A transferable energy management strategy for hybrid electric vehicles via dueling deep deterministic policy gradient
Jingyi Xu, Zirui Li, Guodong Du, Qi Liu, Li Gao, Yanan Zhao
Abstract
Due to the high mileage and heavy load capabilities of hybrid electric vehicles (HEVs), energy management becomes crucial in improving energy efficiency. To avoid the over-dependence on the hard-crafted models, deep reinforcement learning (DRL) is utilized to learn more precise energy management strategies (EMSs), but cannot generalize well to different driving situations in most cases. When driving cycles are changed, the neural network needs to be retrained, which is a time-consuming and laborious task. A more efficient transferable way is to combine DRL algorithms with transfer learning, which can utilize the knowledge of the driving cycles in other new driving situations, leading to better initial performance and a faster training process to convergence. In this paper, we propose a novel transferable EMS by incorporating the DRL method and dueling network architecture for HEVs. Simulation results indicate that the proposed method can generalize well to new driving cycles, with comparably initial performance and faster convergence in the training process.