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Too high stack temperature and insufficient reactant gas flow may lead to severe and irreversible damages in a real solid oxide fuel cell (SOFC) power system. Thus, fault monitoring and diagnosis technology is indispensable to improve the SOFC system reliability. A supervised self-organization map (SOM) model is proposed to diagnose the faults of the SOFC system in this paper. Using the supervised SOM model, the multidimensional testing data of the SOFC is mapped into a two-dimensional map, and the different region in the out map is represented for one fault mode. The method is evaluated using the data obtained from an SOFC mathematical model, and the results show that the supervised SOM analysis contributes on a very efficient way to the faults diagnosis of the SOFC system.

A multivariable hybrid experimental model of a solid oxide fuel cell stack is developed in this paper. The model consists of an improved radial basis function (RBF) neural network model and a pressure-incremental model. The improved RBF model is built to predict the stack voltage with different temperatures and current density. Likewise, the pressure-incremental model is constructed to predict the stack voltage under various hydrogen, oxygen, and water partial pressures. We combine the two models together and make a powerful hybrid multivariable model that can predict the voltage under any current density, temperature, hydrogen, oxygen, and water partial pressure. The validity and accuracy of modeling are tested by simulations, and the simulation results show that it is feasible to build the hybrid multivariable experimental model.