Multi Input Single Output Neural Network Modelling and Identification of Proton Exchange Membrane Fuel Cell

Abstract

Due to the nonlinear and time variant characteristics of Proton Exchange Membrane Fuel Cell (PEMFC), its control is complicated. Thus, a suitable model is needed for PEMFC to gain higher performance stabilization and control. In this paper, the prediction of complicated behaviour of PEMFC is investigated using Artificial Neural Networks (ANN). The averaged cell voltage is regarded as the output; the current density and the cell temperature are considered as the inputs of neural networks. The experimental data are utilized for training and testing the networks. Multilayer perceptron (MLP) with one and two hidden layers and Radial Basis Function (RBF) networks are built, optimized, and tested in MATLAB environment. In order to study the efficiency of the neural network model, a comparison of the results is made through the Support Vector Machine (SVM) model. It is shown that neural model has better and more accurate prediction results than the SVM model of fuel cell, especially in low current region of fuel cell operation. In addition, the performance prediction of PEM fuel cell neural models with noisy data is carried out in order to check the effect of noise on the optimal structure of networks as well as the robustness of neural models

Keywords

• Proton Exchange Membrane Fuel Cell, Neural network, Identification

References

1. [1] D. Yu, S. Yuvarajan, “Electronic circuit model for proton exchange membrane fuel cells,” J. Power Sources, vol. 142, no. 1/2, pp. 238–242, Mar. 2005.
2. [2] D. M. Bernadi, M. W. Verbrugge, “A mathematical model of the solid-polymerelectrolyte fuel cell,” J. Electrochem. Soc., vol. 139, no. 9, pp. 2477–2491, Sep. 1992.
3. [3] S. Yerramalla, A. Davari, A. Feliachi, T. Biswas, “Modeling and simulation of the dynamic behavior of a ploymer electrolyte membrane fuel cell,” J. Power Sources, vol. 124, no. 1, pp. 104–113, Oct. 2003.
4. [4] J.C.Amphlett, R. F. Mann, B. A. Peppley, P.R.Roberge, A.Rodrigues, “A model predicting transient responses of proton exchange membrane fuel cells,” J. Power Sources, vol. 61, no. 1/2, pp. 183–188, Jul./Aug. 1996.
5. [5] T. F. Fuller, J. Newman, “Water and thermal management in solid polymer electrolyte fuel cells,” J. Electrochem. Soc., vol. 140, no. 5, pp. 1218–1225, May 1993.
6. [6] G. Maggio, V. Recupero, L. Pino, “Modeling polymer electrolyte fuel cells: An innovative approach,” J. Power Sources, vol. 101, no. 2, pp. 275–286, Oct. 2001.
7. [7] J. J. Baschuk, X. Li, “Modelling of polymer electrolyte membrane fuel cells with variable degrees of water flooding,” J. Power Sources, vol. 86, no. 1/2, pp. 181–196, Mar. 2000.
8. [8] A. Rowe, X. Li, “Mathematical modeling of proton exchange membrane fuel cells,” J. Power Sources, vol. 102, no. 1/2, pp. 82–96, Dec. 2001.

9. [9] S. Busquet, C. E. Hubert, J. Labb´e, D. Mayer, R.Metkemeijer, “Anew approach to empirical electrical modelling of a fuel cell, an electrolyser or a regenerative fuel cell,” J. Power Sources, vol. 134, no. 1, pp. 41–48, Jul. 2004.
10. [10] T. Berning, D. M. Lu, N. Djilali, “Three-dimensional computational analysis of transport phenomena in a PEM fuel cell,” J. Power Sources, vol. 106, no. 1/2, pp. 284–294, Apr. 2002.
11. [11] Saengrung, A., Abtahi, A., Zilouchian, A., “Neural Network Model for a Commercial PEM Fuel Cell System,” J. Power Sources, vol. 172, pp. 749-759, 2007.
12. [12] V. Rouss, W. Charon, Multi-input and multi-output neural model of the mechanical nonlinear behaviour of a PEM fuel cell system, 2008, vol. 175, pp.1-17.
13. [13] Paulo E. M. Almeida, Marcelo Godoy Simões,” Neural Optimal Control of PEM Fuel Cells with Parametric CMAC Networks”, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 41, NO. 1, ANUARY/FEBRUARY 2005.
14. [14] J. Lobatoa, P. Ca˜nizaresa, M. A. Rodrigoa, J. J. Linaresa, C.-G. Piuleacb, S. Curteanu, “The neural networks based modeling of a polybenzimidazole-based polymer electrolyte membrane fuel cell: Effect of temperature”, J. Power Sources, vol. 192, (2009), pp. 190– 194.
15. [15] Shaoduan Ou, Luke E.K. Achenie, “A hybrid neural network model for PEM fuel cells”, J. Power Sources, vol. 140, (2005), pp. 319–330.
16. [16] S. Jeme¨, D. Hissel, M.C. Péra, J.M. Kauffmann, “On-board fuel cell power supply modeling on the basis of neural network methodology,” J. Power Sources, vol. 124, (2003), pp. 479–486.
17. [17] Golbert J, Lewin DR. Model-based control of fuel cells: (1) Regulatory control. J. Power Sources, 2004;135:135–51.2002;12:831–9.
18. [18] M.Hatti, M.Tioursi, W.Nouibat, “Static Modeling by Neural Networks of a PEM Fuel Cell,”, IEEE Conference, pp. 2121-2126, 2006.
19. [19] L. Wei, Z. Xin-jian, M. Zhi-jun, “Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks”, Springer, J. Cent. South Univ. Technol. (2007)05−0690−06.
20. [20] M. Hatti, M. Tioursi,W. Nouibat, “A Q-Newton Method Neural Network Model for PEM Fuel Cells,” Industrial Informatics IEEE International Conference, 2006, pp. 1352– 1357.
21. [21] M.Hatti, M.Tioursi and W. Nouibat, “Neural Network Approach for Semi-Empirical Modelling of PEM Fuel-Cell”, IEEE ISIE 2006, July 9-12, 2006, Montreal, Quebec, Canada.
22. [22] Cirrincione, M., Pucci, M., Cirrincione, G., Simões, M.G., “A neural non-linear predictive control for PEM-FC,” J. Electrical System, pp. 1-18, 2005. [23] F. Laurencelle, R. Chahine, J. Hamelin, K. Agbossou, M. Fournier, T.K. Bose, A. Laperrire, “Characterization of a Ballard MK5-E proton exchange membrane fuel cell stack”, Fuel Cells 1 (1) (2001) 66–71.
23. [24] Fuel cell hand book, seven edition, EG&G Technical Services, Inc, November 2004.
24. [25] J. C. Amphlett, M. Baumertr, F. Mannr., “Performance modeling of the Ballard mark IV solid polymer electrolyte fuel cell”. J. Electrochem. Soc. vol. 142, no. 1, pp. 9-15, Jan. 1995.
25. [26] J. Kim, M. Lees, S. Srinivasan., “Modeling of proton exchange membrane fuel cell performance with an empirical equation”. J. Electrochem. Soc. vol. 142, no. 8, pp. 2670- 2674, Aug. 1995.
26. [27] Neural networks, a comprehensive foundation, second edition, Simon Haykin, 1999.
27. [28] Zhong, Z.D., Zhu, X.J., Cao, G.A., “Modeling a PEMFC by support vector machine,” J. Power Sources, vol. 160, pp. 293-298, 2006