A Study on Homogeneous Cooling Control of Multi-Channel Battery Modules Based on Reinforcement Learning

The performance, lifespan, and safety of lithium-ion battery packs are critically dependent on thermal management. A key challenge is the temperature inhomogeneity among cells in multi-channel cooling structures, which amplifies electrothermal instability, leading to localized degradation and performance deterioration. Conventional control methods, such as PID or rule-based controls, struggle to address the complex and nonlinear dynamics of the system and are limited in their ability to ensure temperature uniformity as they typically rely on average or maximum temperature values. This study proposes an active and homogeneous cooling control strategy for multi-channel battery modules using Deep Reinforcement Learning (DRL). The proposed controller takes real-time state inputs from the battery module including multi-point temperatures, voltage, and current to execute continuous actions that individually adjust the coolant flow rate in each channel. A multi-objective reward function was designed to consider four critical goals simultaneously: Safety, Uniformity, Energy Efficiency, and Control Stability, enabling the agent to learn a policy that achieves these complex objectives. The efficacy of the proposed method was validated using the Twin-Delayed Deep Deterministic policy gradient (TD3) algorithm in a high-fidelity simulation environment. The results demonstrate that the DRL controller significantly reduces the maximum temperature deviation compared to conventional methods and optimizes the energy consumed for cooling, thereby enhancing the overall performance and durability of the battery system. This research presents a successful application of a data-driven, intelligent control technique to the complex problem of battery thermal management and is expected to contribute to the advancement of thermal management technologies for high-performance electric vehicles and energy storage systems.