An intelligent parametric modeling and identification of a 5 kW ballard PEM fuel cell system based on dynamic recurrent networks with delayed context units

摘要

This article presents a dynamic simulation study for the modeling and identification of a 5 kW Proton Exchange Membrane (PEM) fuel cell system using intelligent ANN approach to get rid of the complexity involved in the analytical modeling as it is intricate with the highly non -linear dynamics such as electrochemical, thermodynamic and water-transport mecha-nisms. The proposed artificial networks for the prediction and identification of a highly non -linear fuel cell system performance are Radial Basis Function Network (RBFN), dynamic Elman Recurrent Network (ERN) and NARX Recurrent Network (NRN) that has delayed context unit. A comparative study is made between the performances of the proposed neural network models for identifying an optimal network structure and configuration based on network performance measures. The optimal NARX network ascertained having an appre-ciable learning and generalization ability is adopted for the prediction and identification of the fuel cell system for its static and dynamic behavior. The network prediction over the system behavior shows good agreement with the benchmark results acquired from a 5 kW-Ballard fuel cell system. The proposed optimal intelligent parametric model can facilitate in replacing a highly non-linear fuel cell system in the fuel cell related model developments that are adopted in several research sectors particularly in transportation applications. ? 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.