Transactions of the Korean hydrogen and new energy society (한국수소및신에너지학회논문집)

The Korean Hydrogen and New Energy Society (한국수소및신에너지학회)
권호 표지
DOI QR코드

DOI QR Code

Model-based Fault Detection Method for the Air Supply System of a Residential PEM Fuel Cell

가정용 고분자전해질 연료전지 공기공급시스템의 모델 기반 고장 검출 기술

  • WON, JINYEON (Department of Mechanical engineering, Yonsei University) ;
  • KIM, MINJIN (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • LEE, WON-YONG (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • CHOI, YOON-YOUNG (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • HONG, JONG SUP (Department of Mechanical engineering, Yonsei University) ;
  • OH, HWANYEONG (Fuel Cell Research Center, Korea Institute of Energy Research)
  • 원진연 (연세대학교 대학원 기계공학부) ;
  • 김민진 (한국에너지기술연구원 연료전지연구실) ;
  • 이원용 (한국에너지기술연구원 연료전지연구실) ;
  • 최윤영 (한국에너지기술연구원 연료전지연구실) ;
  • 홍종섭 (연세대학교 대학원 기계공학부) ;
  • 오환영 (한국에너지기술연구원 연료전지연구실)
  • Received : 2019.09.27
  • Accepted : 2019.12.30
  • Published : 2019.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, as the supply of residential polymer electrolyte membrane fuel cells (PEMFCs) increases, the durability and lifetime of the PEMFC system are becoming important. The related studies have been mainly focused on the durability and lifetime of materials while the research on the durability and maintenance of the system level is insufficient. In this paper, a model-based fault detection method is developed considering an air supply system that is dominant to the system performance and efficiency. A commercial 1 kW residential fuel cell system is built, and experiments are conducted under various operation loads and states (normal, 6 faults). From the experimental data, nominal models and residuals are generated. With the residual pattern obtained from real-time data, the detection and classification of various faults can be possible. The technical importance of this paper is to minimize extra sensor installation by using the empirical model rather than a complex mathematical model, and to decrease the number of models by using the applicable model at three loads. Finally, the model-based fault detection method for the air supply system of a PEMFC is established and is expected to be applicable to other subsystems.

