Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability

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dc.contributor.authorPannee Suanpang
dc.contributor.authorPitchaya Jamjuntr
dc.contributor.correspondenceP. Suanpang; Department of Information Technology, Faculty of Science & 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.abstractThis paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learningÕs capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction. © 2024 by the authors.
dc.identifier.citationSustainability (Switzerland)
dc.identifier.doi10.3390/su16167180
dc.identifier.issn20711050
dc.identifier.scopus2-s2.0-85202637341
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4451
dc.languageEnglish
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectbattery management
dc.subjectelectric vehicle
dc.subjectenhancing performance
dc.subjectoptimizing
dc.subjectQ-learning
dc.subjectsmart city
dc.subjectsustainability
dc.titleOptimal Electric Vehicle Battery Management Using Q-learning for Sustainability
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85202637341&doi=10.3390%2fsu16167180&partnerID=40&md5=08f5aa53a619ca6f397e2b9a3b97a992
oaire.citation.issue16
oaire.citation.volume16
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