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Abstract: Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

[1] Arriaqada, J., Olausson, P., Selimoric, A., 2002. Artificial neural network simulator for SOFC performance prediction. Journal of Power Sources, 112:54-60.

[2] Baschuk, J.J., Li, X., 2000. Modelling of polymer electrolyte membrane fuel cells with variable degrees of water flooding. Journal of Power Sources, 86:181-196.

[3] Bender, G., Wilson, M.S., Zawodzinski, T.A., 2003. Further refinements in the segmented cell approach to diagnosing performance in polymer membrane fuel cells. Journal of Power Sources, 123:163-171.

[4] Berning, T., Djilali, N., 2003. Three-dimensional computational analysis of transport phenomena in a PEM fuel cell−A parametric study. Journal of Power Sources, 124:440-452.

[5] Berning, T., Lu, D.M., Djilali, N., 2002. Three-dimensional computational analysis of transport phenomena in a PEM fuel cell. Journal of Power Sources, 106:284-294.

[6] Chen, S., Bilings, S.A., 1992. Neural networks for nonlinear dynamic system modeling and identification. International Journal of Control, 56(2):319-346.

[7] Efe, M.O., Kaynak, O., 1999. Neuro-fuzzy Approaches for Identification and Control of Nonlinear Systems. Proceedings of the IEEE International Symposium on Industrial Electronics, p.TU2-TU11.

[8] Fowler, M.W., Mann, R.F., Amphlett, J.C., Peppley, B.A., Roberge, P.R., 2002. Incorporation of voltage degradation into a generalized steady state electrochemical model for a PEM fuel cell. Journal of Power Sources, 106:274-283.

[9] Kim, Y.H., Kim, S.S., 1999. An electrical modeling and fuzzy logic control of a fuel cell generation system. IEEE Transactions on Energy Conversion, 14(2):239-244.

[10] Rowe, A., Li, X., 2001. Mathematical modeling of proton exchange membrane fuel cells. Journal of Power sources, 102:82-96.

[11] Sakhare, A., Davari, A., 2003. Control of Stand Solid Oxide Fuel Cell Using Fuzzy Logic. Proceedings of the 35th Southeastern Symposium on System Theory, p.473-476.

[12] Shen, C., Cao, G.Y., 2002. Nonlinear modeling and adaptive fuzzy control of MCFC stack. Journal of Process Control, 12:831-839.

[13] Sun, T., Cao, G., Zhu, X., 2005. Nonlinear modeling of PEMFC based on neural networks identification. J Zhejiang Univ SCI, 6A(5):365-370.

[14] Wang, L.X., Jerry, M.M., 1992. Back Propagation Fuzzy System as Nonlinear Dynamic System Identifiers. IEEE International Conference on Fuzzy Systems, p.1409-1418.