Deep reinforcement learning-based energy management strategies for energy-efficient driving of hybrid electric buses

Abstract

The fuel economy of hybrid electric vehicles is inextricably linked to the energy management strategy (EMS). In this study, a practicality-oriented learning-based EMS for a power-split hybrid electric bus (HEB) is presented, which combines the generative adversarial imitation learning (GAIL) and deep reinforcement learning (DRL). Considering the regular and fixed route of the HEB, optimal control samples that are not affected by the cost function can be obtained by the boundary-line dynamic programing (B-DP) method. On this basis, the samples are dynamically fitted using the GAIL method to inverse derive the reward function that can explain the B-DP control behavior. Concurrently, the proximal policy optimization DRL algorithm will regulate the energy distribution of the vehicle in real time and continuously optimize the energy management capability based on the reward feedback from GAIL. Finally, the feasibility and effectiveness of the proposed EMS is verified by simulation. The results show that the proposed strategy exhibits near-optimal control performance under both the China heavy-duty commercial vehicle cycle-bus and China city bus cycle.