An Effective Model Parameter Estimation of ‎PEMFCs Using Modified GWO ‎Algorithms

Citation Author(s):
Minia University
Submitted by:
Ahmed Diab
Last updated:
Wed, 09/22/2021 - 05:19
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ABSTRACT This paper introduces the application of modified Grey Wolf Optimization (GWO) algorithms ‎‎for the sake of assessing ‎ unknown parameters of Proton Exchange Membrane Fuel Cells ‎‎(PEMFC) ‎models. Three ‎different GWO algorithms are applied: Conventional GWO, Improved ‎GWO (I-GWO) based ‎on dimension learning-based hunting (DLH), and ‎Selective ‎Opposition-based Grey Wolf Optimization ‎‎(SOGWO). These algorithms are applied to three ‎commercial ‎PEMFC stacks: BCS 500W-PEM, 500W-SR-‎‎12PEM and 250W-stack. The analyses ‎are executed considering several ‎operational circumstances. Sum ‎of square errors (SSEs) value ‎of the results based on parameters estimation and those ‎experimentally ‎tested are calculated. ‎The objective function is chosen as SSEs value. The results are compared with those ‎obtained ‎using ‎well-known methods in the literature to validate the effectiveness of the proposed ‎methods. ‎It is ‎noticeable that the simulated I/V curves momentously match the datasheet curves ‎for all the studied ‎‎cases. In addition, considering the accuracy of the solution and the ‎convergence speed, the PEMFC model ‎based on the I-GWO‎ algorithm ‎excels all other ‎algorithms. Based on the simulation results, the I-GWO ‎algorithm can improve the optimization efficiency to 99.96931463‎ for the 250 W stack while the ‎efficiency with GWO and ‎SOGWO are ‎‏ ‏‎98.8324404‎‎2 and 98.8429786‎‎2,‎ respectively for 250 W stack case ‎study.‎ ‎


Data of  three ‎commercial ‎PEMFC stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack.