A speedy reinforcement learning (RL)-based energy management strategy (EMS) is proposed for fuel cell hybrid vehicles (FCHVs) in this research, which approaches near-optimal results with a fast convergence rate based on a pre-initialization framework and meanwhile possesses the ability to extend the fuel cell system (FCS) lifetime. In the pre-initialization framework, well-designed power distribution-related rules are used to pre-initialize the Q-table of the RL algorithm to expedite its optimization process. Driving cycles are modeled as Markov processes and the FCS power difference between adjacent moments is used to evaluate the impact on the FCS lifetime in this research. The proposed RL-based EMS is trained on three driving cycles and validated on another driving cycle. Simulation results demonstrate that the average fuel consumption difference between the proposed EMS and the EMS based on dynamic programming is 5.59% on the training driving cycles and the validation driving cycle. Additionally, the power fluctuation on the FCS is reduced by at least 13% using the proposed EMS compared to the conventional RL-based EMS which does not consider the FCS lifetime. This is significantly beneficial for improving the FCS lifetime. Furthermore, compared to the conventional RL-based EMS, the convergence speed of the proposed EMS is increased by 69% with the pre-initialization framework, which presents the potential for real-time applications.

本研究为燃料电池混合动力汽车 (FCHV) 提出了一种基于快速强化学习 (RL) 的能量管理策略 (EMS)，该策略基于预初始化框架以快速收敛速度接近最优结果，同时具有延长燃料电池系统 (FCS) 寿命的能力。在预初始化框架中，精心设计的配电相关规则用于预初始化 RL 算法的 Q 表，以加快其优化过程。在本研究中，驾驶循环被建模为马尔可夫过程，相邻时刻之间的 FCS 功率差异用于评估对 FCS 寿命的影响。建议的基于 RL 的 EMS 在三个驾驶循环上进行训练，并在另一个驾驶循环上进行验证。仿真结果表明，所提出的 EMS 与基于动态规划的 EMS 在训练驾驶循环和验证驾驶循环上的平均油耗差异为 5.59%。此外，与不考虑 FCS 寿命的传统基于 RL 的 EMS 相比，使用所提出的 EMS，FCS 上的功率波动至少减少了 13%。这对于提高 FCS 寿命非常有益。此外，与传统的基于 RL 的 EMS 相比，所提出的 EMS 的收敛速度通过预初始化框架提高了 69%，这为实时应用提供了潜力。与不考虑 FCS 寿命的基于 RL 的 EMS 相比，使用所提出的 EMS，FCS 上的功率波动至少减少了 13%。这对于提高 FCS 寿命非常有益。此外，与传统的基于 RL 的 EMS 相比，所提出的 EMS 的收敛速度通过预初始化框架提高了 69%，这为实时应用提供了潜力。与不考虑 FCS 寿命的基于 RL 的 EMS 相比，使用所提出的 EMS，FCS 上的功率波动至少减少了 13%。这对于提高 FCS 寿命非常有益。此外，与传统的基于 RL 的 EMS 相比，所提出的 EMS 的收敛速度通过预初始化框架提高了 69%，这为实时应用提供了潜力。