Fuzzy Control-Based Optimization of GWMA for FCHEV Energy Management Strategy

To improve the energy distribution efficiency of fuel cell hybrid electric vehicles under dynamic operating conditions and extend fuel cell lifespan, this study proposes an energy management strategy based on the synergistic optimization of Gaussian-weighted moving average filtering and fuzzy control. Addressing the issue of frequent fuel cell switching caused by high-frequency power fluctuations, Gaussian filtering is employed to decompose the power demand signal by extracting low-frequency steady-state components and high-frequency transient components, and noise suppression is achieved while minimizing signal delay. Combined with the battery state of charge and fuel cell efficiency characteristics, a fuzzy controller is designed to adjust power distribution weights dynamically. Simulation results based on the Worldwide Harmonized Light Vehicles Test Cycle demonstrate that the proposed strategy effectively reduces hydrogen consumption compared to traditional wavelet decomposition-fuzzy control methods. The study proves that the lightweight nature of Gaussian filtering and the dynamic regulation capability of fuzzy control can synergistically optimize real-time performance and control accuracy. This strategy provides a theoretical foundation for the engineering application of simplified fuel cell hybrid electric vehicles' energy management systems.