Parrot optimizer with multiple search strategies for parameters estimation of proton exchange membrane fuel cells model.

Proton exchange membrane fuel cells (PEMFCs) have emerged as a promising renewable energy source, generating significant interest in recent years due to their high efficiency, low operating temperature, and durability. Accurately estimating seven unknown parameters in the PEMFC electrochemical model is crucial for developing a more precise model, thereby improving the efficiency and performance of PEMFC systems. For this reason, a new optimization method inspired by parrots' (pyrrhura molinaes') behavior, named Parrot Optimizer (PO), is introduced here to address the problem of optimal parameter identification ([Formula: see text]) in PEMFC models. The estimate of these unknown characteristics is treated as a challenging, nonlinear optimization issue that has to be addressed with a strong optimization technique. The paper outlines two improvements to the basic PO algorithm: the first involves employing Opposition-based Learning to boost the search efficiency and refine candidate solution generation. The second integrates a Local Escaping Operator with PO to boost the exploration capabilities mitigate the risk of getting trapped in local optima, and enhance overall convergence behavior. The IPO was rigorously validated through the application of benchmark functions to assess its performance. Three distinct PEMFC stacks, the NedStackPS6, BCS Stack, and Ballard Mark V, have been used to empirically demonstrate the efficacy of this improved PO in optimizing the PEMFC model. Several recognized modeling approaches from the literature are used in a comprehensive examination to show the method's efficacy and dependability. For the NedStackPS6, BCS Stack, and Ballard Mark V units, the corresponding SQE values are 2.065816 V, 0.012457 V, and 0.814325 V. The IPO demonstrates a 12.87% improvement in the best measure and an 88.37% reduction in standard deviation compared to PO. The results show that the designed approach, including sensitivity analysis, correctly characterizes the PEMFC model. The improved PO effectively achieves the lowest SQE values and consistent convergence trajectories.