Adaptive neuro-fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes

In this study, a proton exchange membrane fuel cell (PEMFC) is modeled by multilayer perceptron neural network (MLPNN), RBF neural network (RBFNN), and adaptive neuro-fuzzy inference system (ANFIS). Experimental data are obtained on the basis of the fabricated membrane-electrode assembly (MEA) responses using prepared nanocomposite and recast Nafion membranes in the PEMFC. Four parameters including cell temperature, inlet gas temperature, current density, and inorganic additive percent are used as inputs, and the cell voltage is considered as the output. The results show that there is no considerable discrepancy between the RBFNN accuracy (R=0.99554) and the MLPNN accuracy (R=0.99609) for the performance prediction. The required time for developing the RBFNN model is significantly lower than the MLPNN model. A variety of ANFIS structure is explored to approximate the behavior of the system. The effect of cell and inlet gas temperatures on the PEMFC performance is investigated by the ANFIS developed model. Predicted polarization and powercurrent behavior by the ANFIS for the MEA prepared by the recast Nafion and the nanocomposite membranes at the cell temperatures 50 degrees C to110 degrees C are in high agreement with the experimental data. Predicted data by the ANFIS show that because of the property of Cs2.5H0.5PW12O40 additive for retaining water, much higher current density and power density at the same voltage are achieved for the nanocomposite membrane compared with the recast Nafion membrane in the PEMFC. Copyright (c) 2011 John Wiley & Sons, Ltd.