Optimization Design of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

To address the limitations of traditional energy management strategies in fuel cell hybrid power systems—specifically their difficulty in simultaneously accommodating dynamic driving condition adaptability, hydrogen fuel economy, and energy storage system stability—this study proposes a power distribution optimization strategy based on Deep Deterministic Policy Gradient (DDPG). The strategy targets a hybrid powertrain architecture dominated by a fuel cell (FC) and assisted by a lithium battery and a supercapacitor. By constructing a multi-dimensional state space that integrates vehicle speed, acceleration, the state of charge (SOC) of the energy storage system, and load power demand, a multi-objective reward function encompassing hydrogen consumption, SOC deviation, system efficiency, and power fluctuation is designed to achieve dynamic power allocation in a continuous action space. Simulation studies are conducted under three typical driving cycles—WLTP, CLTC-P, and UDDS—with comparative evaluations against the conventional Equivalent Consumption Minimization Strategy (ECMS) and Deep Q-Network (DQN)-based strategies. The results demonstrate that the DDPG-based strategy reduces hydrogen consumption to 607.1 g/100 km, 580.2 g/100 km, and 560.0 g/100 km under the three driving cycles, respectively, achieving a maximum reduction of 28% compared with ECMS. The average system efficiency increases to 64–66%, representing an improvement of 38.9%, while the operating proportion of the fuel cell within the high-efficiency region (40–80% load) increases by 15%. In addition, the strategy exploits the high-frequency response capability of the supercapacitor to smooth instantaneous power fluctuations, effectively reducing the inefficient start–stop events of the fuel cell. Although the SOC fluctuation range of the lithium battery increases by 32.5% compared with ECMS, a dynamic balance between energy efficiency and battery lifespan can be achieved through optimized weighting of the SOC deviation penalty term in the reward function. Overall, this study provides a solution with both theoretical significance and engineering feasibility for global energy optimization of fuel cell–energy storage systems under complex driving conditions.