Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method

Di Miao, Wei Chen, Wei Zhao and Tekle Demsas

Energy, 2020, vol. 193, issue C

Abstract: Scheming and creating a precise model of fuel cell systems is essential to simulate, control, manage, and obtain the optimized parameters accurately in the case of Proton Exchange Membrane Fuel Cell. To get the optimal parameters of the Proton Exchange Membrane Fuel Cell, in this work, a new optimization method, which is called Hybrid Grey Wolf Optimizer, is presented. Hybrid Grey wolf optimizer is an innovative metaheuristic algorithm which is according to the behavior of the pack of the grey wolves. The basic Grey Wolf Optimizer is hybridized by including crossover and mutation operators in the optimization process for better efficiency in the evaluation of the primary parameters of Proton Exchange Membrane Fuel cells. In the process of searching, the adopted operators (crossover and mutation) increase the search potential capability and also evades the trapping in the local optima. The fulfilled analysis of some benchmarks showed that the Hybrid Grey Wolf Optimizer method works efficiently in all investigated criteria, such as convergence and exactness. Besides, Hybrid Grey Wolf Optimizer has been used to estimate the model of PEMFC, and then the achieved data shows a satisfactory efficiency of the proposed Hybrid Grey Wolf Optimizer.

Keywords: Proton exchange membrane fuel cell; Parameter identification; Hybrid grey wolf optimizer; Mutation and crossover operators; Sum of squared errors (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:193:y:2020:i:c:s0360544219323114

DOI: 10.1016/j.energy.2019.116616

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