Multi-input and multi-output neural model of the mechanical nonlinear behaviour of a PEM fuel cell system

A fuel cell system model is necessary to prepare and analyse vibration tests. However, in the literature, the mechanical aspect of the fuel cell systems is neglected. In this paper, a neural network modelling approach for the mechanical nonlinear behaviour of a proton exchange membrane (PEM) fuel cell system is proposed. An experimental set is designed for this purpose: a fuel cell system in operation is subjected to random and swept-sine excitations on a vibrating platform in three axes directions. Its mechanical response is measured with three-dimensional accelerometers. The raw experimental data are exploited to create a multi-input and multi-output (MIMO) model using a multi-layer perceptron neural network combined with a time regression input vector. The model is trained and tested. Results from the analysis show good prediction accuracy. This approach is promising because it can be extended to further complex applications. In the future, the mechanical fuel cell system controller will be implemented on a real-time system that provides an environment to analyse the performance and optimize mechanical parameters design of the PEM fuel system and its auxiliaries.