Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method

Abstract

Building an accurate mathematical model is vital for the simulation, control, evaluation, management, and optimization of proton exchange membrane fuel cells (PEMFCs). Usually this work is processed through building a mathematical model based on empirical or semi-empirical equations firstly and then estimating the unknown model parameters using optimization technologies. In this study, a simple two stage eagle strategy based on JAYA algorithm and Nelder-Mead simplex algorithm is proposed for effectively estimating the unknown model parameters of PEMFCs. In the proposed strategy, JAYA algorithm is employed for the coarse global exploration, and Nelder–Mead simplex search algorithm is employed for the intensive local search. The effectiveness of the proposed strategy is verified through estimation experiments with 7 and 9 unknown parameters. Compared with the basic JAYA algorithm and four other newly reported excellent meta-heuristic algorithms including Grey Wolf Optimizer, Grasshopper Optimization Algorithm, Salp Swarm Optimizer, and Multi-Verse Optimizer, the proposed strategy possesses better performance in terms of accuracy, convergence speed, and stability, thus it is a promising approach for parameter estimation of PEMFC.