Guided Manta Ray Foraging Optimization for Accurate Parameter Extraction of PEM Fuel Cell

Abstract

Proton exchange membrane fuel cells (PEMFCs) are highly promising for producing clean and efficient energy. However, their complex electrochemical behavior, shaped by activation, ohmic, and concentration losses, requires accurate modeling and precise parameter estimation to ensure optimized performance. In this study, guided manta ray foraging optimization (GMANTA), an improved version of the original Manta Ray Foraging Optimization algorithm, is used to estimate key parameters in semiempirical PEMFC voltage models. Studies that simultaneously analyze multiple commercially available PEMFC stacks, such as the Horizon 500 W, BCW 500 W, and SR-12, are relatively scarce in the literature. Consequently, incorporating three experimentally obtained datasets in this study helps fill this gap and provides a more comprehensive and realistic validation framework for metaheuristic optimization algorithms. The proposed method offers a unique contribution by enabling highly accurate parameter identification across varying pressures and temperatures, using a small population size and few iterations. The approach reduces the error between simulated and measured voltage-current (V-I) data, ensuring that the models effectively capture the underlying physical phenomena. To assess the robustness and reliability of the method, GMANTA is compared with eight other well-established metaheuristic algorithms, and differences in error rates among these algorithms are analyzed statistically.