Optimizing Battery Cooling with Reinforcement Learning: A Dynamic Control Strategy for Energy Storage Systems

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dc.contributor.authorAksoy, Necati
dc.contributor.authorCakil, Fatih
dc.contributor.authorOzden, Mustafa
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA -- MAY 23-24, 2025 -- Ankara, TURKIYE
dc.description.abstractThe growing reliance on energy storage systems (ESS) in residential, vehicular, and industrial applications necessitates efficient thermal management solutions to enhance performance and longevity. This study proposes a reinforcement learning (RL)-based cooling control system for optimizing the temperature regulation of battery banks. Unlike traditional rule-based or static cooling strategies, the proposed method dynamically adjusts coolant flow rates using an Expected SARSA agent, which learns an optimal control policy through interactions with a custom-designed environment model. The model accurately simulates thermal absorption and coolant flow dynamics, allowing for precise and adaptive cooling regulation. The performance of the RL agent was evaluated across different training durations, demonstrating that extended training significantly improves stability, reward consistency, and energy efficiency. Compared to conventional cooling strategies, the RL-based system ensures more adaptive, efficient, and reliable thermal management, making it a promising solution for next-generation energy storage applications.
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Inc,Ted University
dc.identifier.doi10.1109/ICHORA65333.2025.11017253
dc.identifier.isbn979-8-3315-1089-3
dc.identifier.isbn979-8-3315-1088-6
dc.identifier.issn2996-4385
dc.identifier.scopus2-s2.0-105008418849
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ICHORA65333.2025.11017253
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5902
dc.identifier.wosWOS:001533792800219
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2025 7Th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ichora
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectReinforcement learning
dc.subjectcooling control
dc.subjectenergy storage
dc.subjectbattery bank
dc.subjectenergy efficiency
dc.titleOptimizing Battery Cooling with Reinforcement Learning: A Dynamic Control Strategy for Energy Storage Systems
dc.typeConference Object

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