Optimal control strategy to charging and discharging techniques for electric vehicle battery pack optimization based on genetic algorithm and machine learning

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dc.contributor.authorYalcin, Sercan
dc.contributor.authorYildirim, Muhammed
dc.contributor.authorKhan, Muhammad Attique
dc.contributor.authorLi, Yang
dc.contributor.authorAlabdullah, Bayan
dc.contributor.authorCho, Yongwon
dc.contributor.authorNam, Yunyoung
dc.date.accessioned2026-06-19T06:39:59Z
dc.date.available2026-06-19T06:39:59Z
dc.date.issued2026
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis paper investigates optimal control strategies for charging and discharging battery packs, aiming to maximize lifespan and performance. The focus is on developing efficient techniques based on Genetic Algorithms (GAs) and machine learning (ML) to optimize battery pack operation. This study uses a proposed GA as a global search engine for optimal control parameters, while integrating Support Vector Machine (SVM) to improve the prediction accuracy of the battery state affected by these parameters. Furthermore, the Deep Reinforcement Learning (DRL) agent is trained in a physics-based simulation environment, such as PyBaMM, directly learning physics-informed, dynamic charging current profiles, unlike traditional DRL studies.The research explores various control parameters, including charging/discharging rates, current profiles, and temperature management, to minimize degradation and maximize energy efficiency. This approach effectively searches the vast solution space to identify optimal control strategies that balance immediate energy demands with long-term battery health. Simulation results demonstrate the effectiveness of the proposed GA-based optimization framework in achieving significant improvements in battery pack lifespan, energy efficiency, and overall performance compared to conventional control methods.
dc.description.sponsorshipNational Research Foundation of Korea (NRF) - Korea government (MSIT) [RS-2023-00218176]; Soonchunhyang University Research Fund; Princess Nourah bint Abdulrahyman University, Riyadh, Saudi Arabia [PNURSP2025R440]
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00218176) and the Soonchunhyang University Research Fund. This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R440) , Princess Nourah bint Abdulrahyman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1016/j.egyr.2025.109011
dc.identifier.issn2352-4847
dc.identifier.scopus2-s2.0-105026856721
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.egyr.2025.109011
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5903
dc.identifier.volume15
dc.identifier.wosWOS:001662245400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofEnergy Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectBattery Pack Design
dc.subjectGenetic Algorithm
dc.subjectCharging And Discharging
dc.subjectArtificial Intelligence
dc.titleOptimal control strategy to charging and discharging techniques for electric vehicle battery pack optimization based on genetic algorithm and machine learning
dc.typeArticle

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