Vibration Modeling of PEM Fuel Cell for Prediction of Cell Number Effects by Experimental Data

One of the most important challenges in increasing the performance, reliability and lifetime of fuel cells is the mechanical load effects that occur on real applications. Therefore, the vibration model of fuel cell that predicts the behavior of various fuel cell layouts is very useful. The fuel cell is made up of different adjacent layers that may have semi opposite mechanical properties. This special structure leads to occurrence of non-linear behavior of fuel cell under dynamic mechanical vibrations and so, a black box method is selected for modeling of its vibration behavior. In this study, the mechanical load experiments in various shape and axes were applied on five layouts of proposed fuel cell and the vibration of its body measure by some accelerometers. The NNARXM neural network is created and trained with the experimental data of three layouts of the fuel cell. Then, the prediction error of this neural network, validated with the two other experimental data of fuel cell layouts, by correlation coefficients and histogram of prediction errors. Neural network validation shows the well prediction of both untrained layout and suitable estimation for any desired layout.