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Multi-Agent DDPG Based Electric Vehicles Charging Station Recommendation

Author

Listed:
  • Khalil Bachiri

    (ETIS Laboratory, CNRS, ENSEA, CY TECH, CY Cergy Paris University, 95011 Cergy, France
    LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco)

  • Ali Yahyaouy

    (LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco)

  • Hamid Gualous

    (LUSAC Laboratory, University of Caen Normandie, 14032 Caen, France)

  • Maria Malek

    (ETIS Laboratory, CNRS, ENSEA, CY TECH, CY Cergy Paris University, 95011 Cergy, France)

  • Younes Bennani

    (LIPN Laboratory—CNRS UMR 7030, La Maison des Sciences Numériques, University of Sorbonne Paris Nord, 93000 Paris, France)

  • Philippe Makany

    (LUSAC Laboratory, University of Caen Normandie, 14032 Caen, France)

  • Nicoleta Rogovschi

    (LIPADE Laboratory, University of Paris Descartes, 75006 Paris, France)

Abstract

Electric vehicles (EVs) are a sustainable transportation solution with environmental benefits and energy efficiency. However, their popularity has raised challenges in locating appropriate charging stations, especially in cities with limited infrastructure and dynamic charging demands. To address this, we propose a multi-agent deep deterministic policy gradient (MADDPG) method for optimal EV charging station recommendations, considering real-time traffic conditions. Our approach aims to minimize total travel time in a stochastic environment for efficient smart transportation management. We adopt a centralized learning and decentralized execution strategy, treating each region of charging stations as an individual agent. Agents cooperate to recommend optimal charging stations based on various incentive functions and competitive contexts. The problem is modeled as a Markov game, suitable for analyzing multi-agent decisions in stochastic environments. Intelligent transportation systems provide us with traffic information, and each charging station feeds relevant data to the agents. Our MADDPG method is challenged with a substantial number of EV requests, enabling efficient handling of dynamic charging demands. Simulation experiments compare our method with DDPG and deterministic approaches, considering different distributions and EV numbers. The results highlight MADDPG’s superiority, emphasizing its value for sustainable urban mobility and efficient EV charging station scheduling.

Suggested Citation

  • Khalil Bachiri & Ali Yahyaouy & Hamid Gualous & Maria Malek & Younes Bennani & Philippe Makany & Nicoleta Rogovschi, 2023. "Multi-Agent DDPG Based Electric Vehicles Charging Station Recommendation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6067-:d:1220460
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    References listed on IDEAS

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    Cited by:

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    2. Jiachen Li & Xingfeng Duan & Zhennan Xiong & Peng Yao, 2024. "Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction," Sustainability, MDPI, vol. 16(8), pages 1-27, April.

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