Impact of Battery Size and Energy Cost on the Market Acceptance of Blended Plug-in Hybrid Electric Vehicles

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8th International Congress of Information and Communication Technology (ICICT-2018) 8th International Congress of Information and Communication Technology (ICICT-2018)

Impact of Battery Size and Energy Cost on the Market Acceptance Impact of Battery Size and Energy Cost on the Market Acceptance of Blended Plug-in Hybrid Electric Vehicles of Blended Plug-in Hybrid Electric Vehicles Yan Xiaaa, Jie Yangbb, Fan Wangcc, Qixiu Chenga* 一 Yan Xia , Jie Yang , Fan Wang , Qixiu Chenga* 一

Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center China of Modern Urban Traffic Technologies, School of b Development Research Institute of Transportation Governed by Law, Southeast Transportation, Southeast University, Nanjing, China University, Nanjing, China c b Engineer School of Highway, Chang’ an University Development Research InstituteM.S., of Transportation Governed by Law, SoutheastChina University, Nanjing, China c Engineer M.S., School of Highway, Chang’ an University China

a a

Abstract Abstract The blended plug-in hybrid electric vehicles (PHEV) have sprung up in the private car market. However, many consumers are Theclear blended plug-in hybrid electric sprungand up the in the private carand market. However, need manytoconsumers are not about whether PHEVs will vehicles save cost(PHEV) for theirhave ownership government the automakers take effective not clear about whether PHEVs save cost for theirof ownership the government and the automakers need to take effective measures to reduce the total costwill of ownership (TCO) PHEVs toand promote and popularize PHEVs. This paper investigates the measures reduce the total cost owners of ownership of vehicle PHEVsmodel to promote andQin popularize PHEVs. Thisand paper investigates the usage datatocollected from PHEV using (TCO) the same of BYD in Shanghai, China, estimates the TCO usage data considering collected from owners the same model ofthe BYD Qin in Shanghai, China, estimates TCO of PHEVs the PHEV acquisition andusing operation costs.vehicle By comparing TCO with ICE vehicles, it isand found that thethe TCO of of PHEVs considering the acquisition and operation costs. By comparing the TCOway withtoICE vehicles, it is found that the TCO of ICE vehicles is lower than PHEVs. Optimizing the battery capacity is a feasible reduce the TCO of PHEVs, e.g., if the ICE vehicles is lower than PHEVs. the battery is a by feasible to reduce TCO of of PHEVs, e.g., the battery capacity is optimized as 20 Optimizing kWh, the TCO would capacity be reduced aboutway 6.2%. Besides,thethe TCO PHEVs is if more battery capacity is optimized as fuel 20 kWh, the TCOprice. would be reduced by about 6.2%. Besides, the TCO of PHEVs is more sensitive to the fluctuation of the and electricity sensitive to the fluctuation of theby fuel and electricity © 2018 The Authors. Published Elsevier B.V. price. © 2018 The Published Ltd. © 2018 The Authors. Authors. Published by by Elsevier B.V. Peer-review under responsibility ofElsevier organizing committee of the 8th International Congress of Information and Communication This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review (ICICT under responsibility of organizing committee of the 8th International Congress of Information and Communication Technology 2018). Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Technology (ICICT 2018). Communication Technology. Keywords: Plug-in hybrid electric vehicles, total cost of ownership, batter capacity, energy cost. Keywords: Plug-in hybrid electric vehicles, total cost of ownership, batter capacity, energy cost.

1. Introduction 1. Introduction Hybrid electric vehicle (HEV) plays an important role in transferring from internal combustion engine (ICE) Hybridtoelectric plays an important roleof invehicles transferring from internal combustion engine (ICE) vehicles battery vehicle electric (HEV) vehicles (BEVs). As a type combining both advantages of tradition ICE vehicles and to battery electric vehicles (BEVs). As a type of vehicles combining both advantages of tradition BEVs, HEVs are appealing to customers as they have longer range than BEVs and save more energyICE vehicles and BEVs, HEVs are appealing to customers as they have longer range than BEVs and save more energy * Corresponding author. Tel.: +86 15850582487 E-mail address:[email protected] * Corresponding author. Tel.: +86 15850582487 © 2018 Theaddress:[email protected] Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license E-mail https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Communication https://creativecommons.org/licenses/by-nc-nd/4.0/) Technology Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Communication Technology 一 一

