A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation

This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters ϵ1, ϵ2, ϵ3, ϵ4, β, λ, Rc. The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R2) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm’s rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling.