Investigation of efficient optimization algorithms for durability-aware energy management for fuel cell hybrid electric vehicles across real-world traffic conditions

This paper investigates durability-aware energy management for a fuel-cell hybrid electric vehicle under highway, extra-urban, urban, and congested traffic conditions derived from real-world data. A fuzzy-logic energy management strategy is optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and evaluated over reconstructed Tehran, UDDS, and WLTP driving cycles. Compared with GA, PSO provides smoother fuel-cell power demand, improves reliability, and increases estimated fuel-cell lifetime by 2.5%, 3.3%, and 4.6% for the Tehran, UDDS, and WLTP cycles, respectively. It also reduces hydrogen consumption by 1.09%, 2.82%, and 1.59%. The proposed traffic-condition-based framework reveals variations in energy-management performance and component aging across different operating environments, providing a more realistic assessment than conventional cycle-based testing. Hardware-in-the-loop experiments using an ECU and a real-time FCHEV model confirm the controller’s robustness against hardware disturbances under diverse scenarios.