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

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  • 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
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    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).

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