Parameter Estimation of Proton Exchange Membrane Fuel Cell Model Using Chaotic Embedded Particle Swarm Optimization Technique | SN Computer Science Skip to main content
Springer Nature Link
Log in

Parameter Estimation of Proton Exchange Membrane Fuel Cell Model Using Chaotic Embedded Particle Swarm Optimization Technique

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The value of parametric coefficients in proton exchange membrane fuel cells (PEMFC) are not specified by the manufacturer thus it becomes necessary to extract those values as they affect the system performance. The losses in the FC systems highly depend on these parameters which result in reduced output voltage. To minimise the losses occurring in PEMFC, improving output voltage, obtaining a better V–I curve, and improving the performance of PEMFC system extraction of parameters are a must. A static semi-empirical PEMFC model is developed in MATLAB to estimate the value of parametric coefficients. Chaotic Embedded Particle Swarm Optimization (CEPSO) algorithm is used to estimate the optimal values of seven parametric coefficients of PEMFC. The voltage-based objective function is proposed to minimise the sum of squared error (SSE) which is obtained due to the difference in simulated values and experimental data collected at N points. The algorithm coding is done in MATLAB. A Ballard Mark V PEMFC stack is numerically simulated to depict the effectiveness of the parameter determination process. The V–I curve of the PEMFC model obtained using optimised values is verified with the simulated model curve. The minimum SSE of 0.690 was obtained in 63 iterations using CEPSO that showed a higher convergence. The proposed technique was verified for different operating conditions. The V–I curve obtained in both cases is closely matched. The performance of the proposed algorithm is compared with DKPSO, grasshopper optimization algorithm, chaotic Mayfly optimization algorithm and harmony global search algorithm. The proposed technique outperformed other mentioned techniques and proved its superiority over others in obtaining minimum error and a better V–I curve.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Mitra U, Arya A, Gupta S, Gupta AK. A brief overview on fuel cell electric vehicles. In: International conference on emerging trends in engineering and medical sciences (ICETEMS), Nagpur, India 2022. pp. 124–9. https://doi.org/10.1109/ICETEMS56252.2022.10093428.

  2. Akinyele D, Olabode E, Amole A. Review of fuel cell technologies and applications for sustainable microgrid systems. Inventions. 2020;5(3):42 (31–35).

    Article  Google Scholar 

  3. Lai J, Ellis MW. Fuel cell power systems and applications. Proc IEEE. 2017;105(11):2166–90. https://doi.org/10.1109/JPROC.2017.2723561.

    Article  Google Scholar 

  4. Mitra U, Arya A, Dwivedi R, Gupta S, Paliwal P, Tomar S. Modelling proton exchange membrane fuel cell for power generation using multi-stage power conversion system. In: IEEE international students' conference on electrical, electronics and computer science (SCEECS), Bhopal, India, 2023. pp. 1–6. https://doi.org/10.1109/SCEECS57921.2023.10063048.

  5. Mitra U, Arya A, Gupta S. A comprehensive and comparative review on parameter estimation methods for modelling proton exchange membrane fuel cell. Fuel. 2023;335:127080. https://doi.org/10.1016/j.fuel.2022.127080. (ISSN 0016-2361).

    Article  Google Scholar 

  6. Yu X, Starke MR, Tolbert LM, Ozpineci B. Fuel cell power conditioning for electric power applications: a summary. IET Electr Power Appl. 2007;1(5):643–56.

    Article  Google Scholar 

  7. Ye M, Wang X, Xu Y. Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization. Int J Hydrog Energy. 2009;34:981–9. https://doi.org/10.1016/j.ijhydene.2008.11.026.

    Article  Google Scholar 

  8. Salim R, Nabag M, Noura H, Fardoun AA. The parameter identification of the Nexa 1.2 kW PEMFC’s model using particle swarm optimization. Renew Energy. 2015;82:26–34. https://doi.org/10.1016/j.renene.2014.10.012z.

    Article  Google Scholar 

  9. Isa ZM, Rahim NA. PEM fuel cell model parameters optimization using modified particle swarm optimization algorithm. In: IEEE conference on clean energy and technology (CEAT) 2013. pp. 442–5. https://doi.org/10.1109/CEAT.2013.6775672.

