Parameter identification of proton exchange membrane fuel cell using a Hybrid Big Bang-Big Crunch optimization

Abstract

It is important to have an accurate mathematical model of a proton exchange membrane fuel cell (PEMFC) for simulation and design a nalysis. Due to deficiency of manufacture information about the accurate value of parameters required for the modeling, it is necessary to identify these parameters. The proposed Hybrid Big Bang-Big Crunch (HBB-BC) optimization algorithm is a meta-heuristic optimization method in which PSO algorithm is used to make more useful Big Crunch phases in BB-BC optimization. In this work the Hybrid BB-BC optimization is proposed to identify the PEMFC parameters. The HBB-BC results are compared with GA, PSO and BB-BC results to study the usefulness of proposed optimization method and indicate the proposed method is an effective and reliable technique which can be applied to identify the model's parameters of PEMFC.