Multi-Agent DDPG Based Electric Vehicles Charging Station Recommendation

Khalil Bachiri (), Ali Yahyaouy, Hamid Gualous, Maria Malek, Younes Bennani, Philippe Makany and Nicoleta Rogovschi
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Khalil Bachiri: ETIS Laboratory, CNRS, ENSEA, CY TECH, CY Cergy Paris University, 95011 Cergy, France
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

Energies, 2023, vol. 16, issue 16, 1-17

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.

Keywords: EV charging station recommendation; reinforcement learning; deep learning; MADDPG; multi-region; smart city (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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