Particle Swarm Optimization and Fuzzy Logic Co-Optimization for Energy Efficiency Cooperative Energy Management Strategy of Hybrid Energy Storage Electric Vehicles

For hybrid energy storage systems requiring efficient energy management to achieve optimal power allocation between the power battery and supercapacitor, this study proposes an optimal energy management method integrating whole-process particle swarm optimization with fuzzy logic control, which simultaneously considers braking safety and energy efficiency optimization. First, a zonal braking force distribution strategy based on the I-curve, ECE regulations curve, and front wheel lockup curve is designed to maximize energy recovery while ensuring braking safety. On this basis, a whole-process “driving–braking” fuzzy logic control strategy for power distribution is constructed, aiming at maximizing braking energy recovery efficiency and minimizing energy consumption per 100 km. The parameters of the membership functions in the fuzzy controller are optimized using the particle swarm optimization algorithm to achieve global optimization of the control process. Finally, simulation validation of the optimization results demonstrates that, compared with traditional logic threshold control under NEDC conditions, the proposed strategy improves braking energy recovery efficiency by 10.32%, reduces energy consumption per 100 km by 0.96 kWh, and decreases the peak current of the power battery by 6.4%, thereby effectively enhancing vehicle economy and extending battery lifespan.