minimizes purchasing, maintenance, operation and salvage costs over a planning period or time horizon. The calculation of vehicle replacement model optimization usually rotates around the trade off between those cost factors since as vehicles get older, their operating and maintenance costs increase while their salvage value decreases. Based on this, it might be more cost wise if the old vehicle is replaced by the new vehicle when its operation and maintenance cost reaches certain points that high enough to overcome the purchasing cost while also considering the salvage value of the vehicle. In this model, the old vehicle that should usually be replaced called as a defender and the new vehicle that should be a replacement usually called as a challenger.

According to Feng and Figgliozzi (2012), there are two category types of vehicle replacement models, which are research oriented and practice oriented models. Research oriented model usually tries to generate the optimal replacement strategy to find out the least cost or maximized

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profit. While in practice oriented model, replacement strategy is typically generated based on other criteria or performance measures. The practice oriented model usually are heuristic models so that this model can produce a suboptimal solution.

In the research oriented model, there are two types of vehicles replacement model; serial replacement and parallel replacement models (Feng and Figgliozzi, 2012). The serial replacement model is a vehicle replacement model for assets that work together in serial system or in order which has high dependency between each vehicles to fulfill the demands, while parallel replacement model is a vehicle replacement model for vehicles that work in parallel without has dependency between each vehicle to fulfill the demands (Hartman, 2000).

Feng and Figgliozzi (2012) analyze the vehicle replacement model considering electric trucks as commercial vehicles to replace diesel trucks. In this study, the authors try to minimize total cost that consists of purchasing, salvage value, operation, and maintenance costs while taking into consideration of vehicle age and types. The salvage value and maintenance costs of the vehicle are calculated as a function of vehicle’s age. In this study, the electric trucks play a role as a challenger and the diesel trucks play a role as a defender. The decision variables of this model are the number of age-i, type-k trucks used in year j (𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖), the number of age-i, type-k truck salvaged in the year j (𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖), and the number of type-k truck purchased in the beginning of year j (𝑃𝑃𝑖𝑖𝑖𝑖).

The vehicle replacement model in this thesis project is an extension model based on the model that Feng and Figliozzi (2012). The model in this project is developed by considering more realistic setting that is not analysed by Feng and Figliozzi (2012). Feng and Figliozzi (2012) does not take into consideration of electric truck infrastructure factors, such as the availability of charging infrastructure and charging time or the waiting time for the electric truck to get a fully charged battery, which are important to support the operational of electric truck. It seems that Feng and Figliozzi (2012) assumed that the electric truck does not need to be charged while doing delivery.

Nevertheless in real life and based on the electric truck distance range, that range from 100 to 300 miles, there is still a possibility for electric truck needs to be charged in the middle of the delivery activity.

1.4.

Research questions

Based on the literature study in the previous section, three gaps can be found, i.e.:

1. No research has been conducted for a company that want to transform its diesel trucks to electric truck as company’s delivery vehicle while considering the needs to invest in its charging infrastructure to support electric vehicle transportation activity

2. No research has been conducted to investigate building battery switching station as a charging infrastructure for the company

3. No research investigates electric vehicle adoption under battery switching station system that allows the decouple of electric vehicle and its battery’s purchasing decision

As described in the first chapter, the purpose of this thesis project is to get the insight of vehicle replacement model to shift from diesel vehicle to electric truck vehicle as the delivery vehicle for the company. Therefore, based on this research gap and the purpose of this thesis project, several research questions can be used to address thesis project’s objective and research gaps:

1. Is it an attractive option for the company to shift from diesel to electric vehicle while investing in its battery switching station?

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2. How is adoption transformation strategy of the company to shift from diesel to electric truck while building its battery switching station?

2.1. When and how many electric vehicles to be bought and salvaged?

2.2. When and how many diesel trucks still need to be purchased and salvaged?

2.3. When and how many battery switching stations to be built?

2.4. When and how many batteries need to be bought and salvaged?

3. What are the factors that can affect electric truck adoption in the company while considering investing in its battery switching station?

