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Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks
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  • Journal title : Korean Chemical Engineering Research
  • Volume 53, Issue 2,  2015, pp.236-242
  • Publisher : The Korean Institute of Chemical Engineers
  • DOI : 10.9713/kcer.2015.53.2.236
 Title & Authors
Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks
Han, In-Su; Shin, Hyun Khil;
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 Abstract
We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.
 Keywords
Fuel Cell;Polymer Electrolyte Membrane;Back-box Modeling;Partial Least Squares;Artificial Neural Networks;
 Language
Korean
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 References
1.
Veziroglu, A. and Macario, R., "Fuel Cell Vehicles: State of the Art with Economic and Environmental Concerns," Int. J. Hydrog. Energy, 36, 25-43(2011). crossref(new window)

2.
Wang, C. Y., "Fundamental Models for Fuel Cell Engineering," Chem. Rev., 104, 4727-4766(2004). crossref(new window)

3.
Ding, Y., Bi, X. T. and Wilkinson, D. P., "Numerical Investigation of the Impact of Two-Phase Flow Maldistribution on PEM Fuel Cell Performance," Int. J. Hydrog. Energy, 39, 469-480(2014). crossref(new window)

4.
Han, I.-S., Lim, J., Jeong, J. and Shin, H. K., "Effect of Serpentine Flow-Field Designs on Performance of PEMFC Stacks for Micro-CHP Systems," Renew. Energy, 54, 180-188(2013). crossref(new window)

5.
Chung, H., Ha, T., Kim, H. and Han, C., "Simulation of PEM Fuel Cell with 2D Steady-State Model," Korean Chem. Eng. Res., 46, 915-921(2008).

6.
Jeong, J., Han, I.-S. and Shin, H. K., "Optimal Sizing of the Manifolds in a PEM Fuel Cell Stack using Three-Dimensional CFD Simulations," Trans. Korean Hydrogen & New Energy Soc., 24, 386-392 (2013). crossref(new window)

7.
Guo, N., Leu, M. C. and Koylu, U. O., "Network based Optimization Model for Pin-Type Flow Field of Polymer Electrolyte Membrane Fuel Cell," Int. J. Hydrog. Energy, 38, 6750-6761(2013). crossref(new window)

8.
Hou, Y., Yang, Z. and Wan, G., "An Improved Dynamic Voltage Model of PEM Fuel Cell Stack," Int. J. Hydrog. Energy, 35, 11154-11160 (2010). crossref(new window)

9.
Zhao, Y. and Pistikopoulos, E., "Dynamic Modeling and Parametric Control for the Polymer Electrolyte Membrane Fuel Cell System," J. Power Sources, 232, 270-278(2013). crossref(new window)

10.
Khadom, A. A., "Modeling of Corrosion Reaction Data in Inhibited Acid Environment using Regressions and Artificial Neural Networks," Korean J. Chem. Eng., 30, 2197-2204(2013). crossref(new window)

11.
Saengrung, A., Abtahi, A. and Zilouchian, A., "Neural Network Model for a Commercial PEM Fuel Cell System," J. Power Sources, 172, 749-759(2007). crossref(new window)

12.
Li, X., Cao, G. and Zhu, X., "Modeling and Control of PEMFC Based on Least Squares Support Vector Machines," Energy Conv. Manag., 47, 1032-1050(2006). crossref(new window)

13.
Zhong, Z., Zhu, X. and Cao, G., "Modeling a PEMFC by a Support Vector Machine," J. Power Sources, 160, 293-298(2006). crossref(new window)

14.
Petrone, R., Zheng, Z., Hissel, D., Pera, M. C., Pianese, C., Sorrentino, M., Becherif, M. and Yousfi-Steiner, N., "A Review on Modelbased Diagnosis Methodologies for PEMFCs," Int. J. Hydrog. Energy, 38, 7077-7091(2013). crossref(new window)

15.
Napoli, G., Ferraro, M., Sergi, F., Brunaccini, G. and Antonucci, V., "Data Driven Models for a PEM Fuel Cell Stack Performance Prediction," Int. J. Hydrog. Energy, 38, 11628-11638(2013). crossref(new window)

16.
Hua, J., Li, J., Ouyang, M., Lu, L. and Xu, L., "Proton Exchange Membrane Fuel Cell System Diagnosis based on the Multivariate Statistical Method," Int. J. Hydrog. Energy, 36, 9896-9905(2011). crossref(new window)

17.
Wold, S., Sjostrom, M. and Eriksson, L., "PLS-Regression: a Basic Tool of Chemometrics," Chemometrics Intell. Lab. Syst., 58, 109-130(2001). crossref(new window)

18.
Han, I.-S., Kim, M., Lee, C.-H., Cha, W., Ham, B.-K., Jeong, J.-H., Lee, H., Chung, C.-B. and Han, C., "Application of Partial Least Squares Methods to a Terephthalic Acid Manufacturing Process for Product Quality Control," Korean J. Chem. Eng., 20, 977-984(2003). crossref(new window)

19.
Han, I.-S., Han, C. and Chung, C.-B., "Melt Index Modeling with Support Vector Machines, Partial Least Squares, and Artificial Neural Networks," J. Appl. Polym. Sci., 95, 967-974(2004).

20.
Han, I.-S. and Han, C., "Modeling of Multistage Air-Compression Systems in Chemical Processes," Ind. Eng, Chem. Res., 42, 2209-2218(2003). crossref(new window)

21.
Min, K. G., Han, I.-S. and Han, C., "Iterative Error-based Nonlinear PLS Method for Nonlinear Chemical Process Modeling," J. Chem. Eng. Japan, 35, 613-625(2002). crossref(new window)

22.
Geladi, P. and Kowalski, B., "Partial Least-Squares Regression: a Tutorial," Anal. Chim. Acta, 185, 1-17(1986). crossref(new window)

23.
Kalogirou, S. A., "Artificial Neural Networks in Renewable Energy Systems Applications: a Review," Renew. Sust. Energ. Rev., 5, 373-401(2001). crossref(new window)

24.
Hagan, M. T., Demuth, H. B. and Beale, M., Neural Network Design, PWS Publishing, Boston, MA(1996).

25.
Hornik, K., Stinchcombe, M. and White, H., "Multilayer Feedforward Networks are Universal Approximatiors," Neural Networks, 2, 359-366(1989). crossref(new window)

26.
Han, I.-S., Jeong, J., Kho, B. K., Choi, C. H., Yu, S. and Shin, H. K., "Development of a 25 kW-Class PEM Fuel Cell System for the Propulsion of a Leisure Boat," Trans. Korean Hydrogen & New Energy Soc., 25, 271-279(2014). crossref(new window)

27.
Han, I.-S., Jeong, J. and Shin, H. K., "PEM Fuel-Cell Stack Design for Improved Fuel Utilization," Int. J. Hydrog. Energy, 38, 11996-12006(2013). crossref(new window)

28.
Andersen, C. M. and Bro, R., "Variable Selection in Regression - Tutorial," J. Chemometr., 24, 728-737(2010). crossref(new window)

29.
Chong, I.-G. and Jun, C.-H., "Performance of Some Variable Selection Methods When Multicollinearity is Present," Chemometrics Intell. Lab. Syst., 78, 103-112(2005). crossref(new window)