Abstract
To solve the energy management issues of supercapacitor-assisted hybrid energy storage parallel hybrid electric vehicle (HEV), this paper proposes a dual optimization scheme integrating Particle Swarm Optimization (PSO)-optimized fuzzy control and Haar wavelet transform. Firstly, a torque distribution control approach based on fuzzy logic with PSO optimization is designed, which takes the demand torque difference and battery State of Charge (SOC) as inputs and uses the engine torque coefficient as output to enhance power efficiency, to keep the engine operating in the peak efficiency zone. Secondly, a scheme for power splitting utilizing Haar wavelet transform is employed to decompose the required power of hybrid energy storage system (HESS), low-frequency elements are directed to the battery, and high-frequency elements are directed to the supercapacitor, to achieve “peak-shaving and valley-filling” and reduce current impact on the battery. Finally, verification via Advisor and MATLAB simulation platforms under typical Chinese urban driving cycles shows that the optimized scheme aggregates engine operating points in the high-efficiency zone, improves the capability of the hybrid energy storage setup, and significantly enhances vehicle fuel economy while ensuring dynamic performance. This provides a solution with both real-time and optimization capabilities for HEV energy management.
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