Adaptive deep reinforcement learning energy management for hybrid electric vehicles considering driving condition recognition

Adaptive energy management strategy (EMS) can adjust the underlying control scheme of hybrid electric vehicles (HEVs) according to different external conditions, thus maintaining high performance consistently. For this reason, this study designs a double-layer EMS for a power-split HEV, which combines deep reinforcement learning (DRL) with driving condition recognition (DCR) to achieve efficient operation of powertrain under different driving conditions. At the upper layer, the long-term dependencies in velocity sequences are captured through the sequential networks to enhance the accuracy of DCR. At the lower layer, multi-objective reward functions are formulated, and the optimal weight parameters are identified for different driving conditions. Furthermore, the deep deterministic policy gradient (DDPG) algorithm is employed to dynamically optimize power flow. The simulation experiments are conducted to verify the feasibility and the energy-saving effects of the proposed strategy. The results indicate that integrating variations in driving conditions into the energy allocation of HEVs can further improve the fuel economy and maintain battery health. Specifically, the method enhances the fuel economy by an average of 10.76 %, compared to a number of traditional benchmark strategies.