10.1007/s40095-021-00410-3

Analysis of the novel dynamic semiempirical model of proton exchange membrane fuel cell by incorporating ambient condition variations

  1. Electromechanics Department, Mohammadia School of Engineers, Rabat, MA
  2. National Superior School of Mines, Rabat, MA
  3. United Arab Emirates University, Al-Ain, AE
  4. Alumnus, Abasyn University Islamabad, Islamabad, PK

Published in Issue 2021-07-22

How to Cite

Abbou, A., El Hasnaoui, A., Khan, S. S., & Yamin, F. (2021). Analysis of the novel dynamic semiempirical model of proton exchange membrane fuel cell by incorporating ambient condition variations. International Journal of Energy and Environmental Engineering, 13(1 (March 2022). https://doi.org/10.1007/s40095-021-00410-3

Abstract

Abstract Proton exchange membrane fuel cell PEMFC is nonlinear source so modeling its output is essential. Also PEMFC is extremely prone to change in ambient conditions. Previously the complex modeling techniques have been used to model PEMFC voltage, but these technique are not good for online purposes. Hence a semiempirical approach has been suggested for PEMFC voltage prediction which uses the combination of theoretical and empirical equations. But most of the semiempirical models have lot of deficiencies which need to be corrected with the help of experimentation of different PEMFC systems at different conditions. In this research paper, a novel semiempirical model has been chosen which is less complex, dynamic and has the ability to diagnose fault as well. The model is only tested on two PEMFC stacks but never tested on single PEMFC. In this research, the model has been tested on single-cell PEMFC system and the parameters are optimized by using lightening search algorithm. The temperature and voltage model have been validated, and the new optimized parameters are recorded for different ambient conditions. The model discrepancies have been identified, and the new equations for parameters have been proposed which can be helpful in the making of generic model.

Keywords

  • Proton exchange membrane fuel cell,
  • Semiempirical modeling,
  • Voltage model,
  • Temperature model,
  • Lightening search algorithm,
  • Optimization

References

  1. Khan, S. S., Rafiq, M. A., Shareef, H., Sultan, M. K.: ”Parameter optimization of PEMFC model using backtracking search algorithm. In: 2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA), Al Ain, United Arab Emirates, 2018, pp. 323–326,
  2. https://doi.org/10.1109/ICREGA.2018.8337625.
  3. Giner-Sanz, J.J., Ortega, E.M., Pérez-Herranz, V.: Mechanistic equivalent circuit modelling of a commercial polymer electrolyte membrane fuel cell. J. Power Sour., Volume 379, 2018, Pages 328–337, ISSN 0378-7753
  4. Akimoto, Yutaro, Suzuki, Shin-nosuke.: Overpotential evaluation of PEMFC using semi-empirical equation and SEM. In: E3S Web of Conferences,
  5. 67
  6. , 01015 (2018) (3rd i-TREC 2018)
  7. Petrone et al. (2013) A review on model-based diagnosis methodologies for PEMFCs 38(17) (pp. 7077-7091) https://doi.org/10.1016/j.ijhydene.2013.03.106
  8. Khan, S.S., Shareef, H., Khan, I.A., Bhattacharjee, V., Sultan, K.W.: ”Effect of ambient conditions on water management and faults in PEMFC systems: A Review. In: IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). Edmonton, AB, Canada
  9. 2019
  10. , pp. 1–5 (2019).
  11. https://doi.org/10.1109/CCECE.2019.8861579
  12. Labach et al. (2017) Steady-state Semi-empirical Model of a Single Proton Exchange Membrane Fuel Cell (PEMFC) at Varying Operating Conditions 17(2) (pp. 166-177) https://doi.org/10.1002/fuce.201600074
  13. Moreira and da Silva (2009) A practical model for evaluating the performance of proton exchange membrane fuel cells 34(7) (pp. 1734-1741) https://doi.org/10.1016/j.renene.2009.01.002
  14. El-fergany et al. (2019) Semi-empirical PEM fuel cells model using whale optimization algorithm 201(July) (pp. 112-197)
  15. Agwa et al. (2019) ”Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer 12(10) https://doi.org/10.3390/en12101884
  16. Chakraborty (2019) Proton exchange membrane fuel cell stack design optimization using an improved jaya algorithm 12(16) https://doi.org/10.3390/en12163176
  17. Menon et al. (2019) Realisation of Optimal Parameters of PEM Fuel Cell Using Simple Genetic Algorithm (SGA) and Simulink Modeling 8(6) (pp. 1542-1548) https://doi.org/10.35940/ijeat.F8157.088619
  18. Han et al. (2019) Environmental Effects Optimal parameters of PEM fuel cells using chaotic binary shark smell optimizer, Energy Sources 00(00) (pp. 1-15)
  19. Kandidayeni et al. (2019) Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms (pp. 912-925) https://doi.org/10.1016/j.energy.2019.06.152
  20. Amphlett (1995) Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell 142(1) https://doi.org/10.1149/1.2043866
  21. Wishart et al. (2006) Optimization of a PEM fuel cell system based on empirical data and a generalized electrochemical semi-empirical model 161(2) (pp. 1041-1055) https://doi.org/10.1016/j.jpowsour.2006.05.056
  22. Larminie, J., Dicks, A.: Fuel Cell Systems Explained. J. Power Sources
  23. 93
  24. ,(2001)
  25. Al-Zeyoudi et al. (2015) Performance evaluation of an open-cathode PEM fuel cell stack under ambient conditions: case study of United Arab Emirates (pp. 798-809) https://doi.org/10.1016/j.enconman.2015.07.082
  26. Khan et al. (2018) Influences of ambient conditions on the performance of proton exchange membrane fuel cell using various models 30(6) (pp. 1087-1110) https://doi.org/10.1177/0958305X18802775
  27. Khan et al. (2020) Membrane-hydration-state detection in proton exchange membrane fuel cells using improved ambient-condition-based dynamic model (pp. 869-889) https://doi.org/10.1002/er.4927
  28. Khan et al. (2018) Novel dynamic semiempirical proton exchange membrane fuel cell model incorporating component voltages (pp. 2615-2630) https://doi.org/10.1002/er.4038
  29. Shareef et al. (2015) Lightning search algorithm (pp. 315-333) https://doi.org/10.1016/j.asoc.2015.07.028
  30. Khan et al. (2019) Dynamic temperature model for proton exchange membrane fuel cell using online variations in load current and ambient temperature 16(5) (pp. 361-370) https://doi.org/10.1080/15435075.2018.1564141
  31. Menesy et al. (2019) Developing and Applying Chaotic Harris Hawks Optimization Technique for Extracting Parameters of Several Proton Exchange Membrane Fuel Cell Stacks 8(December) (pp. 1146-1159)
  32. Maher (2005) Sadiq Al-Baghdadi, Modelling of proton exchange membrane fuel cell performance based on semi-empirical equations 30(10) (pp. 1587-1599) https://doi.org/10.1016/j.renene.2004.11.015
  33. Khan et al. (2021) Dynamic Semiempirical PEMFC Model for Prognostics and Fault Diagnosis (pp. 10217-10227) https://doi.org/10.1109/ACCESS.2021.3049528
  34. Desideri, U., Lazaroiu, G., Zaninelli, D., Lazaroiu, C.: ”A Matlab-Simulink Analysis of Hybrid SOFC Dynamic Behavior. In: Proceedings of the ASME 3rd International Conference on Fuel Cell Science, Engineering and Technology. Ypsilanti, Michigan, USA. May 23-25, pp. 245–251.2005