Meta-Heuristic Adaptive Equivalent Consumption Minimization of a Fuel Cell Vehicle Incorporating Fuzzy Inference and Particle Swarm Optimization

The fuel cell electric vehicle (FCEV) is a promising pathway for transport decarbonization. Managing the FCEV’s onboard energy is a challenging task that requires advanced control of multiple power sources. The equivalent consumption minimization strategy (ECMS) has shown effectiveness in the energy management of hybrid electric vehicles (HEVs), and its performance depends on the settings of the equivalent factor (EF). This article proposed a meta-heuristic fuzzy inference ECMS method (MA-ECMS) for FCEV to improve efficiency and robustness. The fuzzy inference system (FIS) of the MA-ECMS is optimized with a meta-heuristic algorithm, chaotic-enhanced particle swarm optimization (CAPSO), which optimizes the EF settings adaptively based on battery state-of-charge (SoC) and vehicle power demand. A PI-based SoC penalty regulator is added to the MA-ECMS to improve the SoC tracking capability. The optimality and robustness are evaluated for different driving cycles, and initial battery SoC states using the ECMS and A-ECMS as baselines. The real-time performance of MA-ECMS is verified on a processor-in-the-loop (PiL) testing platform. The results suggest that the MA-ECMS can achieve lower hydrogen consumption, better robustness, and fewer SoC tracking errors. Compared with the baselines, up to 13.13% hydrogen can be saved.