Deep reinforcement learning-based thermal management of battery subpack in electric vehicle

Abstract

With the increasing adoption of electric vehicles (EVs), effective battery thermal management is crucial to maintain safety and optimize performance. This study proposes a deep reinforcement learning (DRL)- based approach for battery thermal management, employing the Deep Deterministic Policy Gradient (DDPG) algorithm to regulate coolant flow rate and temperature. The objective is to maintain the battery temperature within the desirable operating range while minimizing energy consumption. A tailored reward function is formulated to consider the energy consumption minimization and thermal management. The effectiveness of the proposed DRL-based controller is evaluated by comparing the results with those of the zone model predictive controller (MPC). Simulation results demonstrate that the DRL-based controller achieves comparable performance to the MPC in battery temperature regulation, while reducing overall energy consumption and maintaining thermal stability. These findings highlight the potential of DRL-based control strategies as a viable alternative to MPC, offering improved energy efficiency for battery thermal management systems without requiring an explicit system model.