In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

Under the banner of sustainable transportation, fuel cell hybrid electric vehicles (FC-HEVs) have become a matter of fascination in today's ever-growing need to save fossil fuels and reduce greenhouse gas emissions, addressing the 3Es challenges (efficiency, economy, and environment). However, one of the most pressing issues at the moment is determining how to incorporate the optimal sizing and energy management strategy (EMS) into FC-HEVs. In this regard, this article presents an integrated approach for optimal sizing as well as a new energy strategy for the design of FC-HEVs. To minimize the decision variables, including operating cost, fuel consumption, and component weight, an in-house optimization MATLAB code with a Multi-Objective Particle Swarm Optimization algorithm was developed. Four energy cases were tested under ARTEMIS and NEDC driving cycles for simulation, and their influence on the key decision variables was also investigated. The results show that using a battery pack with a supercapacitor reduces fuel consumption by 19% and 30.3% in both driving cycles, while decreasing the maximum output power of the fuel cell. It should be noted that hybrid energy sources have the potential to improve vehicle performance.