Comparison of various metaheuristic algorithms to find the optimal PEMFC modeling parameters

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Çetinkaya S. A., ÇOLPAN C. Ö., KORKMAZ S. A., ERGİNER K. E.

6th International Hydrogen Technologies Congress, Çanakkale, Turkey, 23 January 2022, pp.210-213

  • Publication Type: Conference Paper / Full Text
  • City: Çanakkale
  • Country: Turkey
  • Page Numbers: pp.210-213
  • Dokuz Eylül University Affiliated: Yes


It is essential to create a simple and robust model to estimate the performance of a fuel cell under different operating conditions accurately. In the literature, there are different methods used in the modelling of a proton exchange

membrane fuel cell (PEMFC). Among these methods, semi-empirical modelling approach is one of the most preferred methods to find the polarization curve. The most appealing features for employing this method are that

the required data can be collected from the manufacturer's datasheet and the metaheuristic algorithms are quite effective at determining the required values. In this study, the optimization of the parameters for three different

commercial fuel cells was carried out using a new algorithm, namely the improved-gray wolf (IGWO) algorithm; and the results were compared with those from the most extensively used metaheuristic algorithms in the literature,

which are particle swarm optimization algorithm (PSO), gray wolf optimization algorithm (GWO), and elitist continuous genetic algorithm (ECGA).