Electric Vehicle Battery Thermal Management under Extreme Fast Charging with Deep Reinforcement Learning

<div>The increasing importance of electric vehicles requires addressing challenges related to fast charging, safety, and battery range. Thermal management ensures safety, prolongs battery life, and enables extremely fast charging. In this regard, this article proposes a novel battery thermal management system (BTMS) optimization approach based on a model-free deep reinforcement learning (RL) for a battery pack of an electric vehicle under extreme fast-charging conditions considering the detailed dynamics of vehicle-level BTMS. The objective of the proposed approach seeks to minimize the battery degradation and power consumption of the underlying BTMS. In this respect, the dynamic equations of the thermal system model are constructed considering the air-conditioning refrigerant loop and indirect battery liquid cooling loop. Further, the proposed methodology is implemented on a battery pack, and the results are compared with those of model predictive control (MPC) and proportion–integral–derivative (PID) as representatives of optimal control and tracking control baseline strategies, respectively. It is shown that if a perfect BTMS model is at the disposal of MPC, MPC performance can be as good as that of the proposed RL. However, the proposed RL algorithm is 48 times faster than MPC during testing. The superiority of the proposed deep RL over MPC is attributed to its model-free nature. Lastly, the RL outperformance is shown over the PID controller in terms of both battery degradation and BTMS power consumption, where the former is improved by up to 1.05% whereas the latter is improved by up to 43.68%.</div>