Effective parameters' identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer

The aim of this paper is to develop an accurate model for the polymer electrolyte membrane fuel cell (PEMFC), that can precisely mimic and simulate the electrical characteristics of actual PEMFC stacks at different operating conditions. Models of PEMFC are empirical, multi-variables and have many non-linear terms that should be estimated accurately to ensure appropriate modeling. In this paper, a novel application based-on a nature-inspired metaheuristic optimization algorithm namely, the grey wolf optimizer (GWO) to identify PEMFC model parameters is addressed. The GWO relies on the principles of metaheuristic techniques, exploration and exploitation phases, in order to avoid sticking in local optima and getting better solution. The proposed GWO-based method is tested on five commercial PEMFCs. Many investigations and performance tests are made to prove the effectiveness of the algorithm to simulate the electrical behavior of those commercial PEMFC stacks based-on experimental data. In addition, parametric and non-parametric statistical tests are in place to assess the effectiveness of the proposed method. For more validation, PEMFC model based-on GWO is compared with other challenging algorithms published in literature, and very competitive results are reported.