Energy Management Strategy for Hydrogen Fuel Cell–Power Battery Hybrid Electric Vehicles Using Particle Swarm Optimization-Based Fuzzy Logic Control

Addressing the issue of energy optimization management in dynamic nonlinear operating conditions for the hydrogen fuel cell–power battery hybrid electric vehicle, this paper proposes a fuzzy logic control (FLC) energy management strategy, enhanced by a multiobjective sequential preference optimization method using particle swarm optimization (PSO). A multiobjective optimization metric is established, comprising hydrogen consumption, the tracking accuracy of the expected state of charge (SOC) of the power battery, and fuel cell degradation. The optimization process involved three sequential cycles, each prioritizing a different primary objective. Research indicates that an unoptimized fuzzy control strategy minimizes hydrogen consumption at the expense of SOC tracking precision and by permitting large, frequent fluctuations in fuel cell power output. However, the reliability and stability of hybrid systems cannot be adequately ensured. Consequently, the proposed PSO-fuzzy control strategy achieves synergistic improvements across all objectives. It reduces the SOC tracking error by 72% to 84.2% while simultaneously curbing the fuel cell degradation metric by 84.2% to 89.5%. Following optimization, the multiobjective optimization metrics decreased by 28.75%, 58.235%, and 63.77%, respectively. The combined PSO and FLC approach demonstrates strong adaptive capabilities and considerable potential for global optimization.