The driving motor of hybrid electric vehicles (HEVs) exhibits synchronous accumulation of energy and heat under time-varying driving scenarios. The independent processing of energy management and thermal management in the vehicle control unit overlooks the inherent energy-heat coupling relationship, thereby further limiting both fuel economy and thermal safety performance. A deep reinforcement learning (DRL) based integrated energy and motor thermal management strategy is proposed to address the multi-objective optimization between the dual subsystems. To address the sparse-reward issue caused by motor overheating and excessive battery discharge, an adaptive entropy regularization method based on the gradient of the value function is proposed to achieve a self-adaptive balance between exploration and exploitation. Comparative experiments show that the proposed method achieves an approximate global optimum, improving temperature-control performance, while the economic performance gap is only 8.10%, 2.24%, and 5.43% across various standard driving conditions. Test results under real-world prolonged high-load operating conditions further demonstrate the adaptability and robustness of the proposed method.
Zhang et al. (Sun,) studied this question.