To improve the performance of fuel cells, the operating temperature of molten carbonate fuel cell (MCFC) stack should be controlled within a specified range. In this paper, with the RBF neural network’s ability of identifying complex nonlinear systems, a neural network identification model of MCFC stack is developed based on the sampled input-output data. Also, a novel online fuzzy control procedure for the temperature of MCFC stack is developed based on the fuzzy genetic algorithm (FGA). Parameters and rules of the fuzzy controller are optimized. With the neural network identification model, simulation of MCFC stack control is carried out. Validity of the model and the superior performance of the fuzzy controller are demonstrated.
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