Real-Time Battery Thermal Management for Electric Vehicles Based on Deep Reinforcement Learning

With the rapid developments of electric vehicles (EVs) in recent years, it is desirable to improve the energy efficiency to prolong the limited life of battery and extend the cruising range of EVs. In real EVs, the battery thermal management system is installed to cool the battery and maintain the expected high power output. In this article, we propose a novel energy management strategy based on deep reinforcement learning (DRL) considering battery thermal effects on energy efficiency. The main idea is to formulate energy management as an optimization problem, further extract the state sequence features of the vehicle via gated recurrent unit (GRU), and finally, propose a double deep $Q$ network (double DQN)-based algorithm to obtain the optimal strategy. Comparisons of our double DQN algorithm and existent fuzzy control, as well as two other conventional reinforcement learning (RL) algorithms, are conducted under New European Driving Cycle, FTP-75, HWFET, and US06 cycles, and the results demonstrate that the proposed algorithm achieves an energy reduction of more than 6.7% during aggressive driving.