Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network

Abstract In the present work, an Elman neural network (ENN) is suggested for parameter identification of proton exchange membrane fuel cell (PEMFC), which is optimized by a novel optimization method. Proposed optimization algorithm of this paper is a hybrid algorithm that combined the teaching–learning based optimization (TLBO) and differential evolution (DE) algorithm namely the TLBO-DE method. This paper combined these two effective methods to efficiently estimate the PEM fuel cell model parameters, and it’s validated by various validation functions. Performance of this proposed optimization method is confirmed by the obtained results from some well-known benchmarks. Moreover, a TLBO-DE based Elman neural network is implemented for solving the nonlinear parameter identification issue for the PEM fuel cell. According to the obtained results, this proposed network can forecast the stack voltage in various operational situations with high authenticity.

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