1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Communication Technology 10.1016/j.procs.2018.04.217

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than ICE vehicles. The most attracting characteristic of HEVs is that the accessibility of charging infrastructure which hinders the development of BEVs1. In addition to the support of an internal combustion engine, HEVs have an electric motor (EM) to drive the vehicle. Plug-in hybrid electric vehicle (PHEV) is an extended type of HEVs which can be charged in electricity grid and fueled by the traditional way2. PHEVs can be operated in charge depleting electrically (CDE) mode, using the battery as the primary energy source, or charge sustaining (CS) mode, using the internal combustion engine as the primary energy source3. In the CS mode, some vehicles not only run with an energy source, but also use the major engine to get more power. Apart from these two modes, blended PHEVs also have another running mode which is called charge depleting blended (CDB) mode when ICE and EM work together to improve the efficiency of energy consumption and save more energy than ICE vehicles4. There is a tradeoff between battery size and weight of battery pack5. Drivers want as much as the high capacity of battery pack to get more electrical driving miles. However, large battery size has impacts on the weight of battery pack that leads to more energy consumptions. The optimization of battery capacity becomes an important research. This research is based on the operation data collected from Shanghai International Automobile City (SIAC). This data includes one year’s usage data of 50 vehicles with the same model of BYD Qin from May 2015 to May 2016. BYD Qin is one of blended PHEV models which is driven by the combining power of internal combustion engine and electric motor. Vehicle terminals, such as global positioning system data loggers and instruments to measure fuel and electricity consumptions, are installed on vehicles, to record in-use data and vehicles’ driving information at a rate of about 2 samples per minute. Provided with the financial incentives offered by the central and local government, a TCO model can be built up to give insight into the market acceptance of PHEVs in comparison of ICE vehicles. The main contributions of this paper include: (1) build a TCO model of ICE vehicles and PHEVs respectively according to owners ’ travel patterns; (2) optimize the battery size to lower the total cost during the vehicle lifecycle; (3) quantify the impacts of the fluctuation of energy prices on the TCO of PHEVs. This paper is organized as follows. After the literature review, in Section 3, a TCO analysis model is built up considering the costs generated during the acquisition and operation period. In Section 4, applied the usage data of PHEVs from Shanghai, the TCO of PHEVs and ICE vehicles are compared. In Section 5, the sensitivity analysis of battery capacity is conducted to reduce the TCO of the PHEVs, and an optimal battery size is suggested for BYD Qin users in Shanghai. In Section 6, the sensitivity analysis of fuel price and electricity price is conducted to quantify the impacts of energy price on the TCO of PHEVs. The conclusions and recommendations are presented in Section 7.

2. Literature review As an increasingly popular type of new energy vehicles, the TCO analysis of PHEV has received a lot of attention of scholars in the former researches. Neubauer et al.6 made an economic comparison between PHEVs and conventional ICE vehicles and found that energy management methodology, all-electric range, maximum starting state of charge and basic charge timing generally have a small impact on the TCO of PHEVs while PHEV economics are sensitive to driving patterns and the availability of an at-work charger. Propfe et al.7 analyzed the cost competitiveness of different types of hybrid electric vehicles integrating the maintenance and repair cost and the expected resale value of alternative vehicles in one extensive total cost of ownership model by 2020 but no single electric drive option dominated in this research. However, these two researches are based on simulation rather than operation data of PHEVs. After getting the operation data of PHEVs, the effects of policy, battery capacity and fuel, electricity and battery prices on the TCO of PHEVs were evaluated in several researches. Redelbach et al.5 introduced a holistic approach to optimize the battery size of PHEVs and EREVs under German market conditions by analyzing the impact of different driving behaviors on the optimal battery size from total cost of ownership (TCO) perspective and indicated that the public authorities may influence the results of TCO by providing purchasing bonus and increasing fuel price. Lin et al.8 proposed a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost and range limitation cost and found that the charging infrastructure, battery cost, and optimal driving range will affect the TCO significantly. Hou et al.9 proposed a TCO model for battery sizing of PHEVs which innovatively integrates the Beijing driving database and the energy management strategies including battery, fuel, electricity and salvage costs in yearly cash flows and found that fuel price and battery price are the two main factors in the TCO model. Plötz et



