For the modeling and analysis of hydrogen fuel cells, reliable kinetic models are of utmost important. Proton exchange membrane fuel cell (PEMFC) is a complicated, multivariable device. Mathematical simulation of the PEMFC typically results in nonlinear parameter estimation issues, often involving more than one local minima. This paper proposes to solve the PEMFC model estimation issues with a slime mould (SM)-based optimization algorithm.Findings on several benchmark test functions show that, the SMA outperforms the other rest of the compared algorithms. Furthermore, an experimental finding indicates that the predictive outcomes of the model are in better alignment with the real experimental results. The SMA therefore is a valuable and effective method for estimating the parameters of PEMFC models and can be used for other specific fuel cell issues. After estimating the parameter of fuel cell, nonparametric test is obtained and from this test it is justified that the SMA is better than the rest of the compared algorithms.

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Proton exchange membrane fuel cells (PEMFCs) have been known to be a feasible method for sustainable power generation to meet the ever-increasing electricity demand with no greenhouse gases emission. Thus, under varying operating circumstances, the PEMFCs have gained considerable importance as an alternative energy source. The optimal operation of PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PEMFC. Many researchers have applied evolutionary optimization approaches to estimate the PEMFCs parameters as the precise modeling of these cells are not possible. To optimize unknown parameters of PEMFCs, a metaheuristic algorithm is proposed, that is, chaotic mayfly algorithm (CMA) is being proposed in this manuscript. The model's statistical effects are more aligned with the actual experimental findings, according to an experimental finding. The results so obtained suggest that CMA variants are a valuable and efficient method of estimating the parameters of PEMFCs models. Even the nonparametric tests like the Friedman ranking test, Wilcoxon's rank-sum test, and Mood's median test suggest that the proposed algorithm shows better accuracy of the extracted parameters as compared to the other algorithms namely considered in this work.

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