Energy Management Strategy for Optimal Charge Depletion of Plug-In FCHEV Based on Multiconstrained Deep Reinforcement Learning

The energy management strategy (EMS) of plug-in fuel cell hybrid electric vehicles (P-FCHEVs) is studied in this article. Deep reinforcement learning (DRL) is a data-driven method that plays a crucial role in improving battery state of charge (SoC) maintenance and fuel economy and extending fuel cell (FC) lifespan. However, existing methods struggle to balance optimal charge depletion and energy savings. This article proposes a multiconstrained DRL-based EMS. Specifically, an energy management hierarchical framework is built by merging twin delayed deep deterministic policy gradient (TD3) with adaptive fuzzy control filtering. Then, a high-performance exploration technique is designed to accelerate the search for the optimal action, and a multiobjective adaptive penalty function based on the equivalent consumption minimization is constructed to balance fuel economy, battery power maintenance, and energy degradation. Finally, the charge depletion method is developed based on the SoC change prediction. Simulation results show that compared with the baseline TD3, the proposed EMS can reduce the FC degradation rate by 10.49%, improve the SoC maintenance, and above 95% global optimum of the DP method. Furthermore, the average deviation from the terminal SoC is 1.92% under various scenarios, confirming that the proposed EMS can precisely achieve the optimal charge depletion.