Aging-aware optimal power management control and component sizing of a fuel cell hybrid electric vehicle powertrain

The current study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) equipped with a Battery-Ultracapacitor Hybrid Energy Storage System (HESS) using a multi-objective evolutionary algorithm called interactive adaptive-weight genetic algorithm (i-AWGA). The method aims to maximize the vehicle’s driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and HESS size. The energy management strategy involves fuzzy logic controllers to distribute the power demand between the fuel cell and HESS and between the battery and ultracapacitor pack. Under the combined standardized cycle in which the optimization was developed, the optimized FCHEV configuration achieved a driving range of 444 km, hydrogen consumption of 0.9009 kg/100 km. Furthermore, the optimal configuration demonstrated robustness in real-world driving conditions, exhibiting improved energy efficiency, driving autonomy, and power sources lifespan. A cost–benefit analysis was also carried out, in which the optimized configuration was evaluated in terms of cost of ownership, achieving 31.28 US$/km, which means the substantial reduction of up to 63.59% in the invested cost-to-autonomy ratio as compared against other electrified vehicle powertrain topologies. Overall, this study offers a promising approach for designing efficient, cost-effective, and environmentally friendly FCHEVs with improved performance and durability. [Display omitted] •Optimization of a fuel cell electric vehicle powertrain topology equipped with HESS.•Optimal configuration achieved 444 km autonomy and 0.9 kg/100 km hydrogen consumption.•Overall cost-to-autonomy ratio of 31.28 US$/km for the optimized FCHEV configuration.•Robust configurations evaluated under standard and real-world driving cycles.•Fuel cell and battery aging-aware power management control strategy.