Estimation of PEMFC optimal parameters based on an improved butterfly optimization algorithm

Abstract

This paper introduces a novel butterfly optimization algorithm, called the spiral search and dynamic crossover based butterfly optimization algorithm (SCBO), for parameter estimation in proton exchange membrane fuel cell (PEMFC) models. To enhance the global performance of the butterfly algorithm, a spin-search strategy is incorporated to expand its exploration range, while an adaptive factor is introduced to strike a balance between exploration and exploitation. Additionally, a dynamic crossover operation is integrated to enhance solution diversity, addressing the algorithm's tendency to converge to local optima. Extensive experimentation on benchmark functions in comparison with common optimization algorithms demonstrates that SCBO outperforms others in terms of convergence accuracy and speed. Finally, we employ SCBO for parameter identification in a PEMFC model, showcasing its superior results and its ability to capture the model's dynamics when compared to other algorithms.