Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm

Zhe Sun, Dan Cao, Yawen Ling, Feng Xiang, Zhixin Sun and Fan Wu

Energy, 2021, vol. 216, issue C

Abstract: This paper firstly proposes a dynamic differential evolution algorithm (DDE-CGF) with a collective guiding factor to solve the problem of parameter identification and optimization of proton exchange membrane fuel cell (PEMFC) model. Inspired by the swarm intelligence scheme, a collective guidance factor is designed to accelerate the convergence speed without affecting the convergence accuracy. Moreover, a dynamic scaling factor and the dynamic crossover probability based on evolutionary mechanism are introduced to enhance the diversity of population as well as improve the global searching performance. Through testing eight benchmark functions, the DDE-CGF algorithm exhibits superior performance in both convergence accuracy and speed. Based on the excellent global performance, applying DDE-CGF algorithm to the parameter identification of the PEMFC model, and more accurate parameter values are obtained. Comparing with other algorithms, the result proves that the DDE-CGF algorithm could accurately estimate model parameters and the identified model could greatly describe the dynamical characteristic of the PEMFC model.

Keywords: Proton exchange membrane fuel cell (PEMFC); Parameter identification; Dynamic DE algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:216:y:2021:i:c:s0360544220321630

DOI: 10.1016/j.energy.2020.119056

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