Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell

One of the essential phases in delivering a precise simulation of the fuel cell system behavior is estimating the model parameters. A novel identification approach based on the bald eagle search algorithm (BES) is suggested in this study to reliably extract the best PEM fuel cell (PEMFC) characteristics. BES is a modern metaheuristic algorithm with outstanding performance in various applications. Furthermore, this method delivers an accurate result because of its unique convergence mechanism. The paremeter identification of PEMFC is an optimization problem to minimize the sum square error (SSE) of the model and measurements. The unknown parameters are used as decision variables. To assess the effectiveness of the suggested method, two different types of PEMFCs were used: BCS 500 W and NedStack PS6. To approve the excellence of BES, a comparison was performed to those achieved using various optimizers, including gravitational search algorithm (GSA), grey wolf optimizer (GWO), differential evolution (DE), sine cosine algorithm (SCA), RSA encryption algorithm, and arithmetic optimization algorithm (AOA). The achieved results approve the advantage of BES in comparison with other algorithms. The SSE for NedStack PS6 employing the proposed method has been reduced to 2.07974 and 0.01136 for the BCS 500 W type. An accurate fitting for the measured datasets with the least deviation between the estimated and the experimental one.