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

Abstract

The 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.