Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 142-156

Original Article

Comparison of methodologies For Tracking The Maximum Power Point in Fuel Cell System

Mayyadah K. Salim 1 Ammar A. Aldair, 1 Osama Y. K. Al-Atbee 1
1 Electrical Engineering Department, University of Basrah, Iraq
History
  • Received: March 11, 2024
  • Revised: April 28, 2024
  • Accepted: May 3, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.14

Abstract

The Maximal Power Point Tracking (MPPT) is a method employed to maximize the generated power from an energy source, such as PV (photovoltaic) or PEMFC (Proton Exchange Membrane Fuel Cell). In this study, the Grey Wolf Optimizer algorithm is utilized for the MPPT to regulate the boost converter positioned between the stack cell and the battery. The primary challenge addressed by the MPPT is that the efficiency of PEMFC is influenced by the supplied gases and cell temperature. To maintain optimal performance, the system aims to operate at the efficient power point, and the MPPT assists in achieving this by adjusting the voltage to align with the point where the PEMFC characteristic yields the maximum available power. Consequently, the MPPT’s objective is to identify the Maximum Power Point (MPP) and guide the PEMFC to operate at this specific point. This process is essential to overcome challenges associated with fluctuating inputs and to optimize the system for improved performance in a PEMFC. Typically, the MPPT control algorithm involves modifying the converter duty cycle (denoted as D) to compel the PEMFCs to operate at their MPP, ensuring efficient power production even under varying input conditions

