Applied Chemistry for Engineering (공업화학)

The Korean Society of Industrial and Engineering Chemistry (한국공업화학회)
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Study of Voltage Loss on Polymer Electrolyte Membrane Fuel Cell Using Empirical Equation

Empirical Equation을 이용한 고분자전해질 연료전지의 전압 손실에 대한 연구

  • Kim, Kiseok (School of Chemical Engineering University of Ulsan) ;
  • Goo, Youngmo (Korea Automotive Technology Institute) ;
  • Kim, Junbom (School of Chemical Engineering University of Ulsan)
  • 김기석 (울산대학교 화학공학부) ;
  • 구영모 (자동차부품연구원 전기구동시스템 연구센터) ;
  • 김준범 (울산대학교 화학공학부)
  • Received : 2018.10.12
  • Accepted : 2018.11.03
  • Published : 2018.12.10
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Abstract

The role of empirical equation to predict the performance of polymer electrolyte membrane fuel cell is important. The activation, ohmic and mass transfer losses were separated in a polarization curve, and the curve fitting according to each region was performed using Kim's model and Hao's model. Changes of each loss were compared according to operation variables of the temperature, pressure, oxygen concentration and membrane thickness. The existing model showed a good fitting convergence, but less fitting accuracy in the separated loss region. A new model using the convergence coefficient was suggested to improve the accuracy of performance prediction of fuel cells of which results were demonstrated.

고분자전해질 연료전지(PEMFC)의 성능을 예측할 수 있는 empirical equation의 역할이 중요하게 대두되고 있다. 본 연구에서는 polarization curve에서 activation loss, ohmic loss, mass transfer loss 영역을 분리하였고, 현재까지 개발된 model 중 Kim의 model과 Hao의 model을 선정하여 각 영역의 fitting을 시행하였다. 온도, 압력, 산소 농도 및 막 두께를 운전변수로 설정하여 조건 변화에 대한 각 loss의 변화를 비교하였다. 기존 model은 전반적으로 좋은 fitting 정확도를 보였지만, 분리된 loss 영역에서는 부정확한 fitting 결과를 보이기도 하였다. 연료전지 성능 예측의 정확도를 개선하기 위하여 converge coefficient를 도입한 새로운 model을 제안하였다. 본 연구에서 제안한 model을 연료전지 성능 예측에 적용한 경우에 신뢰도 평가에서 개선된 결과를 얻을 수 있었다.

Keywords

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Figure 1. Cells used in the experiment (a) FCT Cell (b) K Cell.

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Figure 2. Polarization curve at different temperatures.

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Figure 3. Loss Separation at different temperatures (a) activation loss of Hao’s model (b) activation loss of Kim’s model (c) ohmic loss of Hao’s model (d) ohmic loss of Kim’s model (e) mass transfer loss of Hao’s model (f) mass transfer loss of Kim’s model.

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Figure 4. Polarization curve at different pressures.

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Figure 5. Loss Separation at different pressures (a) activation loss of Hao’s model (b) activation loss of Kim’s model (c) ohmic loss of Hao’s model (d) ohmic loss of Kim’s model (e) mass transfer loss of Hao’s model (f) mass transfer loss of Kim’s model.

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Figure 6. Polarization curve of different oxygen concentrations.

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Figure 7. Loss separation at different oxygen concentrations (a) activation loss of Hao’s model (b) activation loss of Kim’s model (c) ohmic loss of Hao’s model (d) ohmic loss of Kim’s model (e) mass transfer loss of Hao’s model (f) mass transfer loss of Kim’s model.

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Figure 8. Polarization curve at different membrane thicknesses.

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Figure 9. Loss separation at different membrane thicknesses (a) activation loss of Hao’s model (b) activation loss of Kim’s model (c) ohmic loss of Hao’s model (d) ohmic loss of Kim’s model (e) mass transfer loss of Hao’s model (f) mass transfer loss of Kim’s model.

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Figure 10. Data fitting of Hao’s model (a) activation loss (b) ohmic loss (c) mass transfer loss.

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Figure 11. Percentage of current density parameter iloss.

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Figure 12. Data fitting results by changing converge coefficient c (a) activation loss (b) polarization curves.

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Figure 13. Data fitting results by Hao’s model and New model (a) activation loss (b) ohmic loss (c) mass transfer loss.

