Efficient Deep Reinforcement Learning‐Based Energy Management Strategy for Hybrid Electric Vehicles

An efficient deep reinforcement learning (DRL) energy management strategy for parallel hybrid electric vehicles is proposed in this paper. Firstly, a comprehensive model of the vehicle's powertrain system is established, and the energy management problem is briefly described. Subsequently, an efficient learning framework based on the deep Q‐network (DQN) algorithm is constructed. The framework additionally incorporates a thermostat‐based rule‐aiding system to direct the model's training, accelerate the rate of training, and enhance the model's optimality and training rate in conjunction with enhanced priority experience replay, avoiding cold‐start in RL. In the simulation, the proposed model is compared with methods based on DQN and rule‐DQN. The results demonstrate that the newly developed DRL framework achieves higher training efficiency and optimality. Furthermore, this research examines how changes in the initial values of rule adoption rate and exploration rate influence the control performance of the model. Finally, the model, trained using a 520‐second driving cycle, is evaluated on the worldwide light‐duty test cycle (WLTC) driving cycle, validating its high adaptability.