Optimal parameter estimation of PEM fuel cell using slime mould algorithm

Abstract

For the modeling and analysis of hydrogen fuel cells, reliable kinetic models are of utmost important. Proton exchange membrane fuel cell (PEMFC) is a complicated, multivariable device. Mathematical simulation of the PEMFC typically results in nonlinear parameter estimation issues, often involving more than one local minima. This paper proposes to solve the PEMFC model estimation issues with a slime mould (SM)-based optimization algorithm. Findings on several benchmark test functions show that, the SMA outperforms the other rest of the compared algorithms. Furthermore, an experimental finding indicates that the predictive outcomes of the model are in better alignment with the real experimental results. The SMA therefore is a valuable and effective method for estimating the parameters of PEMFC models and can be used for other specific fuel cell issues. After estimating the parameter of fuel cell, nonparametric test is obtained and from this test it is justified that the SMA is better than the rest of the compared algorithms.