Akademik Veri Yönetim Sistemi
FUEL CELLS, cilt.26, sa.2, ss.1-22, 2026 (SCI-Expanded, Scopus)
Proton exchange membrane fuel cells (PEMFCs) are highly promising for producing clean and efficient energy. However, theircomplex electrochemical behavior, shaped by activation, ohmic, and concentration losses, requires accurate modeling and preciseparameter estimation to ensure optimized performance. In this study, guided manta ray foraging optimization (GMANTA), animproved version of the original Manta Ray Foraging Optimization algorithm, is used to estimate key parameters in semiempiricalPEMFC voltage models. Studies that simultaneously analyze multiple commercially available PEMFC stacks, such as the Horizon500 W, BCW 500 W, and SR-12, are relatively scarce in the literature. Consequently, incorporating three experimentally obtaineddatasets in this study helps fill this gap and provides a more comprehensive and realistic validation framework for metaheuristicoptimization algorithms. The proposed method offers a unique contribution by enabling highly accurate parameter identificationacross varying pressures and temperatures, using a small population size and few iterations. The approach reduces the errorbetween simulated and measured voltage–current (V–I) data, ensuring that the models effectively capture the underlying physicalphenomena. To assess the robustness and reliability of the method, GMANTA is compared with eight other well-establishedmetaheuristic algorithms, and differences in error rates among these algorithms are analyzed statistically.