Proactive Energy Management for Fuel Cell Hybrid Vehicles: An Expert-Guided Slope-Aware Deep Reinforcement Learning Approach

Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer a promising path toward sustainable transportation, but their operational economy and component durability are highly dependent on the energy management strategy (EMS). Conventional deep reinforcement learning (DRL) approaches to EMS often suffer from training instability and are typically reactive, failing to leverage predictive information such as upcoming road topography. To overcome these limitations, this paper proposes a proactive, slope-aware EMS based on an expert-guided DRL framework. The methodology integrates a rule-based expert into a Soft Actor-Critic (SAC) algorithm via a hybrid imitation–reinforcement loss function and guided exploration, enhancing training stability. The strategy was validated on a high-fidelity FCHEV model incorporating component degradation. Results on the dynamic Worldwide Harmonized Light Vehicles Test Cycle (WLTC) show that the proposed slope-aware strategy (DRL-S) reduces the SOC-corrected overall operating cost by a substantial 14.45% compared to a conventional rule-based controller. An ablation study confirms that this gain is fundamentally attributed to the utilization of slope information. Microscopic analysis reveals that the agent learns a proactive policy, performing anticipatory energy buffering before hill climbs to mitigate powertrain stress. This study demonstrates that integrating predictive information via an expert-guided DRL framework successfully transforms the EMS from a reactive to a proactive paradigm, offering a robust pathway for developing more intelligent and economically efficient energy management systems.