Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm - Sun, Zhe; Cao, Dan; Ling, Yawen; Xiang, Feng; Sun, Zhixin; Wu, Fan (Energy (Oxford), 2021)
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. •We propose a dynamic differential evolution with collective guidance factor (DDE-CGF) algorithm.•The search efficiency is enhanced in high dimension search space.•The effectiveness is confirmed by testing benchmark functions.•The identification of the PEMFC model is conducted by adopting DDE-CGF.