Yan Xia et al. / Procedia Computer Science 131 (2018) 377–386 Xia, Yang, Wang and Cheng./ Procedia Computer Science00 (2018) 000–000

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al.10 computed TCO projections for conventional and electric vehicles based on changing future fuel, electricity and battery prices and found that PHEVs cost optimal for many drivers. Du et al.11 provided a method of optimal vehicle designs to realize minimum life cycle cost and maximum petroleum consumption under different scenarios and found that gasoline price affects the TCO greatly and electricity price has minimum impact on TCO of PHEVs. The previous studies show that the government incentives, battery capacity, purchase price, and energy cost are main influential factors that affect the TCO of PHEVs. But few of these researches considered the driving pattern from the data and within-day recharging pattern. And because the battery price depends on the development of manufacturing industry of battery, this factor is not considered in this paper. 3. Acquisition and operation cost The ownership cost is calculated as the present value of the total acquisition and operating cost in the lifespan, which is assumed as 10 years 12. The operating cost part is divided into two parts, i.e., fuel cost and electricity cost. The purchase cost of vehicle depends on the capacity of the battery, growing up with the increase of battery size. The operating costs are mainly determined by charging and refueling expenditure. The objective function is set to minimize the total cost of ownership:

min C r   Ci r   Cr r 

(1)

r [ 0,  ]

where Ci r  is acquisition cost (¥), Cr r  is operating cost (¥), and C r  is the total cost of ownership (¥). The detailed descriptions of how to estimate the acquisition and operating cost are presented in the following

sections.

3.1 Acquisition Cost Acquisition cost refers to the purchase cost which is determined by the vehicle frame and battery size. Larger battery size indicates more cells in the battery and higher cost. Because of the car-buying subsidy and tax credit, the actual cost of buying a PHEV is usually lower than the retail price. Besides taking purchasing cost into account, it is essential to evaluate the resale value in order to comprehensively assess the life cycle cost of a vehicle. The resale value of today’s vehicles in Germany accounts for 36% of the initial purchasing price7. Due to the effect of brand and quality, the resale value of ICE automobiles made in China is assumed as 20% of purchasing price, according to the situation in China when the vehicle life is assumed as 10 years. The resale value is assumed to be 15% of initial purchasing price of PHEVs at the end of 10 years12. The function of acquisition cost is as follows7:

Ci r   Cb r   Cre r   Cs  Ct where Ci r  is acquisition cost (¥) during the vehicle life,

resale value (¥) after the life of the

vehicle, Cs

(2) Cb r 

is purchasing cost (¥) of the vehicle, Cre r  is the is subsidy (¥) provided by the government, and Ct is the tax credit.

3.2 Operating Cost We assume PHEVs and ICE vehicles have the same insurance and maintenance costs in this research. Thus, the operating cost is assumed to be composed of fuel cost and electricity cost. The share of driving in CDE mode and blended mode depends on the electric driving range and the driving pattern of the individual user. Recharge decision, constrained by travel activities and charger network coverage, affects the share of the distance traveled on electricity 3 . From the data, it is feasible to extract the information of daily driving distance and the distance traveled on electricity of PHEVs. With the local energy price, the cost in the operation can be calculated as follows:

C r r   C f r   C e r 

where Cr r  is total cost (¥) in operation, operation.

(3) C f r 

is fuel cost (¥) in operation, and

Ce r 

is electricity cost (¥) in

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3.2.1 Fuel Cost General blended PHEVs provide charge depleting electrically (CDE) mode, charge sustaining (CS) mode and charge depleting blended (CDB) mode. Fuel will be consumed in the CS mode and CDB mode. Due to different consumption rates in the CS mode and CDB mode and the consuming speed in blended mode is lower than that in the CS mode because of the participation of electricity, the cost of fuel and gasoline is divided into two parts as follows: CF r   1  S f1 r  Dp r   S f 2 r  Ds r   g  i  CRF (4)





1

1-

1  i n

CRF 

(5)

i

where 1 is share (%) of driving in CDB mode , S f r  is fuel consumption rate (L/km) in CDB mode, D p r  is

