Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization

Abstract

Abstract Polymer electrolyte membrane fuel cells (PEMFC) are generally preferred in engineering applications due to their energy conversion efficiency, high power density, and low operating temperatures. In recent years, it has come to the fore in electric vehicles and unmanned aerial vehicle applications, which have critical and strategic importance. However, researchers use fuel cells in many different applied-theoretical studies. The models they use to increase the accuracy of these studies should be very similar to the real PEMFC. Therefore, in this paper, chaos embedded particle swarm optimization algorithm (CEPSO) and a new objective function are proposed for the first time in the literature to find the unknown parameters of PEMFC heaps more realistically. Three commercial types of PEMFCs stack namely 250\xa0W Stack, BCS-500\xa0W, and Nedstack PS6, which are commonly investigated in the literature, were numerically simulated to show the effectiveness of the proposes for parameter determining. The success of the suggestions is shown by the results obtained.