  10. Askarzadeh A, Rezazadeh A. Optimization of PEMFC model parameters with a modified particle swarm optimization. Int J Energy Res. 2011;35:1258–65. https://doi.org/10.1002/er.1787.

    Article  Google Scholar 

  11. Özdemir MT. Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization. Int J Hydrog Energy. 2021;46(30):16465–80. https://doi.org/10.1016/j.ijhydene.2020.12.203.

    Article  Google Scholar 

  12. Li X, Yan Q, Yu D. Parameter optimization for a polymer electrolyte membrane fuel cell model. Appl Mech Mater. 2010. https://doi.org/10.4028/www.scientific.net/AMM.37-38.834.

    Article  Google Scholar 

  13. Sedighizadeh M, Kashani MF. A tribe particle swarm optimization for parameter identification of proton exchange membrane fuel cell. Int J Eng Trans A. 2015;28:16–25. https://doi.org/10.5829/idosi.ije.2015.28.01a.03.

    Article  Google Scholar 

  14. Li Q, Chen W, Wang Y, Liu S, Jia J. Parameter identification for PEM fuel-cell mechanism model based on effective informed adaptive particle swarm optimization. IEEE Trans Ind Electron. 2011;58(6):2410–9. https://doi.org/10.1109/TIE.2010.2060456.

    Article  Google Scholar 

  15. Abdullah A, Rezk H, Hadad A, Hassan M, Mohamed A. Optimal parameter estimation of proton exchange membrane fuel cells. Intell Autom Soft Comput. 2021;29:619–31. https://doi.org/10.32604/iasc.2021.018289.

    Article  Google Scholar 

  16. Kumar P, Sathishkumar K, Natarajan R. A comprehensive review on parameter estimation techniques for proton exchange membrane fuel cell modelling. Renew Sustain Energy Rev. 2018;93:121-144.1325. https://doi.org/10.1016/j.rser.2018.05.017.

    Article  Google Scholar 

  17. Mitzel J, Gülzow A, Kabza A, Hunger J, Araya SS, Piela P, Alecha I, Tsotridis G. Identification of critical parameters for PEMFC stack performance characterization and control strategies for reliable and comparable stack benchmarking. Int J Hydrog Energy. 2016;41(46):21415–26. https://doi.org/10.1016/j.ijhydene.2016.08.065.

    Article  Google Scholar 

  18. Alatas B, Akin E, Bedri-Ozer A. Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals. 2009;40(4):1715–34. https://doi.org/10.1016/j.chaos.2007.09.063. (ISSN 0960 0779).

    Article  MathSciNet  MATH  Google Scholar 

  19. El-Fergany AA. Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser. IET Renew Power Gener. 2017;12(1):9–17.

    Article  Google Scholar 

  20. Shaheen MA, Hasanien HM, El Moursi MS, El-Fergany AA. Precise modelling of PEM fuel cell using improved chaotic MayFly optimization algorithm. Int J Energy Res. 2021;45:18754–69. https://doi.org/10.1002/er.6987.

    Article  Google Scholar 

  21. Fahim SR, Hasanien HM, Turky RA, Alkuhayli A, Al-ShammaÁ AA, Noman AM, Tostado-Veliz M, Jurado F. Parameter identification of proton exchange membrane fuel cell based on hunger games search algorithm. Energies. 2021;5022:1–21.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India

    Uliya Mitra, Anoop Arya, Sushma Gupta & Priyanka Paliwal

  2. Banaras Hindu University, Varanasi, UP, India

    Anshul Verma

Authors
  1. Uliya Mitra
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Anoop Arya
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sushma Gupta
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Priyanka Paliwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Anshul Verma
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Uliya Mitra.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mitra, U., Arya, A., Gupta, S. et al. Parameter Estimation of Proton Exchange Membrane Fuel Cell Model Using Chaotic Embedded Particle Swarm Optimization Technique. SN COMPUT. SCI. 4, 473 (2023). https://doi.org/10.1007/s42979-023-01957-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01957-0

Keywords