Similar articles

Abstract: Control design is important for proton exchange membrane fuel cell (PEMFC) generator. This work researched the anode system of a 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network (DRNN) dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.

[1] Caux, S., Lachaize, J., Fadel, M., Shott, P., Nicod, L., 2005. Modelling and control of a fuel cell system and storage elements in transport applications. J. Process Control, 15(4):481-491.

[2] Dutta, S., Shimpalee, S., van Zee, J.W., 2001. Numerical prediction of mass-exchange between cathode and anode channels in a PEM fuel cell. Int. J. Heat Mass Transfer, 44(11):2029-2042.

[3] Ge, S.H., Yi, B.L., Xu, H.F., 1999. Model of water transport for proton-exchange membrane fuel cell (PEMFC). J. Chem. Ind. Eng., 50(1):39-48 (in Chinese).

[4] Larminie, J., Dicks, A., 2002. Fuel Cell Systems Explained. John Wiley & Sons, Chichester, England.

[5] Liu, H.J., Han, P., Yu, X.N., 2004. Load control system of thermal power sets based on self-tuning PID decoupling control with diagonal recurrent neural network. Power Eng., 24(6):809-818 (in Chinese).

[6] Moore, R.M., Hauer, K.H., Friedman, D., Cunningham, J., Badrinarayanan, P., Ramaswamy, S., Eggert, A., 2005. A dynamic simulation tool for hydrogen fuel cell vehicles. J. Power Sources, 141(2):272-285.

[7] Pukrushpan, J.T., Huei, P., Stefanopoulou, A.G., 2004. Control oriented modeling and analysis for automotive fuel cell systems. J. Dyn. Syst., Meas. Control, 126(1):14-25.

[8] Wang, J.G., Wang, Y.J., Wan, S.Y., 2004. PID parameter self-tuning and real-time control based on dynamic neural network. Syst. Eng. Electr., 26(6):777-810 (in Chinese).

[9] Wu, H.X., Shen, S.P., 2003. Basis of theory and applications on PID control. Control Eng. China, 10(1):37-42 (in Chinese).

[10] Yang, Q., Dang, X.J., 2004. Realization of a multivariable decoupling control system based on neural network two-degree-of-freedom PID. Computer Eng. Appl., 40(26):197-199 (in Chinese).

[11] Zhang, S.J., Cao, X.B., 2003. Single neuron adaptive PID control of spacecraft large angle attitude maneuvers. Aerospace Shanghai, 10(1):37-42 (in Chinese).