ABSTRACT: High efficiency and eco friendliness, proton exchange membrane fuel cells (PEMFCs) have become a good solution to cleaner energy solutions. However, due to the electrochemical complexity of PEMFCs and the limitations of existing optimization methods, accurately estimating PEMFC parameters to achieve optimal performance is still challenging. In this work, we propose a hybrid optimization algorithm, SCPSO, combining Particle Swarm Optimization with Mixed Mutant Slime Mold to improve precision, consistency, and computational efficiency in PEMFC parameter optimization. Six PEMFC types, BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W Stack were applied to SCPSO and compared with seven state-of-the-art algorithms, FLA, HFPSO, PSOLC, ESMA, LSMA, DETDO, and EGJO
