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Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations

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  • Zhang, Cheng
  • Yang, Fan
  • Ke, Xinyou
  • Liu, Zhifeng
  • Yuan, Chris

Abstract

Autonomous electric vehicles have attracted enormous interests as an effective way to significantly improve urban transportation efficiency, reduce commute cost and the corresponding environmental burden. This work proposed a multiphysics energy model to quantify the energy consumption and greenhouse gas emissions from an autonomous electric vehicle based on vehicle dynamics and the vehicle system energy demand. A case study is conducted on a mid-size autonomous electric vehicles taxi operating in New York City based on possible driving data and scenarios. It is found that the monthly average unit energy consumption for the autonomous electric vehicle ranges from 325 to 397 Wh km−1, and the greenhouse gas emissions is 6.5% more from an autonomous electric vehicle with a driver than that without a driver. The study provides a physical approach for quantifying the energy consumption and greenhouse gas emissions from an autonomous electric vehicle, and can support the sustainable development and deployment of autonomous electric vehicle technologies in future.

Suggested Citation

  • Zhang, Cheng & Yang, Fan & Ke, Xinyou & Liu, Zhifeng & Yuan, Chris, 2019. "Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919312711
    DOI: 10.1016/j.apenergy.2019.113597
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    4. Aleksandra Kaczyńska & Piotr Sulikowski & Jarosław Wątróbski & Wojciech Sałabun, 2023. "Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management," Energies, MDPI, vol. 16(18), pages 1-17, September.
    5. 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.
    6. Amirgholy, Mahyar & Gao, H. Oliver, 2023. "Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach," Applied Energy, Elsevier, vol. 329(C).
    7. Yoo, Sunbin & Kumagai, Junya & Kawabata, Yuta & Keeley, Alexander & Managi, Shunsuke, 2021. "Willingness to Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles," MPRA Paper 108882, University Library of Munich, Germany.
    8. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    9. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).
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