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

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.