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Adaptive neuro‐fuzzy inference system and artificial neural network modeling of proton exchange membrane fuel cells based on nanocomposite and recast Nafion membranes
Author(s) -
Amirinejad Mehdi,
TavajohiHasankiadeh Naser,
Madaeni Sayed Siavash,
Navarra Maria Assunta,
Rafiee Ezzat,
Scrosati Bruno
Publication year - 2011
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.1929
Subject(s) - proton exchange membrane fuel cell , adaptive neuro fuzzy inference system , nafion , membrane , nanocomposite , materials science , inference system , voltage , biological system , artificial neural network , chemical engineering , composite material , algorithm , electrode , engineering , computer science , chemistry , artificial intelligence , fuzzy logic , electrical engineering , fuzzy control system , electrochemistry , biochemistry , biology
SUMMARY 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 power–current behavior by the ANFIS for the MEA prepared by the recast Nafion and the nanocomposite membranes at the cell temperatures 50 °C to110°C are in high agreement with the experimental data. Predicted data by the ANFIS show that because of the property of Cs 2.5 H 0.5 PW 12 O 40 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 © 2011 John Wiley & Sons, Ltd.