1

S f 2 r  is

fuel

Ds r  is

annual driving distance (km) in CDB mode and CDE mode, consumption rate (L/km) in CS mode, annual driving distance (km) in CS mode, g is fuel price (¥/L), i is influence factor of charging infrastructure3, and CRF is capital recovery factor representing the ratio of a constant annuity to the present value of receiving that annuity over n years. 3.2.2 Electricity Cost As an important part of the operation of PHEVs, the electricity needs to be accurately estimated from the data which will have a significant impact on the estimation of total cost. The electricity cost refers to the consumed electricity in the CDE mode and CDB mode apart from electricity transferred from the fuel. The function of electricity cost is set up as follows:





C E r   1  Se1 r   D p r   1  1   Se2 r   D p r   e 

1  CRF c

(6)

where S e r  is electricity consumption rate (kWh/km) in CDE mode, Se r  is electricity consumption rate (kWh/km) in CDB mode, e is the price of electricity (¥/kWh), and c is charging efficiency of electricity (%). The annual travelled distance in the CDE, CS and CDB mode is based on the probability density function h(x) which derives from the distribution of daily travelled distance. The function of annual driving distance in different driving modes is defined as follows r (7) h  x   xdx  0 D p r   r  i  D m   h x  xdx  1

2

0

Ds r   D  Dp r 

(8)

Where D is the annual total driving distance (km), hx  is probability density function of daily travelled distance, rm is the maximal daily driving distance (km).

4. Comparisons between PHEVs and ICE vehicles When BYD Qin is operated in the electric mode, the electric motor drives the vehicle alone, and the maximal driving mileage can reach 70km13. When the battery power is lower than electric range limitation, the vehicle mode will automatically or manually switch to CS mode. In all modes, Qin can also feed the brake energy back, that is the motor brings the charge back to the battery about 2.5-3 kWh per 100 km which can support additional traveling about 15 km13.

4.1 Basic Assumptions The parameters adopted in the TCO model is summarized in Table 1. PHEVs can be charged either at home or



Yan Xia et al. / Procedia Computer Science 131 (2018) 377–386 Xia, Yang, Wang and Cheng./ Procedia Computer Science00 (2018) 000–000

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workplace, however, the market price of electricity is different for the residential, commercial or industrial usage. As shown in Fig. 1, the charging behavior at night is different from that at daytime which will have an impact on the charging cost so that it is essential to combine these two situations together. In Shanghai, the policy of peak-valley electricity price is conducted, that is, the peak electricity is from 6AM to 10PM when the price is ¥0.617/kWh and the off-peak electricity is from 10PM to 6AM when the price is ¥0.307/kWh14. In addition, because the price of fuel and gasoline changed frequently, the average price is taken in this research, set as ¥6.15/L15.

Fig.1. Charging starting time distribution

The retail price of BYD Qin is ¥183,900. In order to vigorously advocate electric vehicles and plug-in hybrid vehicles, the central government of China provides subsidies of ¥24,000 for all PHEVs, and subsidies provided by the local government for the PHEVs cannot exceed 50% of the subsidies from the state. According to the new policy of subsidy in Shanghai in 2017, BYD Qin can get ¥10,000 subsidy in addition to the state subsidies16. Thus, BYD Qin can get a total of ¥34,000 subsidies, and the actual purchasing price of the vehicle is ¥149,900. BYD G6, a model of ICE vehicles, is adopted as the counterpart of BYD Qin for TCO analysis. These two models have similar vehicle power and configuration. According to the current market price, the market price of BYD G6 is ¥108,800 which is lower than the actual price of BYD Qin. The battery of BYD Qin has a total of 10 groups of battery cells and the price of each group is about ¥6,0009. We assume the total price of BYD Qin battery components is about ¥60,000. Table 1. Basic assumptions Scenario parameter

Unit

Value (2017)

Fuel price

¥/L

6.15

Electricity price (Peak)

¥/kWh

0.617

Electricity price (Off-Peak)

¥/kWh

0.307

Battery pack cost

¥/kWh

4500

Yearly interest rate

%

6

Length of vehicle life cycle

year

10

6382

Xia, Yang, Wang and (2018) 000–000 Yan XiaCheng./ et al. / Procedia Computer Science00 Science 131 (2018) 377–386 Charging efficiency of electricity

