Modeling of a PEM Fuel Cell Stack using Partial Least Squares and Artificial Neural Networks

- 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;

Han, In-Su; Shin, Hyun Khil;

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

Cited by

1.

PCBs 독성 예측을 위한 주요 분자표현자 선택 기법 및 계산독성학 기반 QSAR 모델 개발,김동우;이승철;김민정;이은지;유창규;

2.

Pattern Recognition for Typification of Whiskies and Brandies in the Volatile Components using Gas Chromatographic Data,;;

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).

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).

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).

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).

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).

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).

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).

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).

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).

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).

13.

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

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).

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).

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).

17.

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

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).

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).

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).

22.

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

23.

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

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).

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).

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).