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Scheduling Model and Algorithm for Transportation and Vehicle Charging of Multiple Autonomous Electric Vehicles

Author

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  • Xiaoli Wang

    (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Zhiyu Zhang

    (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Mengmeng Jiang

    (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Yifan Wang

    (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

  • Yuping Wang

    (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)

Abstract

Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and electric vehicle technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations and long charging times. A critical challenge is maximizing passenger travel satisfaction while reducing the AEV idle time. This involves coordinating passenger transport and charging tasks via leveraging the information from charging stations, passenger transport, and AEV data. There are four important contributions in this paper. Firstly, we introduce an integrated scheduling model that considers both passenger transport and charging tasks. Secondly, we propose a multi-level differentiated charging threshold strategy, which dynamically adjusts the charging threshold based on both AEV battery levels and the availability of charging stations, reducing competition among vehicles and minimizing waiting times. Thirdly, we develop a rapid strategy to optimize the selection of charging stations by combining geographic and deviation distance. Fourthly, we design a new evolutionary algorithm to solve the proposed model, in which a buffer space is introduced to promote diversity within the population. Finally, experimental results show that compared to the existing state-of-the-art scheduling algorithms, the proposed algorithm shortens the running time of scheduling algorithms by 6.72% and reduces the idle driving time of AEVs by 6.53%, which proves the effectiveness and efficiency of the proposed model and algorithm.

Suggested Citation

  • Xiaoli Wang & Zhiyu Zhang & Mengmeng Jiang & Yifan Wang & Yuping Wang, 2025. "Scheduling Model and Algorithm for Transportation and Vehicle Charging of Multiple Autonomous Electric Vehicles," Mathematics, MDPI, vol. 13(1), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:1:p:145-:d:1558968
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    References listed on IDEAS

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    1. Imen Azzouz & Wiem Fekih Hassen, 2023. "Optimization of Electric Vehicles Charging Scheduling Based on Deep Reinforcement Learning: A Decentralized Approach," Energies, MDPI, vol. 16(24), pages 1-18, December.
    2. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
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