Keywords

References

  1. D. Akinyele, E. Olabode, and A. Amole, “Review of fuel cell technologies and applications for sustainable microgrid systems,” Inventions, vol. 5, no. 3, p. 42, 2020.
  2. A. A. Z. Diab, H. Ali, H. Abdul-Ghaffar, H. A. Abdelsalam, and M. Abd El Sattar, “Accurate parameters extraction of pemfc model based on metaheuristics algorithms,” Energy Reports, vol. 7, pp. 6854–6867, 2021.
  3. M. A. Mossa, O. M. Kamel, H. M. Sultan, and A. A. Z. Diab, “Parameter estimation of pemfc model based on harris hawks’ optimization and atom search optimization algorithms,” Neural Computing and Applications, vol. 33, pp. 5555–5570, 2021.
  4. H. Islam, S. Mekhilef, N. B. M. Shah, T. K. Soon, M. Seyedmahmousian, B. Horan, and A. Stojcevski, “Performance evaluation of maximum power point tracking approaches and photovoltaic systems,” Energies, vol. 11, no. 2, p. 365, 2018.
  5. N. Naseri, S. El Hani, A. Aghmadi, K. El Harouri, M. S. Heyine, and H. Mediouni, “Proton exchange membrane fuel cell modelling and power control by p&o algorithm,” in 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–5, IEEE, 2018.
  6. M. H. Wang, M.-L. Huang, W.-J. Jiang, and K.-J. Liou, “Maximum power point tracking control method for proton exchange membrane fuel cell,” IET Renewable Power Generation, vol. 10, no. 7, pp. 908–915, 2016.
  7. N. Karami, L. El Khoury, G. Khoury, and N. Moubayed, “Comparative study between p&o and incremental conductance for fuel cell mppt,” in International conference on renewable energies for developing countries 2014, pp. 17–22, IEEE, 2014.
  8. D. N. Luta and A. K. Raji, “Fuzzy rule-based and particle swarm optimisation mppt techniques for a fuel cell stack,” Energies, vol. 12, no. 5, p. 936, 2019.
  9. J. Jiao and X. Cui, “Adaptive control of mppt for fuel cell power system,” J. Converg. Inf. Technol, vol. 8, no. 4, pp. 362–371, 2013.
  10. K. Afshar, N. Bigdeli, and S. Ahmadi, “A novel approach for robust maximum power point tracking of pem fuel cell generator using sliding mode control approach,” International Journal of Electrochemical Science, vol. 7, no. 5, pp. 4192–4209, 2012.
  11. A. O. Baba, G. Liu, and X. Chen, “Classification and evaluation review of maximum power point tracking methods,” Sustainable Futures, vol. 2, p. 100020, 2020.
  12. P. Motsoeneng, J. Bamukunde, and S. Chowdhury, “Comparison of perturb & observe and hill climbing mppt schemes for pv plant under cloud cover and varying load,” in 2019 10th International Renewable Energy Congress (IREC), pp. 1–6, IEEE, 2019.
  13. S. Hosseini, S. Danyali, F. Nejabatkhah, and S. K. M. Niapoor, “Multi-input dc boost converter for grid connected hybrid pv/fc/battery power system,” in 2010 IEEE Electrical Power & Energy Conference, pp. 1–6, IEEE, 2010.
  14. M. Becherif and D. Hissel, “Mppt of a pemfc based on air supply control of the motocompressor group,” International Journal of Hydrogen Energy, vol. 35, no. 22, pp. 12521–12530, 2010.
  15. N. Chanasut and S. Premrudeepreechacharn, “Maximum power control of grid-connected solid oxide fuel cell system using adaptive fuzzy logic controller,” in 2008 IEEE Industry Applications Society Annual Meeting, pp. 1–6, IEEE, 2008.
  16. K. H. Loo, G. Zhu, Y. Lai, and K. T. Chi, “Development of a maximum-power-point tracking algorithm for direct methanol fuel cell and its realization in a fuel cell/supercapacitor hybrid energy system,” in 8th International Conference on Power Electronics-ECCE Asia, pp. 1753–1760, IEEE, 2011.
  17. M. Sarvi and M. Barati, “Voltage and current based mppt of fuel cells under variable temperature conditions,” in 45th International Universities Power Engineering Conference UPEC2010, pp. 1–4, IEEE, 2010.
  18. S. Ahmadi, S. Abdi, and M. Kakavand, “Maximum power point tracking of a proton exchange membrane fuel cell system using pso-pid controller,” International journal of hydrogen energy, vol. 42, no. 32, pp. 20430– 20443, 2017.
  19. M. Derbeli, O. Barambones, M. Y. Silaa, and C. Napole, “Real-time implementation of a new mppt control method for a dc-dc boost converter used in a pem fuel cell power system,” in Actuators, vol. 9, p. 105, MDPI, 2020.
  20. Z.-J. Mo, X.-J. Zhu, L.-Y. Wei, and G.-Y. Cao, “Parameter optimization for a pemfc model with a hybrid genetic algorithm,” International Journal of Energy Research, vol. 30, no. 8, pp. 585–597, 2006.
  21. J. C. Amphlett, R. Baumert, R. F. Mann, B. A. Peppley, P. R. Roberge, and T. J. Harris, “Performance modeling of the ballard mark iv solid polymer electrolyte fuel cell: Ii. empirical model development,” Journal of the Electrochemical Society, vol. 142, no. 1, p. 9, 1995.
  22. O. E. Turgut and M. T. Coban, “Optimal proton exchange membrane fuel cell modelling based on hybrid teaching learning based optimization–differential evolution algorithm,” Ain Shams Engineering Journal, vol. 7, no. 1, pp. 347–360, 2016.
  23. R. F. Mann, J. C. Amphlett, M. A. Hooper, H. M. Jensen, B. A. Peppley, and P. R. Roberge, “Development and application of a generalised steady-state electrochemical model for a pem fuel cell,” Journal of power sources, vol. 86, no. 1-2, pp. 173–180, 2000.
  24. Y. Chen and N. Wang, “Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells,” International Journal of Hydrogen Energy, vol. 44, no. 5, pp. 3075–3087, 2019.
  25. A. Marahatta, Y. Rajbhandari, A. Shrestha, A. Singh, A. Gachhadar, and A. Thapa, “Priority-based low voltage dc microgrid system for rural electrification,” Energy Reports, vol. 7, pp. 43–51, 2021.
  26. S. Saadatmand, P. Shamsi, and M. Ferdowsi, “The voltage regulation of a buck converter using a neural network predictive controller,” in 2020 IEEE Texas Power and Energy Conference (TPEC), pp. 1–6, IEEE, 2020.
  27. M. S. Alam, F. S. Al-Ismail, A. Salem, and M. A. Abido, “High-level penetration of renewable energy sources into vol. 8, pp. 190277–190299, 2020.
  28. M. Islam, A. F. Abdul Ghaffar, E. Sulaeman, M. M. Ahsan, A. Z. Kouzani, and M. P. Mahmud, “Performance analysis of pi and dmrac algorithm in buck–boost converter for voltage tracking in electric vehicle using simulation,” Electronics, vol. 10, no. 20, p. 2516, 2021.
  29. C. Cui, N. Yan, and C. Zhang, “An intelligent control strategy for buck dc-dc converter via deep reinforcement learning,” arXiv preprint arXiv:2008.04542, 2020.
  30. R. Abdul-Halem, H. A. Hairik, and A. J. Kadhem, “Experimental prototype for pwm-based sliding mode boost converter,” in 2010 1st International Conference on Energy, Power and Control (EPC-IQ), pp. 230–236, IEEE, 2010.
  31. S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer adv eng softw 69: 46–61,” ed, 2014.
  32. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948, ieee, 1995.
  33. I. A. Amin, D. Y. Mahmood, and A. H. Numan, “Optimal localization of upfc for transmission line losses minimizing using particle swarm optimization,” Engineering and Technology Journal, vol. 39, no. 10, pp. 1463–1472, 2021.
  34. F. S. Abdulla, A. N. Hamoodi, and A. M. Kheder, “Particle swarm optimization algorithm for solar pv system under partial shading.,” Przeglad Elektrotechniczny, vol. 97, no. 10, 2021.
  35. B. N. A. Samed and A. A. Aldair, “Design tunable robust controllers for unmanned aerial vehicle based on particle swarm optimization algorithm.,” Iraqi Journal for Electrical & Electronic Engineering, vol. 15, no. 2, 2019.
  36. F. Liu, Y. Kang, Y. Zhang, and S. Duan, “Comparison of p&o and hill climbing mppt methods for grid-connected pv converter,” in 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 804–807, IEEE, 2008.
  37. M. A. G. De Brito, L. Galotto, L. P. Sampaio, G. d. A. e Melo, and C. A. Canesin, “Evaluation of the main mppt techniques for photovoltaic applications,” IEEE transactions on industrial electronics, vol. 60, no. 3, pp. 1156–1167, 2012.