By answering these research questions, the research gap can be fulfilled the purpose of this thesis project can be addressed. Therefore these research questions can play as a guide through this project.

1.5.

Methodology

For the company, the most important consideration in the business is based on economic analysis. Therefore, for analyzing the vehicle replacement plan while considering the battery switching station investment, the company needs to investigate the total cost of ownership as economic consideration.

The transformation strategy from diesel to electric trucks is chosen based on the least total cost of ownership. Based on this, the method to calculate and optimize total cost of ownership is needed for this project; thus, a mathematical model is developed. This model is developed based on vehicle replacement mathematical model of Feng and Figgliozzi (2012) and extended by adding more cost factors in the model.

The additional cost factors that are considered are the cost regarding battery switching station, electric truck’s battery, emission, and waiting time for the electric truck to get a fully charged battery at the BSS. Consequently, the model becomes more complicated and one-level linear mathematical model like the one that Feng and Figgliozzi (2012) use might become insufficient to deliver the optimal solution and might require long computational duration, especially when there is a non-linear formula to calculate cost factors. Therefore, to reduce computation time and get the optimal solution, a two-stage calculation of linear programming is used for this project. In other words, this model can be addressed as a bi-level linear programming.

The cost factor formulas to calculate numbers of battery in the system and waiting time costs for the electric truck to get a fully charged battery at the BSS have non-linear equations, which are linearized and used as the part of the main model. The calculation process is explained in Figure 4. The first stage is doing the optimization based on this linearized main model. After this optimization is done, the decision variables can be used to as input parameters to the model that is developed without linearizing cost factors. This calculation is done to find the real value of waiting time and minimum numbers of battery in the system. Therefore all of the decisions variables will have real value to calculate the total cost of ownership. If in the second-stage there is infeasibility in the calculation or the solution can not be obtained, the formulas of waiting time and minimum numbers of battery from the first-stage model should be updated until the solution is found.

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Figure 4. Two-Level Model

Analysis based on several scenarios are run through this mathematical model in AIMMS. These scenarios are generated to investigate what factors affect the adoption of the electric truck and battery swtiching station investment decision in the company. The investigated factors are the oil price, emission reduction target, emission tax, and electric vehicle improvements such as the change of and battery cost.

2. Assumption and Explanation of The System

This chapter contains the assumption and design of the system to build a mathematical model of this thesis, such that the research questions can be addressed. This chapter also explains the calculation of important components that will be a foundation or variables which are used later in the main mathematical model.

2.1. Analysis of The Possible Numbers of Battery Switching Station (BSS)

Based on what has been discussed in chapter 1 at case description, this thesis project scope is for a company that uses medium duty truck as a delivery vehicle to distribute demand in the Netherlands. As has been mentioned before, battery switching station is still considered as a new concept for charging infrastructure, and it has not been used widely. In Netherlands, there is no battery switching station that can be used as a recharging facility for an electric truck.

The idea for battery switching station is to copy the gasoline station idea, which makes the vehicle only need a short time to get new power or energy. Therefore, battery switching station that only needs three until ten minutes to make vehicle gets a fully charged battery is a good option as charging infrastructure for a commercial vehicle that considers time as an important factor in the operational level. Nevertheless, this advantages of battery switching station can not be achieved if the accessibility of station is poor, such as the electric truck needs to travel far from its original delivery route to access battery switching station. Therefore, battery switching station also needs to copy the easy accessibility of those of gasoline station by making it possible for an electric truck to travel not far from its original route to reach BSS.

Based on this, the numbers of BSS needs to be built high enough and needs to be spread in the Netherlands such that it will be easy for the electric truck to access it. To find out the numbers of

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the possible battery switching station should be built, the frequency of electric truck needs to visit BSS based on electric vehicle’s maximum range distance and maximum daily distance travelled should be analysed first. Following this, the number of possible BSS is calculated based on the allowable time to reach BSS.

