Neural Network based Diagnostic of PEM Fuel Cell

This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural network model is used to enable monitoring the influence of the humidity content of the fuel cell membrane, through employing electrochemical impedance spectroscopy method (EIS analysis). This technique allows analyzing and diagnosing PEM fuel cell failure modes (flooding & drying). The benefit of this work is summed up in the demonstration of the existence in a simple way that helps to define the state of health of the PEMFC.