Keywords

References

  1. Ministry of Trade, Industry and Energy and Korea Energy Corporation, "New & renewable energy white paper", 2018.
  2. F. Barbir, "PEM fuel cells: theory and practice. Academic Press Series series editor", Elsevier Academic Press, Netherlands, 2005.
  3. W. Y. Lee, M. J. Kim, H. Y. Oh, Y. J. Sohn, and S. G. Kim, "A Review on Prognostics of Polymer Electrolyte Fuel Cells", Trans. of the Korean Hydrogen and New Energy Soc., Vol. 29, No. 4, 2018, pp. 339-356, doi: https://doi.org/10.7316/KHNES.2018.29.4.339.
  4. W. Y. Lee, G. G. Park, Y. J. Sohn, S. G. Kim, and M. J. Kim, "Fault Detection and Diagnosis Methods for Polymer Electrolyte Fuel Cell System", Trans. of the Korean Hydrogen and New Energy Soc., Vol. 28, No. 3, 2017, pp. 252-272, doi: https://doi.org/10.7316/KHNES.2017.28.3.252.
  5. Z. Zheng, R. Petrone, M. C. Pera, D. Hissel, M. Becherif, C. Pianese, N. Y. Steiner, and M. Sorrentino, "A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems, international journal of hydrogen energy", Vol. 38, No. 21, 2013, pp. 8914-8926, doi: https://doi.org/10.1016/j.ijhydene.2013.04.007.
  6. A. Benmouna, M. Becherif, D. Depernet, F. Gustin, H. S. Ramadan, and S. Fukuhara, "Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system", International Journal of Hydrogen Energy, Vol. 42, No. 2, 2017, pp. 1534-1543, doi: https://doi.org/10.1016/j.ijhydene.2016.07.181.
  7. Z. LI, "Data-driven fault diagnosis for PEMFC systems", 2014. Retrieved from https://www.theses.fr/2014AIXM4335.
  8. L. Mao, L. Jackson, and B. Davies, "Investigation of PEMFC fault diagnosis with consideration of sensor reliability", International Journal of Hydrogen Energy, Vol. 43, No. 35, 2018, pp. 16941-16948, doi: https://doi.org/10.1016/j.ijhydene.2017.11.144.
  9. M. Ibrahim, U. Antoni, N. Y. Steiner, S. Jemei, C. Kokonendji, B. Ludwig, P. Mocoteguy, and D. Hissel, "Signal-Based Diagno stics by Wavelet Transform for Proton Exchange Membrane Fuel Cell", Energy Procedia, Vol. 74, 2015, pp. 1508-1516, doi: https://doi.org/10.1016/j.egypro.2015.07.708.
  10. E. Pahon and P. Mocoteguy, "A signal-based method for fast PEMFC diagnosis", Applied Energy, Vol. 165, 2016, pp. 748-758, doi: https://doi.org/10.1016/j.apenergy.2015.12.084.
  11. R. Isermann, "Model-based fault-detection and diagnosis - status and applications", Annual Reviews in Control, Vol. 29, No. 1, 2005, pp. 71-85, doi: https://doi.org/10.1016/j.arcontrol.2004.12.002.
  12. N. Y. Steiner, D. Candusso, D. Hissel, and P. Mocoteguy, "Model-based diagnosis for proton exchange membrane fuel cells", Mathematics and Computers in Simulation, Vol. 81, No. 2, 2010, pp. 158-170, doi: https://doi.org/10.1016/j.matcom.2010.02.006.
  13. R. Petrone, Z. Zheng, D. Hissel, M. C. Péra, C. Pianese, M. Sorrentino, M. Becherif, and N. Yousfi-Steiner, "A review on model-based diagnosis methodologies for PEMFCs", International Journal of Hydrogen Energy, Vol. 38, No. 17, 2013, pp. 7077-7091, doi: https://doi.org/10.1016/j.ijhydene.2013.03.106.
  14. T. Escobet , D. Feroldi, S. de Lira, V. Puig, J. Quevedo, J. Riera, and M. Serra, "Model-based fault diagnosis in PEM fuel cell systems", Jounal of Power Sources, Vol. 192, No. 1, pp. 216-223, doi: https://doi.org/10.1016/j.jpowsour.2008.12.014.
  15. Q. Yang, A. Aitouche and B. O. Bouamama, "Residuals Generation based on Nonlinear Analytical Redundancy Applied to Air Supply Sub-System of Fuel Cell", 18th Mediterranean Conference on Control and Automation, MED'10, 2010. Retrieved from https://pdfs.semanticscholar.org/a116/c96b73fc2ebbbb0b97871da4d74985967a26.pdf.
  16. A. Aitouche, Q. Yang, and B. O. Bouamama, "Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy", The European Physical Journal Applied Physics, Vol. 54, No. 2, 2011, pp. 23408, doi: https://doi.org/10.1051/epjap/2011100250.
  17. J. Lunze, "A method to get analytical redundancy relations for fault diagnosis", IFAC PapersOnLine Vol. 50, No. 1, 2017, pp. 1006-1012, doi: https://doi.org/10.1016/j.ifacol.2017.08.208.
  18. G. Nicchiotti, L. Fromaigeat, and L. Etienne, "Machine Learning Strategy for Fault Classification Using Only Nominal Data", European Conference of the Prognostic and Health Management Society, 2016. Retrieved from https://www.phmsociety.org/si tes/phmsociety.org/files/phm_submission/2016/phmec_16_010.pdf.
  19. I. Fagarasan and S. St. Iliescu, "Parity Equations for Fault Detection and Isolation", 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, 2008, doi: https://doi.org/10.1109/aqtr.2008.4588715.
  20. C. Damour, M. Benne, B. Grondin-Perez, M. Bessafi, D. Hissel, and J. P. Chabriat, "Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition", Journal of Power Sources, Vol. 299, 2015, pp. 596-603, doi: https://doi.org/10.1016/j.jpowsour.2015.09.041.