Optimal Electrochemical Model Parameters Identification for Utility-Scale PEM Electrolyzers

Accurate modeling of proton exchange membrane (PEM) electrolyzers is paramount to precisely tracking their dynamic performance in response to temperature and pressure changes when they are utilized in large-scale power-to-gas applications. The exactitude of a PEM electrolyzer model is based essentially on the accuracy of the model parameters. As a result, this paper formulates the parameter identification of PEM as an optimization problem. The seven unknown parameters of the detailed PEM model are identified under various operating conditions using a flexible and effective artificial ecosystem-based optimizer (AEO) algorithm. To validate the efficiency and superiority of the proposed approach, the reported results are compared to those yielded by other electrolyzer parameter estimation models reported in the literature. The results reveal the ability of the identified parameters obtained by the proposed algorithm to achieve a closer matching between the measured and the estimated datasets that affirms the parameters' accuracy.