Optimizing fuel economy and durability of hybrid fuel cell electric vehicles using deep reinforcement learning-based energy management systems

An effective energy management strategy (EMS) is crucial for the reliable operation of fuel cell hybrid electric vehicles (FCHEVs). This study proposes a power distribution optimization strategy for FCHEVs that leverages deep reinforcement learning (DRL) and Pontryagin's minimum principle (PMP). The DRL algorithm effectively balances fuel economy, battery durability, and fuel cell durability objectives. The degradation mechanisms of battery and fuel cell under extreme working conditions are considered in the PMP optimization. A comprehensive evaluation framework is established with degradation and energy consumption models to serve as a reward for deep reinforcement learning to balance fuel economy and power sources' lifetime. Simulation results show that the proposed EMS framework reduces FC degradation by 18.4% and battery degradation by 71.1% compared to traditional PMP-based EMS under the NEDC driving condition. Hardware-in-the-loop (HIL) testing demonstrates that the proposed EMS framework has the potential for real-time application, with an average relative error between experiment and simulation of approximately 0.0203. This research highlights the significance of the proposed EMS framework in ensuring the reliable operation of FCHEVs with enhanced performance and reduced cost.