IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v250y2019icp147-160.html
   My bibliography  Save this article

Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing

Author

Listed:
  • Tu, Wei
  • Santi, Paolo
  • Zhao, Tianhong
  • He, Xiaoyi
  • Li, Qingquan
  • Dong, Lei
  • Wallington, Timothy J.
  • Ratti, Carlo

Abstract

The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:147-160
    DOI: 10.1016/j.apenergy.2019.04.157
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919308177
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.04.157?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kelly, Jarod C. & MacDonald, Jason S. & Keoleian, Gregory A., 2012. "Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics," Applied Energy, Elsevier, vol. 94(C), pages 395-405.
    2. 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.
    3. Xinyu Chen & Hongcai Zhang & Zhiwei Xu & Chris P. Nielsen & Michael B. McElroy & Jiajun Lv, 2018. "Impacts of fleet types and charging modes for electric vehicles on emissions under different penetrations of wind power," Nature Energy, Nature, vol. 3(5), pages 413-421, May.
    4. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    5. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
    6. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    7. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    8. Zarar Siddiqi & Ron Buliung, 2013. "Dynamic ridesharing and information and communications technology: past, present and future prospects," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(6), pages 479-498, August.
    9. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    10. Dijk, Marc & Orsato, Renato J. & Kemp, René, 2013. "The emergence of an electric mobility trajectory," Energy Policy, Elsevier, vol. 52(C), pages 135-145.
    11. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    12. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    13. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    14. Sun, Lishan & Wang, Shunchao & Liu, Shuli & Yao, Liya & Luo, Wei & Shukla, Ashish, 2018. "A completive research on the feasibility and adaptation of shared transportation in mega-cities – A case study in Beijing," Applied Energy, Elsevier, vol. 230(C), pages 1014-1033.
    15. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    16. Grüger, Fabian & Dylewski, Lucy & Robinius, Martin & Stolten, Detlef, 2018. "Carsharing with fuel cell vehicles: Sizing hydrogen refueling stations based on refueling behavior," Applied Energy, Elsevier, vol. 228(C), pages 1540-1549.
    17. Yanyan Xu & Serdar Çolak & Emre C. Kara & Scott J. Moura & Marta C. González, 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility," Nature Energy, Nature, vol. 3(6), pages 484-493, June.
    18. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    19. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    20. Wolbertus, Rick & Kroesen, Maarten & van den Hoed, Robert & Chorus, Caspar, 2018. "Fully charged: An empirical study into the factors that influence connection times at EV-charging stations," Energy Policy, Elsevier, vol. 123(C), pages 1-7.
    21. 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.
    22. Zachary A. Needell & James McNerney & Michael T. Chang & Jessika E. Trancik, 2016. "Potential for widespread electrification of personal vehicle travel in the United States," Nature Energy, Nature, vol. 1(9), pages 1-7, September.
    23. Plötz, Patrick & Jakobsson, Niklas & Sprei, Frances, 2017. "On the distribution of individual daily driving distances," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 213-227.
    24. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    25. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
    26. Brady, John & O’Mahony, Margaret, 2016. "Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas," Applied Energy, Elsevier, vol. 177(C), pages 165-178.
    27. Vivian Scott & Oliver Geden, 2018. "The challenge of carbon dioxide removal for EU policy-making," Nature Energy, Nature, vol. 3(5), pages 350-352, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong, Xianqian & Wu, Jinglai & Zhang, Nong & Wang, Bing, 2022. "Energy efficiency optimization of Simpson planetary gearset based dual-motor powertrains for electric vehicles," Energy, Elsevier, vol. 259(C).
    2. Qiang Xing & Zhong Chen & Ziqi Zhang & Xiao Xu & Tian Zhang & Xueliang Huang & Haiwei Wang, 2020. "Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior," Energies, MDPI, vol. 13(6), pages 1-32, March.
    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. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    5. Lu, Ke & Xu, Yuan, 2024. "The continuance behavioral intention to adopt electrified ride-sourcing: Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    6. Zhang, Zhe & Yu, Qing & Gao, Kun & He, Hong-Di & Liu, Yang & Huang, Haichao, 2025. "Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure," Applied Energy, Elsevier, vol. 377(PA).
    7. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    8. Li, Zhitao & Gao, Fan & Hao, Jingjing & Liang, Jian & Han, Chunyang & Tang, Jinjun, 2025. "Exploring spatiotemporal characteristics of ride-hailing ridership connecting with metro stations: A comparative analysis of holidays, weekdays, and weekends," Journal of Transport Geography, Elsevier, vol. 123(C).
