Optimal Sizing and Energy Management for Fuel Cell Electric Vehicles with 3D-ordered MEAs: A Pareto Frontier Study

Fuel cell electric vehicles (FCEVs) are zero-emission but face cost and power density challenges. To mitigate these limitations, a novel 3D-ordered nano-structured self-supporting membrane electrode assembly (MEA) has been developed. This paper investigates the optimal component sizing of the battery and fuel cell in FCEVs equipped with 3D-ordered MEAs, integrating the energy management. To explore the trade-offs between component cost, operational cost and fuel cell degradation, the sizing and energy management problem is formulated into a multi-objective optimisation problem. A Pareto frontier (PF) study is conducted using the decomposed multi-objective evolutionary algorithm (MOEA/D) for a more diverse distribution of feasible solutions. The modular design of fuel cells is derived from a scaled and stressed experiment. After executing MOEA/D across the three aggressive driving cycles, power source configurations are selected from the corresponding PFs based on objective trade-offs, ensuring robustness of the overall system. The optimisation performance of the MOEA/D is compared with that of the multi-objective Particle Swarm Optimisation. In addition, the selected powertrain configurations are evaluated and compared through standard and realworld driving cycles in a simulation environment. This paper also performs a sensitivity analysis to reveal the influence of diverse component unit costs and hydrogen price. The results indicate that the mediumsized configuration, consisting of a 63.31 kW fuel cell stack and a 52.15 kWh battery pack, delivers the best overall performance. It achieves a 26.71% reduction in component cost and up to 12.76% savings in hydrogen consumption across various driving conditions. These findings provide valuable insights into the design and optimisation of fuel cell systems for FCEVs.