Model Parameters Estimation of a Proton Exchange Membrane Fuel Cell Using Improved Version of Archimedes Optimization Algorithm

Abstract

In the present study, a new technique was proposed for best variable identification for modeling of a Proton Exchange Membrane (PEM) fuel cell. The major idea is providing a new methodology to best variables estimation so that the absolute error (IAE) between the estimated data based on the proposed model and the real data has been minimized. The proposed method uses a new improved design of Archimedes Optimization Algorithm (IAOA) to this purpose. The designed model is then implemented on two practical case studies and the results are compared with some well-known methods. Final achievements indicate that the suggested method has a good efficiency and high data fitting with optimal parameters estimation.