    9. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    10. Ding, Jiaxuan & Kim, Eui-Jin & Maksimenko, Vladimir & Bansal, Prateek, 2025. "Can decoy effects nudge ride-hailing drivers’ preferences for electric vehicles?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 196(C).
    11. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    12. Liu, Nian & Yan, Jinwei & Tan, Lu & Li, Xinhao & Chen, Youxin & Zhang, Kuan, 2025. "A low-carbon trading strategy of multi-microgrid system considering electricity-hydrogen-carbon coupling characteristics," Energy, Elsevier, vol. 327(C).
    13. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    14. Zhao, Chuyun & Tang, Jinjun & Zeng, Yu & Li, Zhitao & Gao, Fan, 2023. "Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions," Journal of Transport Geography, Elsevier, vol. 112(C).
    15. Cha, Kyoung-Soo & Kim, Dong-Min & Jung, Young-Hoon & Lim, Myung-Seop, 2020. "Wound field synchronous motor with hybrid circuit for neighborhood electric vehicle traction improving fuel economy," Applied Energy, Elsevier, vol. 263(C).
    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. Kwon, Kihan & Seo, Minsik & Min, Seungjae, 2020. "Efficient multi-objective optimization of gear ratios and motor torque distribution for electric vehicles with two-motor and two-speed powertrain system," Applied Energy, Elsevier, vol. 259(C).
    18. Yao, Jiwei & You, Fengqi, 2020. "Simulation-based optimization framework for economic operations of autonomous electric taxicab considering battery aging," Applied Energy, Elsevier, vol. 279(C).
    19. Wang, Jun-Cheng & Wang, Fa-Hui & Wang, Ya-Xiong & Chen, Shi-An, 2023. "Analysis of real-time energy losses of electric vehicle caused by non-stationary road irregularity," Energy, Elsevier, vol. 282(C).
    20. Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2022. "Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers," Applied Energy, Elsevier, vol. 319(C).
    21. Lei Wang & Wenxiang Li & Jinxian Weng & Dong Zhang & Wanjing Ma, 2023. "Do low-carbon rewards incentivize people to ridesplitting? Evidence from structural analysis," Transportation, Springer, vol. 50(5), pages 2077-2109, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. 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).
    4. Zhang, Shaojun & Li, Shuyang & Tian, Bowen & Fu, Xiao & Chen, Bokui & Wu, Xiaomeng & Wu, Ye, 2025. "Individual electric vehicle range evaluation and optimization by real-world usage data," Energy, Elsevier, vol. 320(C).
    5. Milan Straka & Rui Carvalho & Gijs van der Poel & v{L}ubov{s} Buzna, 2020. "Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data," Papers 2006.01672, arXiv.org, revised Jun 2020.
    6. 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.
    7. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    8. Wiedemann, Nina & Xin, Yanan & Medici, Vasco & Nespoli, Lorenzo & Suel, Esra & Raubal, Martin, 2024. "Vehicle-to-grid for car sharing - A simulation study for 2030," Applied Energy, Elsevier, vol. 372(C).
    9. Lee, Yongseung & Kim, Chongman & Shin, Juneseuk, 2016. "A hybrid electric vehicle market penetration model to identify the best policy mix: A consumer ownership cycle approach," Applied Energy, Elsevier, vol. 184(C), pages 438-449.
    10. Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2022. "Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers," Applied Energy, Elsevier, vol. 319(C).
    11. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    12. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    13. 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.
    14. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    15. Albert Hiesl & Jasmine Ramsebner & Reinhard Haas, 2021. "Modelling Stochastic Electricity Demand of Electric Vehicles Based on Traffic Surveys—The Case of Austria," Energies, MDPI, vol. 14(6), pages 1-19, March.
    16. Siobhan Powell & Gustavo Vianna Cezar & Liang Min & Inês M. L. Azevedo & Ram Rajagopal, 2022. "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption," Nature Energy, Nature, vol. 7(10), pages 932-945, October.
    17. Xu, Xiaodan & Aziz, H.M. Abdul & Liu, Haobing & Rodgers, Michael O. & Guensler, Randall, 2020. "A scalable energy modeling framework for electric vehicles in regional transportation networks," Applied Energy, Elsevier, vol. 269(C).
    18. Hartvigsson, Elias & Taljegard, Maria & Odenberger, Mikael & Chen, Peiyuan, 2022. "A large-scale high-resolution geographic analysis of impacts of electric vehicle charging on low-voltage grids," Energy, Elsevier, vol. 261(PA).
    19. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    20. Golsefidi, Atefeh Hemmati & Hipolito, F. & Pereira, Francisco Câmara & Samaranayake, Samitha, 2025. "Incremental expansion of large scale fixed and mobile charging infrastructure in stochastic environments: A novel graph-based Benders decomposition approach," Applied Energy, Elsevier, vol. 380(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:250:y:2019:i:c:p:147-160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.