PEM fuel cells model parameter identification based on a new improved fluid search optimization algorithm

Abstract

Model-identification and parameter extraction of the proton exchange membrane fuel cell (PEMFC) is a well-defined procedure for improving the PEMFC efficiency for designing and control purposes. This paper presents a new version of the improved fluid search optimization algorithm for optimal parameter identification of the undetermined parameters of the PEMFCs. The total of square deviations between the experimentally measured values and the optimal achieved values from the algorithm is considered the cost function. Two empirical PEMFC models including BCS 500-W and NedStack PS6 are employed and analyzed to present the capability of the proposed procedure under different conditions. Simulation results are compared with different optimizers under the same conditions to demonstrate the system efficiency. The final results showed that the proposed chaos-based fluid search optimization algorithm is successfully used to extract the parameters of a PEMFC model precisely.