基于 TD3-PER 的混合动力履带车辆能量管理

To optimize the fuel economy and traction battery performance of series hybrid electric tracked vehicle （SHETV），an energy management strategy （EMS） based on twin delayed deep deterministic policy gradient with prioritized experience replay （TD3-PER）is proposed. The TD3 algorithm can achieve more precise continuous control and prevent training from falling into over-assessment. The PER algorithm can accelerate strategy training and obtain higher optimization performance. Based on the model of the SHETV including longitudinal and lateral dynamics，the framework construction and simulation verification of EMS based on TD3-PER is completed. The results show that compared with deep deterministic policy gradient algorithm，the strategy proposed reduces the fuel consumption of SHETV by 3.89%，making its fuel economy reaching 95.05% of DP algorithm as a benchmark，with a better battery SOC retention ability and working condition adaptability.