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Individual trip chain distributions for passenger cars: Implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles

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Listed:
  • He, Xiaoyi
  • Wu, Ye
  • Zhang, Shaojun
  • Tamor, Michael A.
  • Wallington, Timothy J.
  • Shen, Wei
  • Han, Weijian
  • Fu, Lixin
  • Hao, Jiming

Abstract

The energy and environmental benefits of electric vehicles (EVs) are highly dependent on individual driving patterns. To characterize individual driving patterns in Beijing, a populated megacity in East Asian, GPS-based travel data from 459 private passenger vehicles were gathered covering nearly 17,000 sampling days in 2013–2015. The data were analyzed using a statistical model to produce 0.5h, 4h, 8h and daily individual trip chain distributions, which were used to evaluate customer acceptance for battery electric vehicles (BEVs) based on inconvenience thresholds and to assess the energy consumption for plug-in hybrids (PHEVs). The mean daily distances travelled on weekdays and weekends in Beijing were found to be 44.6km and 51.4km respectively. In Beijing the mean habitual travel distance (40.4km) is modest, the random component of travel distance is lower, and the fraction of habitual travel is higher than for cities in the U.S. and in Germany. These factors make EV deployment in Beijing more favorable than in the U.S. or Germany. We show that the estimated acceptance rate for BEVs is very sensitive to the predetermined inconvenience threshold level. The abundant public transportation alternatives and traffic management in Beijing are factors which reduce the inconvenience of BEVs and may make them acceptable without substantially increased cost for larger battery capacity. PHEVs with all-electric ranges of 50km (PHEV50) have an ensemble utility factor (UF) and equivalent gasoline consumption estimated to be 0.55 and 4.39L/100km. However, for 50% of vehicle owners PHEV50s would have a UF of 0.94 and equivalent gasoline consumption of 3.03L/100km. Our results show that attention to heterogeneity among individuals instead of analysis at the ensemble level is essential to understanding the real-world acceptance and benefits of EVs.

Suggested Citation

  • He, Xiaoyi & Wu, Ye & Zhang, Shaojun & Tamor, Michael A. & Wallington, Timothy J. & Shen, Wei & Han, Weijian & Fu, Lixin & Hao, Jiming, 2016. "Individual trip chain distributions for passenger cars: Implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 180(C), pages 650-660.
    • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:650-660
    DOI: 10.1016/j.apenergy.2016.08.021
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    Cited by:

    1. Ke, Wenwei & Zhang, Shaojun & He, Xiaoyi & Wu, Ye & Hao, Jiming, 2017. "Well-to-wheels energy consumption and emissions of electric vehicles: Mid-term implications from real-world features and air pollution control progress," Applied Energy, Elsevier, vol. 188(C), pages 367-377.
    2. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    3. Yali Zheng & Xiaoyi He & Hewu Wang & Michael Wang & Shaojun Zhang & Dong Ma & Binggang Wang & Ye Wu, 2020. "Well-to-wheels greenhouse gas and air pollutant emissions from battery electric vehicles in China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(3), pages 355-370, March.
    4. Ren, Yilong & Lan, Zhengxing & Yu, Haiyang & Jiao, Gangxin, 2022. "Analysis and prediction of charging behaviors for private battery electric vehicles with regular commuting: A case study in Beijing," Energy, Elsevier, vol. 253(C).
    5. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    6. Chen, Jiahui & Wang, Fang & He, Xiaoyi & Liang, Xinyu & Huang, Junling & Zhang, Shaojun & Wu, Ye, 2022. "Emission mitigation potential from coordinated charging schemes for future private electric vehicles," Applied Energy, Elsevier, vol. 308(C).
    7. Wang, Zhan & Deng, Xiangzheng & Bai, Yuping & Chen, Jiancheng & Zheng, Wentang, 2016. "Land use structure and emission intensity at regional scale: A case study at the middle reach of the Heihe River basin," Applied Energy, Elsevier, vol. 183(C), pages 1581-1593.
    8. Eisenmann, Christine & Buehler, Ralph, 2018. "Are cars used differently in Germany than in California? Findings from annual car-use profiles," Journal of Transport Geography, Elsevier, vol. 69(C), pages 171-180.
    9. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    10. Lin, Boqiang & Shi, Lei, 2022. "Do environmental quality and policy changes affect the evolution of consumers’ intentions to buy new energy vehicles," Applied Energy, Elsevier, vol. 310(C).
    11. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    12. Li, Lin & Dababneh, Fadwa & Zhao, Jing, 2018. "Cost-effective supply chain for electric vehicle battery remanufacturing," Applied Energy, Elsevier, vol. 226(C), pages 277-286.
    13. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.
    14. Sodenkamp, Mariya & Wenig, Jürgen & Thiesse, Frédéric & Staake, Thorsten, 2019. "Who can drive electric? Segmentation of car drivers based on longitudinal GPS travel data," Energy Policy, Elsevier, vol. 130(C), pages 111-129.
    15. Ji, Wei, 2018. "Data-Driven Behavior Analysis and Implications in Plug-in Electric Vehicle Policy Studies," Institute of Transportation Studies, Working Paper Series qt6dw4d18t, Institute of Transportation Studies, UC Davis.
    16. Zhao, Yinan & Wen, Yifan & Wang, Fang & Tu, Wei & Zhang, Shaojun & Wu, Ye & Hao, Jiming, 2023. "Feasibility, economic and carbon reduction benefits of ride-hailing vehicle electrification by coupling travel trajectory and charging infrastructure data," Applied Energy, Elsevier, vol. 342(C).
    17. Boya Zhou & Shaojun Zhang & Ye Wu & Wenwei Ke & Xiaoyi He & Jiming Hao, 2018. "Energy-saving benefits from plug-in hybrid electric vehicles: perspectives based on real-world measurements," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(5), pages 735-756, June.
    18. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    19. Fernández-Dacosta, Cora & Shen, Li & Schakel, Wouter & Ramirez, Andrea & Kramer, Gert Jan, 2019. "Potential and challenges of low-carbon energy options: Comparative assessment of alternative fuels for the transport sector," Applied Energy, Elsevier, vol. 236(C), pages 590-606.
    20. He, Liqiang & Hu, Jingnan & Zhang, Shaojun & Wu, Ye & Zhu, Rencheng & Zu, Lei & Bao, Xiaofeng & Lai, Yitu & Su, Sheng, 2018. "The impact from the direct injection and multi-port fuel injection technologies for gasoline vehicles on solid particle number and black carbon emissions," Applied Energy, Elsevier, vol. 226(C), pages 819-826.
    21. He, X. & Wang, F. & Wallington, T.J. & Shen, W. & Melaina, M.W. & Kim, H.C. & De Kleine, R. & Lin, T. & Zhang, S. & Keoleian, G.A. & Lu, X. & Wu, Y., 2021. "Well-to-wheels emissions, costs, and feedstock potentials for light-duty hydrogen fuel cell vehicles in China in 2017 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).

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