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PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging

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Listed:
  • Junchi Ma

    (School of Information Engineering, Chang’an University, Xi’an 710061, China)

  • Yuan Zhang

    (School of Information Engineering, Chang’an University, Xi’an 710061, China)

  • Zongtao Duan

    (School of Information Engineering, Chang’an University, Xi’an 710061, China)

  • Lei Tang

    (School of Information Engineering, Chang’an University, Xi’an 710061, China)

Abstract

Electric vehicles (EVs) are becoming increasingly popular in ride-hailing services, but their slow charging speed negatively affects service efficiency. To address this challenge, we propose PROLIFIC, a deep reinforcement learning-based approach for efficient EV scheduling and charging in ride-hailing services. The objective of PROLIFIC is to minimize passenger waiting time and charging time cost. PROLIFIC formulates the EV scheduling problem as a Markov decision process and integrates a distributed charging scheduling management model and a centralized order dispatching model. By using a distributed deep Q-network, the agents can share charging and EV supply information to make efficient interactions between charging and dispatch decisions. This approach reduces the curse of dimensionality problem and improves the training efficiency of the neural network. The proposed approach is validated in three typical scenarios with different spatiotemporal distribution characteristics of passenger order, and the results demonstrate that PROLIFIC significantly reduces the passenger waiting time and charging time cost in all three scenarios compared to baseline algorithms.

Suggested Citation

  • Junchi Ma & Yuan Zhang & Zongtao Duan & Lei Tang, 2023. "PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13553-:d:1237349
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    References listed on IDEAS

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