%

90

4.2 Driving Behaviors of PHEVs A PHEV’s electricity and fuel consumptions depend on whether it is operated in CDE, CS mode or CDB mode. In this research, the indicator of distinguishing different operating mode is the battery state of charge (SOC). According to the data analysis, the vehicle will turn into CS mode after the SOC is consumed by 85%. Therefore, 15% of the SOC is adopted as the threshold when the CDE mode is switched. The driving trip data contains 34,126 trips of 50 vehicles in one year. Only 2,309 trips are found purely driven under CDE mode. 5,286 trips, about 15% of total trips, are switched into CS mode during the trip because the SOC falls below 15%. Most trips are running in the CDB mode consuming fuel and electricity at the same time. The locations of charging are analyzed as well. The places where drivers frequently charge their vehicles at night are assumed as their home locations. There are 25479 charging events, of which about 7,005 charging events are away-from-home, and the others are charged at home. As the vehicles can be charged by normal household voltage,

these vehicles are more frequently charged at the workplaces or shopping centers compared to other types of

PHEVs or BEVs17. The average number of the days when the vehicle was driven is 314 in 365 days according to the data analysis in the 50 vehicles. The results of the analysis of daily traveled distance are shown in Fig. 2. The distribution interval with the most significant probability occurs between 80 and 100 kilometers, which indicates that most drivers drive around 90 kilometers and exceed the driving distance that can be covered in the CDE mode18. From the aspect of saving energy and cost of ownership, the battery capacity is supposed to be enlarged to reduce the fuel consumption.

Fig. 2. Frequency distribution of daily driving distance

4.3 Results The average daily traveled distance is 110 kilometers in the data analysis which is the basic for the calculation of fuel cost in the operation period. The fuel consumption rate of BYD G6 is 6.9L per hundred kilometers19. The consumption rate of fuel and electricity of BYD Qin in different operating mode is estimated using the real-world driving data. The consumption rate is illustrated in Table 2. Table 2. Consumption rate

ICE

Fuel(L/100km)

Electricity(kwh/km)

6.9

-



Yan Xia et al. / Procedia Computer Science 131 (2018) 377–386 Xia, Yang, Wang and Cheng./ Procedia Computer Science00 (2018) 000–000

PHEV

CDE mode

-

0.2

CDB mode

1.7

0.17

CS mode

7

-

383 7

The comparison between the total costs of the ownership is based on the TCO Model in Section 2, the assumptions in Section 4.1 and driving data analysis in Section 4.2. The results are shown in Table 3. Table 3. TCO comparison between ICE and PHEV ICE

PHEV

Acquisition (¥)

108,800

149,900

Fuel cost (¥)

120,243

75,563

Electricity cost (¥)

-

10,486

Resale value (¥)

21,760

22,485

Total cost (¥)

207,283

213,464

The results show that the total cost of ownership of ICEVs is lower than that of PHEVs, although the electricity price is much lower than the fuel price and the subsidy is provided for the PHEVs.

5. Sensitivity analysis of battery capacity impacts It is noted that the CS mode accounted for a larger proportion in the annual mileage and there are still many daily driving distances exceeding 70 kilometers which is the maximum range of purely electric driving according to the distribution of daily driving distance. It indicates that the battery capacity of BYD Qin is still not large enough to satisfy the daily travel demand. However, the larger the battery size is, the heavier the vehicle becomes. The vehicle weight has impacts on the vehicle's fuel consumption rate and electricity consumption rate. Since no data of PHEVs with other weights is obtained, the impact of body weight on the energy consuming rate refer to relevant research20. Battery capacity has impacts on the increased proportion of CDE mode and blended mode in the daily distance, and it also increases the weight of the vehicle, thereby speeding up fuel and electricity consumption rate. A balance should be found so that the increase in battery capacity will bring about the greatest benefits. The analysis of impacts of the battery capacity on TCO is based on the TCO analysis established in Section 3 and the related assumptions in Section 4. For simplicity, the ratio between the CDE mode and the blended mode before increasing battery capacity is assumed the same, and the impacts of changing vehicle weight on the consumption rate of fuel and electricity are not considered.

