A Two-Layer Fuzzy Control Energy Management Method for Hydrogen Fuel Cell Heavy-Duty Trucks Considering Fuel Cell Health State

Abstract

Hydrogen fuel cell trucks have enormous development potential in the pursuit of global carbon neutrality and sustainable development. However, their commercialization and mass production are facing challenges in various aspects, especially the durability problem of fuel cells. This paper is intended to set up a high-power hydrogen fuel cell system (FCS) model, considering the fuel cell degradation factors, and based on this, proposes a two-layer fuzzy energy management strategy (EMS) to optimize the life of fuel cell and the total energy consumption of the vehicle. The first control layer provides real-time energy distribution efficiently from multiple sources and thus allows flexibility in energy supply. The second layer regulates the dynamic adjustment of fuel cell output power with degradation of both fuel cells and batteries considered, to make the prolonging of system lifetime possible. In this respect, the equivalent hydrogen consumption, which incorporates fuel cell degradation, is used as the objective function. A genetic algorithm is employed to optimize the membership functions and rule weights involved in the fuzzy control system, considering the determination of the best energy management solution. Simulation results of a 115 kW fuel cell truck operating based on the China World Transient Vehicle Cycle (C-WTVC) conditions show that the proposed strategy reduces fuel cell degradation by 36.7% compared to that of traditional single-layer fuzzy control methods under extreme operating conditions. These results clearly depict gains in economic efficiency and sustainability of the system and hence indicate the advantages of the two-layer control strategy.

Key findings

• ▸The proposed method improves fuel cell durability and extends system lifetime.
• ▸The equivalent hydrogen consumption is used as the objective function to incorporate fuel cell degradation.
• ▸A genetic algorithm is employed to optimize membership functions and rule weights.

Keywords

Identifiers

DOI

10.4271/2025-01-7092

Journal

SAE Technical Paper Series

Year

2025