Analysis and diagnosis of PEM fuel cell failure modes (flooding & drying) across the physical parameters of electrochemical impedance model: Using neural networks method

The objective of this work is to define and implement a method of artificial neural network to create an optimal impedance model of the proton exchange membrane fuel cell (PEMFC) which considers the electrochemistry and the mass transfer theory, which are used to analyze and diagnose PEM fuel cell failure modes (flooding & drying) across the physical parameters of the electrochemical impedance model. For this, we have based on the neural network technique for the calculation and estimation of various constituents parameters of this model. The Multi-Layer-Perceptron through back-propagation training algorithms shows satisfactory performance with the regard of parameter prediction. Furthermore, the neural network method applied to the impedance model is valid and valuable, which used to estimate the physical parameters of the electrochemical impedance model of the fuel cell (PEMFC) in both cases; flooding and drying of the fuel cell heart. The novelty of our work is summed up in the demonstration of the existence in a simple and uncomplicated way that allows the knowledge of the state of health of the PEMFC.