Adaptive Chaotic Aquila–Particle Swarm Optimization for accurate parameter estimation of solid oxide fuel cells

This study introduces the Adaptive Chaotic Aquila-Particle Swarm Optimization (ACA-PSO) algorithm to address the challenge of precise parameter identification in Solid Oxide Fuel Cells (SOFCs) under diverse operating conditions ranging from 1073 to 1273 K and pressures between 1 and 9 atm, where traditional optimization methods often falter in accuracy and efficiency. By integrating chaotic search with swarm intelligence, ACA-PSO enhances exploration and exploitation. It achieves the lowest Mean Squared Error values, for example, $$2.96\times {10}^{-6}$$ at 3 atm, along with the fastest run times such as 0.137415 s at 9 atm, and consistently secures the top Friedman rankings, most often ranked first, across ten benchmark cases when compared against nine algorithms including Particle Swarm Optimization and Grey Wolf Optimizer. The algorithm’s estimated parameters, such as an open-circuit voltage of 1.140942 V at 9 atm and a cathode exchange current density of 4.538461 mA/cm2, align with theoretical models like the Nernst and Butler-Volmer equations, thereby advancing the understanding of Solid Oxide Fuel Cells dynamics. Its practical efficiency further supports optimized cell design for sustainable energy systems, although refinement of ohmic resistance estimates and broader applicability to other fuel cell types remain areas for future exploration.