EMS for hydrogen fuel cell electric vehicles based on improved fuzzy control

Hydrogen fuel cell electric vehicles (HFCEVs) ensure both environmental cleanliness and an extended driving range. Control strategies based on optimization often achieve superior theoretical performance; however, they impose a high computational burden on entry-level passenger cars, where MCU resources are already prioritized for safety-critical functions like autonomous driving and AEB. Meanwhile, rule-based ones exhibit compromised adaptability and economic performance. Thus, this article focuses on improving the rule-based energy management strategies (EMSs) for the complex hybrid energy storage system (HESS) in the HFCEV. First, the powertrain model is developed, highlighting the selection and matching of key components, including the electric motor, hydrogen fuel cell and Li-ion battery. Subsequently, a Mamdani-type fuzzy control-based EMS improved via Dung Beetle Optimizer (DBO) is proposed. Fuzzy control serves as the framework to enhance efficiency performance, while DBO iteratively optimizes the parameters of membership functions. Finally, the proposed EMS is compared with multi-point control, power-tracking control, general fuzzy control and pattern-recognition-based fuzzy control. The results demonstrate that the proposed EMS significantly enhances economic performance under driving conditions featuring high-speed intervals and dynamic acceleration transitions. © 2025 The Authors.