Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

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dc.contributor.authorPannee Suanpang
dc.contributor.authorPitchaya Jamjuntr
dc.contributor.correspondenceP. Suanpang; Department of Information Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
dc.date.accessioned2025-03-10T07:34:20Z
dc.date.available2025-03-10T07:34:20Z
dc.date.issued2024
dc.description.abstractAs global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development. © 2024 by the authors.
dc.identifier.citationWorld Electric Vehicle Journal
dc.identifier.doi10.3390/wevj15020067
dc.identifier.issn20326653
dc.identifier.scopus2-s2.0-85185832055
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4465
dc.languageEnglish
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectcharge station
dc.subjectelectric vehicle
dc.subjectmulti-agent reinforcement learning
dc.subjectoptimizing
dc.subjectrecommendation systems
dc.subjectsmart cities
dc.titleOptimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185832055&doi=10.3390%2fwevj15020067&partnerID=40&md5=8173897979b959b118559c7953a4a6f7
oaire.citation.issue2
oaire.citation.volume15
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