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Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption

Author

Listed:
  • Nan Ding

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Jingshuai Yang

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Zhibin Han

    (School of Automobile, Chang’an University, Xi’an 710064, China)

  • Jianming Hao

    (School of Highway, Chang’an University, Xi’an 710064, China
    Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA)

Abstract

In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we first develop a nonlinear electric energy consumption model based on typical driving cycles of suburban and urban areas, with consideration of vehicle load, travel distance, and speed. An adaptive particle swarm optimization algorithm is then designed to solve the problem. Moreover, we study cases built from the actual operational data of Company J and compare the optimal delivery schemes of EVRPTW-DC and EVRPTW under the traditional linear electric energy consumption law. The results show that our nonlinear energy consumption model, which provides a better simulation of energy consumption, can lead to a more realistic delivery plan. Finally, we explore the applicability of the proposed EVPRTW-DC and discuss the conditions of using a linear electric energy consumption coefficient.

Suggested Citation

  • Nan Ding & Jingshuai Yang & Zhibin Han & Jianming Hao, 2022. "Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption," Mathematics, MDPI, vol. 10(17), pages 1-27, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3099-:d:900605
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    References listed on IDEAS

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    Cited by:

    1. Nan Ding & Manman Li & Jianming Hao, 2023. "A Two-Phase Approach to Routing a Mixed Fleet with Intermediate Depots," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    2. Hao Qiang & Rui Ou & Yanchun Hu & Zhenyu Wu & Xiaohua Zhang, 2023. "Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    3. Mengke Li & Yongkui Shi & Meiyan Li, 2023. "Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions," Mathematics, MDPI, vol. 11(7), pages 1-19, March.

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