Energy Management Strategy for Optimal Charge Depletion of Plug-In FCHEV Based on Multiconstrained Deep Reinforcement Learning
Haocong Wang, Xiaomin Wang, Zhumu Fu
Abstract
The energy management strategy (EMS) of plug-in fuel cell hybrid electric vehicles (P-FCHEV) is studied in this paper. Deep reinforcement learning (DRL) is a data-driven method that plays a crucial role in improving battery SoC maintenance, and fuel economy and extending fuel cell lifespan. However, existing methods struggle to balance optimal charge depletion and energy savings. This paper proposes a multi-constrained 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 multi-objective 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 fuel cell 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.