Abstract
Modelling of Proton Exchange Membrane Fuel Cell (PEMFC) characteristics assumes importance in view of better understanding, analysis and design of high efficient fuel cell systems. Limited by its complexity, strongly coupled behaviour and multivariate characteristics; optimization techniques are attempted to model PEMFC characteristics. Influenced by convergence speed, computational efficiency, level of complexity, dependency on initial solution and ability to locate global optimum; recently evolved Flower Pollination Algorithm is utilized in this work for PEMFC modelling. This method is applied to derive unknown model parameters of fuel cells having different characteristics and rating. Further, to illustrate the superiority of the method, results obtained are compared with some of the recent works. Moreover, to showcase its efficiency; comprehensive comparison is made in terms of model parameter values, sum of squared error, individual absolute error and relative error values.
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