Fig. 3. Changed total cost relative to battery capacity

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Xia, Yang, Wang and Cheng./ Procedia Computer Science00 (2018) 000–000 Yan Xia et al. / Procedia Computer Science 131 (2018) 377–386

The reduction of the TCO of PHEVs can be used as an indicator as increasing battery size21. According to the total cost analysis in Fig 3, the optimal range of purely electric driving analyzed here is 100 kilometers. The compared results are shown in Table 4. Table 4: TCO of different batter capacities Capacity(km)

70

80

90

100

Acquisition cost (¥)

149,900

158,471

167,043

175,614

Fuel cost (¥)

75,563

59,092

39,373

29,220

Electricity (¥)

10,486

14,458

19,164

21,709

Resale value (¥)

22,485

23,771

25,056

26,342

Total cost (¥)

213,464

208,251

200,523

200,201

ICE cost (¥)

207,283

207,283

207,283

207,283

The results indicate that as the battery capacity increases, the total cost of ownership decreases gradually. When the capacity approaches the maximum value of driving distance, the total cost of ownership will be lower than that of the counterparts of ICE vehicles.

6. Sensitivity analysis of price impacts The high total cost of ownership of PHEVs lowers the market acceptance22. Actions should be taken to reduce the TCO to improve the development of PHEVs in China. The price of fuel and electricity is regarded as a critical factor in reducing the TCO. The sensitivity analysis contains two parts. These two parts are about the impacts of fuel price and electricity price on the TCO when the driving range of the battery is 70 kilometers and 100 kilometers. The reason for selecting these two scenarios is the obvious difference between the fuel and electricity consumption in these two scenarios. The results are shown in Fig. 4.

(a)

(b)



Yan Xia al. / Procedia Computer Science 131 (2018) Xia, Yang, Wang andetCheng./ Procedia Computer Science00 (2018)377–386 000–000

(c)

3859

(d)

Fig.4. (a) Changed cost with fuel price (70km); (b) Changed cost with electricity price (70km) ; (c) Changed cost with fuel price (100km); (d) Changed cost with electricity price (100km)

The comparison of TCOs with the change of fuel price and electricity price shows that the fuel price takes a more critical role in the TCO than electricity price. As the battery capacity increases, the electricity price has a more visible impact on the TCO of PHEVs.

7. Conclusions This paper makes a comparison of the TCO between ICE vehicles and PHEVs using the usage data collected from the PHEV owners in Shanghai, China. The results show that the increase of the battery capacity has a positive impact on reducing the TCO of PHEVs. A battery size of 20 kWh is suggested for the BYD Qin owners in Shanghai for the purpose of reducing their TCOs. The fuel price and electricity price affect the TCO of PHEV significantly. Overall, the TCO of PHEVs is more sensitive to the fluctuation of fuel prices, and the price of electricity play a more important role when the battery capacity is increased. The conclusions from this paper are helpful for the customers who are considering replacing their ICE vehicles with PHEVs. The results can also assist the automakers and the government to make better decisions on the aspects of battery design, subsidy policy, etc. Acknowledgement This study is supported by the Projects of International Cooperation and Exchange (No. 51561135003), the Youth Program (No. 71501038), Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603). The analysis data provided by Shanghai International Automobile City (SIAC) support this study. References 1. Merrill, S.A., Transitions to Alternative Transportation Technologies--Plug-in Hybrid Electric Vehicles. Social Science Electronic Publishing, 2010. 2. Pourabdollah, M., Murgovski, N., Grauers, A., and Bo, E., Optimal Sizing of a Blended PHEV Powertrain. IEEE Transactions on Vehicular Technology, 2013. 62(6): p. 2469-2480. 3. Dong, J. and Z. Lin, Within-day recharge of plug-in hybrid electric vehicles: Energy impact of public charging infrastructure. Transportation Research Part D, 2012. 17(5): p. 405-412. 4. Redelbach, M., E.D. Özdemir, and H.E. Friedrich, Optimizing battery sizes of plug-in hybrid and extended range electric vehicles for different user types. Energy Policy, 2014. 73: p. 158-168. 5. Neubauer, J., A. Brooker, and E. Wood, Sensitivity of plug-in hybrid electric vehicle economics to drive patterns, electric range, energy management, and charge strategies. Journal of Power Sources, 2013. 236 (15 August 2013): p. 357-364.

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