2.1.1. Transportation Operation Assumptions and Frequency to Visit BSS (𝒇𝒇𝒇𝒇)

Since the working place is in the Netherlands, the working condition and regulation regarding truck and driver in the Netherlands should be investigated first as a base of building assumption and designing the mathematical model. Based on the interviews and correspondences with DHL and KLG of Netherlands, there are several characteristics of a delivery truck and driver’s working condition that are important to be noting:

1. In normal case, most truck will be driven by one driver in one day; therefore the truck will be operated in one shift for one day

2. Most of the distributions have time windows during daylight; this means that majorly trucks will be operated at the same time

3. The driving hours for the driver is based on the UE regulation, which states that daily driving hours should not exceed nine hours, and it can be extended to ten hours only twice a week. Furthermore, after working for working for six days, each driver should have weekly rest. Based on this, the approximation of daily working hours for drivers or daily operated hours of trucks can be calculated:

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜 =𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑚𝑚𝑡𝑡𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚 𝑤𝑤𝑡𝑡𝑤𝑤𝑖𝑖𝑖𝑖𝑤𝑤𝑤𝑤 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜 𝑖𝑖𝑤𝑤 𝑡𝑡𝑤𝑤𝑒𝑒 𝑤𝑤𝑒𝑒𝑒𝑒𝑖𝑖

𝑤𝑤𝑚𝑚𝑚𝑚𝑛𝑛𝑒𝑒𝑤𝑤 𝑡𝑡𝑜𝑜 𝑑𝑑𝑡𝑡𝑑𝑑𝑜𝑜 𝑖𝑖𝑤𝑤 𝑡𝑡𝑤𝑤𝑒𝑒 𝑤𝑤𝑒𝑒𝑒𝑒𝑖𝑖 (1)

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜 =9 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜 𝑚𝑚 4 𝑑𝑑𝑡𝑡𝑑𝑑𝑜𝑜+10 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜 𝑚𝑚 2 𝑑𝑑𝑡𝑡𝑑𝑑𝑜𝑜 6 𝑑𝑑𝑡𝑡𝑑𝑑𝑜𝑜

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜 =56 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜

6 𝑑𝑑𝑡𝑡𝑑𝑑𝑜𝑜 = 9.33 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜/𝑑𝑑𝐷𝐷𝐷𝐷

To know the frequency of electric truck needs to visit BSS in one day, the maximum daily distance can be travelled by truck and electric truck’s limited range should be found out. To analyse the maximum daily distance can be travelled by truck, the average speed of truck in Netherlands needs to be known. Based on the Netherlands regulation, the maximum speed truck in are:

-31mph or 50 km/hour in built up area -50 mph or 80km/hour outside built up area - 62 mph or 100km/hour at expressways

For this case, it is assumed that in Netherland, the average speed for delivery truck is same with the maximum speed of truck in the built up area, which is 31mph or 50 km/h. This assumption is made since this speed is in the range of delivery truck’s average speed based on Walkowich (2017). He states that the delivery truck’s speed is around 48-56 km/hours. Therefore, the maximum daily distance travelled by truck is:

𝑀𝑀𝐷𝐷𝑀𝑀𝐷𝐷𝑀𝑀𝑜𝑜𝑀𝑀 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 = 𝐷𝐷𝑎𝑎𝐷𝐷𝑤𝑤𝐷𝐷𝑤𝑤𝐷𝐷 𝑜𝑜𝑠𝑠𝐷𝐷𝐷𝐷𝑑𝑑 𝑀𝑀 𝑀𝑀𝐷𝐷𝑀𝑀𝐷𝐷𝑀𝑀𝑜𝑜𝑀𝑀 𝑑𝑑𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷𝑤𝑤𝑤𝑤 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜 (2) 𝑀𝑀𝐷𝐷𝑀𝑀𝐷𝐷𝑀𝑀𝑜𝑜𝑀𝑀 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 = 9.33 ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜 𝑀𝑀 50 𝑤𝑤𝑀𝑀/ℎ𝑤𝑤𝑜𝑜𝑤𝑤𝑜𝑜

𝑀𝑀𝐷𝐷𝑀𝑀𝐷𝐷𝑀𝑀𝑜𝑜𝑀𝑀 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 = 466.5 𝑤𝑤𝑀𝑀

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Based on the information from DHL and KLG and truck’s speed assumption, it is assumed that a logistics company for this thesis project has the transportation operation activity’s characteristics as:

1. The company operates all of its vehicles to deliver demand in the Netherlands every day for 260 working days in one year

2. The trucks operate for 9.33 hours per day and work at the same hours 3. The trucks are driven with the average speed of 50km/hour

4. Maximum daily distance travelled by each truck is 466.5 km

5. Since there is no information regarding the number daily demand distribution and demand location distribution, it is assumed that the company will have daily normal demand distribution and the demand is uniformly distributed in the Netherlands

These assumptions are used as a foundation to build the model.

To estimate the frequency of electric truck to visit BSS in one day, other information regarding the maximum driving distance of electric truck with a fully charged battery needs to be found. For this case, the maximum distance range for the electric truck is assumed to be 322 km. This assumption is made for the near future electric truck technology. For present technology, the normal medium electric delivery truck can travel around 161 km, such as an e-Navistar truck. The longer limited distance range can be reached by Emoss truck, which is 100-250 km (emoss.biz, 2017). Since there is the development of battery technology to increase the distance range of a fully charged battery truck, this distance range is predicted to increase. The value of maximum range distance for the electric truck is also based on the plan of Tesla to develop an electric truck that can reach 322-483 km (wired.com, 2017).

Based on the assumption of distance range of electric truck, being 322, and the maximum distance travelled of delivery truck in Netherlands, being 466.5 km, it can be concluded that if electric truck goes out from depot with fully charged battery, it still needs to visit battery switching station to get fully charged battery to reach its maximum distance. The frequency to visit BSS each day at year-j (𝑓𝑓𝑖𝑖) can be calculated as:

𝑓𝑓𝑖𝑖= �𝑡𝑡𝑎𝑎𝑒𝑒𝑤𝑤𝑡𝑡𝑤𝑤𝑒𝑒 𝑜𝑜𝑠𝑠𝑒𝑒𝑒𝑒𝑑𝑑 𝑚𝑚 𝑚𝑚𝑡𝑡𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚 𝑑𝑑𝑡𝑡𝑖𝑖𝑡𝑡𝑑𝑑 𝑤𝑤𝑡𝑡𝑤𝑤𝑖𝑖𝑖𝑖𝑤𝑤𝑤𝑤 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜

𝑅𝑅𝑗𝑗 � (3)

𝑓𝑓𝑖𝑖= �𝑀𝑀𝑡𝑡𝑚𝑚𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚 𝐷𝐷𝑡𝑡𝑖𝑖𝑡𝑡𝑑𝑑 𝐷𝐷𝑖𝑖𝑜𝑜𝑡𝑡𝑡𝑡𝑤𝑤𝐷𝐷𝑒𝑒

𝑅𝑅𝑗𝑗

𝑓𝑓𝑖𝑖= �50

𝑘𝑘𝑘𝑘

𝑚𝑚 9.33ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜

322 𝑖𝑖𝑚𝑚 � = ⌊1.4⌋ = 1

With 𝑓𝑓𝑖𝑖 as the frequency to visit BSS each day at year-j and 𝑅𝑅𝑖𝑖 as the limited distance range of electric truck at year-j. For this case, as the range distance is assumed to be 322 km from the starting year, the 𝑓𝑓𝑖𝑖 will be either one or zero when the range distance increase in the future years.

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Because there are no fixed delivery routes and it is assumed that the routes can always change, there is no location plan for BSS that will be placed exactly on the delivery route. Therefore there is a possibility that electric trucks need to go out from its original delivery route to visit BSS. It is assumed that the driver can go out from its original delivery route to reach BSS during a limited period and go back to the delivery route; therefore there will be additional distance need to travel by truck to recharge its power. However, it is worth noting that this additional distance is still included the maximum distance of e-truck can travel, which is 466.5 km because the time to go round trip to BSS is included in working hours. Based on this, there will be the allowable distance that truck can travel to get the full charged battery and go back to its original delivery route. This allowable distance can be found out from the allowable time to go round trip to BSS that is decided based on the company policy.

𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 𝐷𝐷𝑤𝑤 𝑤𝑤𝑤𝑤 𝑤𝑤𝑤𝑤𝑜𝑜𝑤𝑤𝑑𝑑 𝐷𝐷𝑤𝑤𝐷𝐷𝑠𝑠 𝐷𝐷𝑤𝑤 𝐵𝐵𝐵𝐵𝐵𝐵 =𝑡𝑡𝑎𝑎𝑒𝑒𝑤𝑤𝑡𝑡𝑤𝑤𝑒𝑒 𝑜𝑜𝑠𝑠𝑒𝑒𝑒𝑒𝑑𝑑 𝑚𝑚 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑤𝑤𝑡𝑡𝑛𝑛𝑡𝑡𝑒𝑒 𝑡𝑡𝑖𝑖𝑚𝑚𝑒𝑒 𝑡𝑡𝑡𝑡 𝑤𝑤𝑡𝑡 𝑤𝑤𝑡𝑡𝑚𝑚𝑤𝑤𝑑𝑑 𝑡𝑡𝑤𝑤𝑖𝑖𝑠𝑠 𝑡𝑡𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵

𝐹𝐹𝑤𝑤𝑒𝑒𝐹𝐹𝑚𝑚𝑒𝑒𝑤𝑤𝐷𝐷𝑑𝑑 𝑡𝑡𝑡𝑡 𝑎𝑎𝑖𝑖𝑜𝑜𝑖𝑖𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗 (4) 𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 𝐷𝐷𝑤𝑤 𝑤𝑤𝑤𝑤 𝑤𝑤𝑤𝑤𝑜𝑜𝑤𝑤𝑑𝑑 𝐷𝐷𝑤𝑤𝐷𝐷𝑠𝑠 𝐷𝐷𝑤𝑤 𝐵𝐵𝐵𝐵𝐵𝐵 =𝑡𝑡𝑎𝑎𝑒𝑒𝑤𝑤𝑡𝑡𝑤𝑤𝑒𝑒 𝑜𝑜𝑠𝑠𝑒𝑒𝑒𝑒𝑑𝑑 𝑚𝑚 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑤𝑤𝑡𝑡𝑛𝑛𝑡𝑡𝑒𝑒 𝑡𝑡𝑖𝑖𝑚𝑚𝑒𝑒 𝑡𝑡𝑡𝑡 𝑤𝑤𝑡𝑡 𝑤𝑤𝑡𝑡𝑚𝑚𝑤𝑤𝑑𝑑 𝑡𝑡𝑤𝑤𝑖𝑖𝑠𝑠 𝑡𝑡𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑠𝑠 𝑥𝑥 𝑘𝑘𝑎𝑎𝑥𝑥𝑚𝑚𝑘𝑘𝑚𝑚𝑘𝑘 𝑠𝑠𝑎𝑎𝑚𝑚𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑎𝑎𝑘𝑘𝑚𝑚𝑤𝑤𝑎𝑎 ℎ𝑤𝑤𝑚𝑚𝑎𝑎𝑠𝑠

𝑅𝑅𝑗𝑗

Since the delivery truck deliver demand for 9.33 hours per day, to make truck does not loose much time to get its fully charged battery, it is assumed that the allowable time to go round trip to BSS and back to truck’s original delivery route is 0.5 hours, which is only 5.2% of total working time. Based on this assumption, and the previous assumption about average speed for truck and limited distance range of electric truck, the allowable distance to go round trip to BSS can be calculated:

𝐷𝐷𝐷𝐷𝑜𝑜𝐷𝐷𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷 𝐷𝐷𝑤𝑤 𝑤𝑤𝑤𝑤 𝑤𝑤𝑤𝑤𝑜𝑜𝑤𝑤𝑑𝑑 𝐷𝐷𝑤𝑤𝐷𝐷𝑠𝑠 𝐷𝐷𝑤𝑤 𝐵𝐵𝐵𝐵𝐵𝐵 = 50 𝑖𝑖𝑚𝑚/ℎ 𝑚𝑚0.5 ℎ𝑡𝑡𝑚𝑚𝑤𝑤𝑜𝑜