Table 1. Standard Operation Conditions of Fuel Cell Performance Test

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Table 2. Fitting Parameters and Losses at Different Temperatures (1,500 mA/cm2)

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Table 3. Fitting Parameters and Losses at Different Pressures (1,500 mA/cm2)

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Table 4. Fitting Parameters and Losses at Different Oxygen Concentrations (1,500 mA/cm2)

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Table 5. Fitting Parameters and Losses at Different Membrane Thicknesses (1,500 mA/cm2)

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References

  1. Y. Wang, K. S. Chen, J. Mishler, S. Cho, and X. C. Adroher, A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research, Appl. Energy, 88, 981-1007 (2010).
  2. T. Horde, P. Achard, and R. Metkemeijer, PEMFC application for aviation: Experimental and numerical study of sensitivity to altitude. Int. J. Hydrogen Energy, 37, 10818-10829 (2012). https://doi.org/10.1016/j.ijhydene.2012.04.085
  3. O. Z. Sharaf and M. F. Orhan, An overview of fuel cell technology: Fundamentals and applications, Renew. Sustain. Energy Rev., 32, 810-853 (2014). https://doi.org/10.1016/j.rser.2014.01.012
  4. T. Wilberforce, A. Alaswad, A. Palumbo, M. Dassisti, and A. G. Olabi, Advances in stationary and portable fuel cell applications, Int. J. Hydrogen Energy, 41, 16509-16522 (2016). https://doi.org/10.1016/j.ijhydene.2016.02.057
  5. T. H. Bradley, B. A. Moffitt, D. N. Mavris, and D. E. Parekh, Development and experimental characterization of a fuel cell powered aircraft, J. Power Sources, 171, 793-801 (2007). https://doi.org/10.1016/j.jpowsour.2007.06.215
  6. M. Ball and M. Weeda, The hydrogen economy - Vision or reality?, Int. J. Hydrogen Energy, 40, 7903-7919 (2015). https://doi.org/10.1016/j.ijhydene.2015.04.032
  7. J. Wishart, Z. Dong, and M. Secanell, Optimization of a PEM fuel cell system based on empirical data and a generalized electrochemical semi-empirical model, J. Power Sources, 161, 1041-1055 (2006). https://doi.org/10.1016/j.jpowsour.2006.05.056
  8. M. Secanell, J. Wishart, and P. Dobsona, Computational design and optimization of fuel cells and fuel cell systems: A review, J. Power Sources, 196, 3690-3704 (2011). https://doi.org/10.1016/j.jpowsour.2010.12.011
  9. S. Busquet, C. E. Hubert, J. Labbe, D. Mayer, and R. Metkemeijer, A new approach to empirical electrical modelling of a fuel cell, an electrolyser or a regenerative fuel cell, J. Power Sources, 134, 41-48 (2004). https://doi.org/10.1016/j.jpowsour.2004.02.018
  10. W. Lee, G. Park, T. Yang, Y. Yoon, and C. Kim, Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks, Int. J. Hydrogen Energy, 29, 961-966 (2004).
  11. J. H. Lee, T. R. Lalk, and A. J. Appleby, Modeling electrochemical performance in large scale proton exchange membrane fuel cell stacks, J. Power Sources, 70, 258-268 (1998). https://doi.org/10.1016/S0378-7753(97)02683-9
  12. P. Argyropoulos, K. Scott, A. K. Shukla, and C. Jackson, A semi-empirical model of the direct methanol fuel cell performance Part I. Model development and verification, J. Power Sources, 123, 190-199 (2003). https://doi.org/10.1016/S0378-7753(03)00558-5
  13. J. Lee and T. R. Lalk, Modeling fuel cell stack systems, J. Power Sources, 73, 229-241 (1997).
  14. K. Haraldsson and K. Wipke, Evaluating PEM fuel cell system models, J. Power Sources, 126, 88-97 (2004). https://doi.org/10.1016/j.jpowsour.2003.08.044
  15. J. Kim, S. Lee, and S. Srinivasan, Modeling of proton exchange membrane fuel cell performance with an empirical equation, J. Electrochem. Soc., 8, 2670-2674 (1995).
  16. G. Squadrito, G. Maggio, E. Passalacqua, F. Lufrano, and A. Patti, An empirical equation for polymer electrolyte fuel cell (PEFC) behaviour, J. Appl. Electrochem., 29, 1449-1455 (1999). https://doi.org/10.1023/A:1003890219394
  17. L. Pisani, G. Murgia, M. Valentini, and B. D'Aguanno, A new semi-empirical approach to performance curves of polymer electrolyte fuel cells, J. Power Sources, 108, 192-203 (2002). https://doi.org/10.1016/S0378-7753(02)00014-9
  18. S. D. Fraser and V. Hacker, An empirical fuel cell polarization curve fitting equation for small current densities and no-load operation, J. Appl. Electrochem., 38, 451-456 (2008). https://doi.org/10.1007/s10800-007-9458-2
  19. D. Hao, J. Shen, Y. Hou, Y. Zhou, and H. Wang, An improved empirical fuel cell polarization curve model based on review analysis, Int. J. Chem. Eng., 16, 1-10 (2016).
  20. X. Liang, G. Pan, L. Xu, and J. Wang, A modified decal method for preparing the membrane electrode assembly of proton exchange membrane fuel cells, Fuel, 139, 393-400 (2015). https://doi.org/10.1016/j.fuel.2014.09.022
  21. C. Jung, W. Kim, and S. Yi, Optimization of catalyst ink composition for the preparation of a membrane electrode assembly in a proton exchange membrane fuel cell using the decal transfer, Int. J. Hydrogen Energy, 37, 18446-18454 (2012). https://doi.org/10.1016/j.ijhydene.2012.09.013