Model parameters estimation of a proton exchange membrane fuel cell using improved version of Archimedes optimization algorithm

Proton Exchange Membrane (PEM) fuel cell is one of the most popular fuel cells because of its higher efficiency among the other fuel cells. Because of the expensive materials in designing of this type of fuel cell, it should be first design and simulated in the best and optimum way to reduce the construction costs as much as possible. In the present study, a new model identification is proposed for optimal parameters identification of the PEM fuel cells. The major idea in this study is to provide a new optimal methodology to parameters estimation of the unknown variables in the PEM fuel cell model so that the absolute error (IAE) between the estimated data based on the proposed model and the real data has been minimized. The proposed method uses a new improved design of Archimedes Optimization Algorithm (IAOA) to this purpose. The designed model is then implemented on two practical case studies and the results are compared with some well-known methods. Final results shows that the proposed method with 0.10 and 0.14 error values for Nexa and NedStack PS6 models, respectively, provides the best solution among the other comparative methods. (C) 2021 The Authors. Published by Elsevier Ltd.