Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

Abstract

Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability.