Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC

DOI: 10.4236/eng.2014.68044   PDF   HTML     3,143 Downloads   4,091 Views   Citations


Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.

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Grondin-Perez, B. , Roche, S. , Lebreton, C. , Benne, M. , Damour, C. and Kadjo, J. (2014) Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC. Engineering, 6, 418-426. doi: 10.4236/eng.2014.68044.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Görgun, H., Arcuk, M. and Barbir, F. (2006) An Algorithm for Estimation of Membrane Water Content in PEM Fuel Cells. Journal of Power Sources, 157, 389-394.
[2] Zhao, Y.H. (2013) Dynamic Modelling and Parametric Control for the Polymer Electrolyte. Journal of Power Sources, 232, 270-278.
[3] Panos, C., Kouramas, K.I., Georgiadis, M.C., Brandon, N. and Pistikopoulos, E.N. (2012) Modelling and Explicit Model Predictive Control for PEM Fuel Cell Systems. 20th European Symposium on Computer Aided Process Engineering, 15-25.
[4] Pukrushpan, J.T. (2003) Modeling and Control of Fuel Cell Systems and Fuel Processors. Ph.D. Thesis, University of Michigan, Ann Arbor.
[5] Lee, W.-Y., Park, G.-G., Yang, T.-H., Yoon, Y.-G. and Kim, C.-S. (2004) Empirical Modeling of Polymer Electrolyte Membrane Fuel Cell Performance Using Artificial Neural Networks. International Journal of Hydrogen Energy, 29, 961-966.
[6] Saengrung, A., Abtahi, A. and Zilouchian, A. (2007) Neural Network Model for a Commercial PEM Fuel Cell System. Journal of Power Sources, 172, 749-759.
[7] Rouss, V. and Charon, W. (2008) Multi-Input and Multi-Output Neural Model of the Mechanical Nonlinear Behaviour of a PEM Fuel Cell System. Journal of Power Sources, 175, 1-17.
[8] Cheddie, D. and Munroe, N. (2005) Review and Comparison of Approaches to Proton Exchange. Journal of Power Sources, 147, 72-84.
[9] Saadi, A., Becherif, M., Aboubou, A. and Ayad, M.Y. (2013) Comparison of Proton Exchange Membrane Fuel Cell Static Models. Renewable Energy, 56, 64-71.
[10] McKay, D.A., Ott, W.T. and Stefanopoulou, A.G. (2005) Modeling, Parameter Identification and Validation of Reactant and Water Dynamics for a Fuel Cell Stack. International Mechanical Engineering Congress and Exposition, 1177-1186.
[11] Amphlett, J.C., Baumert, R.M., Mann, R.F., Peppley, B.A. and Roberge, P.R. (1995) Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell I, Mechanistic Model Development. Journal of Electrochemical Society, 142, 1-8.
[12] Springer, T.E., Zawodzinski, T.A. and Gottesfeld, S. (1991) Polymer Electrolyte Fuel Cell Model. Journal of Electrochemical Society, 138, 2334-2342.
[13] Gong, W. and Cai, Z. (2013) Accelerating Parameter Identification of Proton Exchange Membrane Fuel Cell Model with Ranking-Based Differential Evolution. Energy, 59, 356-364.
[14] Mougenot, M. (2011) Elaboration et optimisation d’électrodes de piles PEMFC à très faible taux de platine par pulvérisation plasma. Ph.D. Thesis, Université d’Orléans, Orléans.
[15] Nørgaard, M., Ravn, O., Poulsen, N.K. and Hansen, L.K. (2000) Neural Networks for Modelling and Control of Dynamics Systems. Springer, Berlin.
[16] Stoppiglia, H., Dreyfus, G., Dubois, R. and Oussar, Y. (2003) Ranking a Random Feature for Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1399-1414.

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