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Abstract: Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. predictive control of PEMFC based on support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO receding optimization applied to the PEMFC predictive control yielded good performance.

[1] Camacho, E.F., Bordon, C., 1999. Model Predictive Control. Springer, Berlin; New York.

[2] Freire, T.J.P., Gonzalez, E.R., 2001. Effect of membrane characteristics and humidification conditions on the impedance response of polymer electrolyte fuel cells. Journal of Electroanalytical Chemistry, 503(1-2):57-68.

[3] Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Network, IV:1942-1948.

[4] Li, G., Wu, M.S., Yu, D.T., 2004. Prediction control algorithm and application of output power of fuel cells. Chinese Journal of Power Source, 28(6):348-350 (in Chinese).

[5] Müller, K.R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V., 1999. Predicting Time Series with Support Vector Machines. Advances in Kernel Methods−Support Vector Learning. MIT Press, Cambridge, MA, p.243-254.

[6] Onnen, C., BabuSka, R., Kaymak, U., Sousa, J.M., Verbruggen, H.B., Isermann, R., 1997. Genetic algorithms for optimization in predictive control. Control Eng. Practice, 5(10):1363-1372.

[7] Rowe, A., Li., X.G., 2001. Mathematical modelling of Proton Exchange Membrane Fuel Cells. Journal of Power Sources, 102(1-2):82-96.

[8] Shi, Y., Eberhart, R., 1999. Empirical Study of Particle Swarm Optimization. Proceedings of Congress on Evolutionary Computation, p.1945-1950.

[9] Sridhar, P., Perumal, R.K., Rajalakshmi, N., Raja, M., Dhathathreyan, K.S., 2001. Humidification studies on polymer electrolyte membrane fuel cell. Journal of Power Sources, 101(1):72-78.

[10] Wang, D.C., Wang, M.H., 2004. On SVMR predictive control based on GA. Control and Design, 19(9):1067-1070.

[11] Xiao, J.M., 2004. Research on Neural Network Predictive Control Based on Particle Swarm Optimization. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, China (in Chinese).

[12] Yoshida, H., 1999. A Particle Swarm Optimization for Reactive Power and Voltage Control in Electric Power Systems Considering Voltage Security Assessment. IEEE SMC’99. Tokyo, Japan.

[13] Zhang, Y.J., Ouyang, M.G., Lu, Q.C., Luo, J.X., Li, X.H., 2004. A model predicting performance of proton exchange membrane fuel cell stack thermal systems. Applied Thermal Engineering, 24(4):501-513.