1 = 25 𝑤𝑤𝑀𝑀 (5)

Because the round trip distance from original delivery route to reach BSS is 25 km, the one way trip to go BSS is 12.5 km. Based on this, to achieve its allowable time to go round trip from original delivery route to BSS, the company that wants to use the electric truck to deliver demand in the Netherlands needs to provide BSS each 12.5 km in the Netherlands. Following this logic, the possible numbers of BSS in the Netherlands can be predicted based on the following formula:

𝑃𝑃𝑤𝑤𝑜𝑜𝑜𝑜𝐷𝐷𝑃𝑃𝐷𝐷𝐷𝐷 𝑁𝑁𝑜𝑜𝑀𝑀𝑃𝑃𝐷𝐷𝑤𝑤𝑜𝑜 𝑤𝑤𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝑤𝑤 𝑁𝑁𝐷𝐷𝐷𝐷ℎ𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷𝑤𝑤𝑑𝑑𝑜𝑜

= 𝑁𝑁𝑒𝑒𝑡𝑡ℎ𝑒𝑒𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑑𝑑 𝑡𝑡𝑤𝑤𝑒𝑒𝑡𝑡 𝑜𝑜𝑠𝑠𝑡𝑡𝐷𝐷𝑒𝑒

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑠𝑠 𝑥𝑥 𝑎𝑎𝑑𝑑𝑑𝑑𝑤𝑤𝑤𝑤𝑎𝑎𝑎𝑎𝑑𝑑𝑎𝑎 𝑡𝑡𝑚𝑚𝑘𝑘𝑎𝑎 𝑡𝑡𝑤𝑤 𝑡𝑡𝑤𝑤 𝑎𝑎𝑤𝑤 𝑎𝑎𝑤𝑤𝑚𝑚𝑤𝑤𝑠𝑠 𝑡𝑡𝑎𝑎𝑚𝑚𝑠𝑠 𝑡𝑡𝑤𝑤 𝐵𝐵𝐵𝐵𝐵𝐵 2𝑥𝑥�𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑠𝑠 𝑥𝑥 𝑘𝑘𝑎𝑎𝑥𝑥𝑚𝑚𝑘𝑘𝑚𝑚𝑘𝑘 𝑠𝑠𝑎𝑎𝑚𝑚𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑎𝑎𝑘𝑘𝑚𝑚𝑤𝑤𝑎𝑎 ℎ𝑤𝑤𝑚𝑚𝑎𝑎𝑠𝑠𝑅𝑅𝑗𝑗

2

= 𝑁𝑁𝑒𝑒𝑡𝑡ℎ𝑒𝑒𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑑𝑑 𝑡𝑡𝑤𝑤𝑒𝑒𝑡𝑡 𝑜𝑜𝑠𝑠𝑡𝑡𝐷𝐷𝑒𝑒

𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑠𝑠𝑠𝑠𝑎𝑎𝑎𝑎𝑠𝑠 𝑥𝑥 𝑎𝑎𝑑𝑑𝑑𝑑𝑤𝑤𝑤𝑤𝑎𝑎𝑎𝑎𝑑𝑑𝑎𝑎 𝑡𝑡𝑚𝑚𝑘𝑘𝑎𝑎 𝑡𝑡𝑤𝑤 𝑡𝑡𝑤𝑤 𝑎𝑎𝑤𝑤 𝑎𝑎𝑤𝑤𝑚𝑚𝑤𝑤𝑠𝑠 𝑡𝑡𝑎𝑎𝑚𝑚𝑠𝑠 𝑡𝑡𝑤𝑤 𝐵𝐵𝐵𝐵𝐵𝐵 2 𝑥𝑥 𝐹𝐹𝑎𝑎𝑎𝑎𝐹𝐹𝑚𝑚𝑎𝑎𝑤𝑤𝐹𝐹𝑑𝑑 𝑡𝑡𝑤𝑤 𝑎𝑎𝑚𝑚𝑠𝑠𝑚𝑚𝑡𝑡 𝐵𝐵𝐵𝐵𝐵𝐵𝑗𝑗

