Notice of Violation of IEEE Publication Principles: A Q-Newton Method Neural Network Model for PEM Fuel Cells

Abstract

Proton exchange membranes are one of the most promising fuel cell technologies for transportation and residential applications. Considering these two aims applications, a simulation of the whole fuel cell system is a major milestone. This would lead to the possibility of optimizing the complete system. In a fuel cell system, there is a strong relationship between available electrical power and actual operating conditions: gas conditioning, membrane hydration state, temperature, current set point. . . Thus, a minimal behavioural model of a fuel cell system able to evaluate the output variables and their variations is highly interesting. Artificial neural networks (NN) are a very efficient tool to reach such an aim. In this paper, a proton exchange membrane fuel cell (PEMFC) neural network is proposed using a Quasi- Newton method. It is implemented on Matlab/Simulinkreg software. The uses experimental data found in literature as training specimens; on the condition the system is provided enough hydrogen. Considering the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic neural network of PEMFC according to the different cell temperatures and different anode and cathode pressures.