2

= 𝑁𝑁𝑒𝑒𝑡𝑡ℎ𝑒𝑒𝑤𝑤𝑡𝑡𝑡𝑡𝑤𝑤𝑑𝑑 𝑡𝑡𝑤𝑤𝑒𝑒𝑡𝑡 𝑜𝑜𝑠𝑠𝑡𝑡𝐷𝐷𝑒𝑒

𝑜𝑜𝐹𝐹𝑚𝑚𝑡𝑡𝑤𝑤𝑒𝑒 𝑡𝑡𝑤𝑤𝑒𝑒𝑡𝑡 𝑜𝑜𝑠𝑠𝑡𝑡𝐷𝐷𝑒𝑒 𝑡𝑡𝑜𝑜 𝐵𝐵𝐵𝐵𝐵𝐵 𝑡𝑡𝑡𝑡𝐷𝐷𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑤𝑤 (6)

In this case, since the allowable time to go round trip from original delivery route to BSS is 0.5 hours. The possible numbers of BSS in the Netherlands are:

𝑃𝑃𝑤𝑤𝑜𝑜𝑜𝑜𝐷𝐷𝑃𝑃𝐷𝐷𝐷𝐷 𝑁𝑁𝑜𝑜𝑀𝑀𝑃𝑃𝐷𝐷𝑤𝑤𝑜𝑜 𝑤𝑤𝑓𝑓 𝐵𝐵𝐵𝐵𝐵𝐵 𝐷𝐷𝑤𝑤 𝑁𝑁𝐷𝐷𝐷𝐷ℎ𝐷𝐷𝑤𝑤𝐷𝐷𝐷𝐷𝑤𝑤𝑑𝑑𝑜𝑜 =12.5 𝑖𝑖𝑚𝑚 𝑚𝑚 12.5 𝑖𝑖𝑚𝑚41543 𝑖𝑖𝑚𝑚2 = 267 𝐵𝐵𝐵𝐵𝐵𝐵 (7)

(27)

15

Another possibility of distance between BSS and possible numbers of BSS based on the different allowable time to go round trip to BSS is shown in the table below. It is worth noting that this calculation number only hold for the needs of e-truck to visit BSS once. The distance between BSS decreases, and possible numbers of BSS increase if the frequency of going to BSS increases due to the limitation of battery distance range. For this case, the possible numbers of BSS in the Netherlands that will be used are 267 BSS since it is decided that the allowable time to go to round trip from original delivery route to BSs be 0.5 hours.

Table 1. Altenative Possible Numbers of BSS in The Netherlands allowable

time to go round trip(s) to

BSS(hours)

frequency to go to BSS

allowable time to go round trip to BSS(hours)

distance to go one round trip to BSS(km)

percentage distance to go round trip(s) to BSS from the total distance

maximum distance to deliver demand (km)

the distance between BSS (km)

the possible number of BSS

0.5 1 0.5 25 5.36% 441.67 12.5 267

1 1 1 50 10.71% 416.67 25 66

1.5 1 1.5 75 16.07% 391.67 37.5 30

2 1 2 100 21.43% 366.67 50 17

2.2. Battery Switching Station System

There three important components for BSS; swapping machine, chargers, and battery inventory.

Based on these important components, two critical performance measurements need to be considered for building BSS system; the number of batteries to be kept in the stations and waiting time for the electric vehicles to get a fully charged battery.

2.2.1. Waiting Time

Waiting time for the electric vehicle to get fully charged battery is an important issue to be considered. The electric truck should not wait too long to get a battery or else it will hinder the advantage of BSS that allows an electric vehicle to get fully charged battery fast. Moreover, for the delivery truck, the time schedule can be very strict. For understanding how to calculate waiting time, the BSS system should be analysed first.

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