Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm

Cenk Andiç

doi:10.29130/dubited.1930735

TR EN

Süzülen Yılan Algoritması ile PEMFC Modelinin Parametre Tahmini

Abstract

Bu çalışma, proton değişim membranlı yakıt pili (PEMFC) modellerinin yedi bilinmeyen parametresinin tahmini için Süzülen Yılan Optimizasyon (SYO) algoritmasına dayalı yeni bir yaklaşım önermektedir. Önerilen SYO yaklaşımı, üç farklı yakıt pili modeli üzerinde test edilmiştir: Nedstack PS6, BCS 500W ve Ballard Mark V. Yakıt pillerine ait deneysel gerilim-akım (V-I) polarizasyon verileri kullanılarak, yarı ampirik modele ait yedi bilinmeyen parametre (ξ₁, ξ₂, ξ₃, ξ₄, λ, Rc, β) önerilen GSO yaklaşımı ile tahmin edilmiştir. Elde edilen parametreler yardımıyla, yakıt pilinin çıkış gerilimi yüksek doğrulukla hesaplanmıştır. Önerilen SYO yaklaşımı kullanılarak, Nedstack PS6, BCS 500W ve Ballard Mark V modelleri için sırasıyla 2.0655, 0.0193 ve 0.7598 SSE değerleri elde edilmiştir. Güncel literatürde yer alan yöntemlerle yapılan karşılaştırmalı sonuçlar, önerilen yaklaşımın Nedstack PS6, BCS 500W ve Ballard Mark V modelleri için sırasıyla E-ABC, TDE ve EMEDEA algoritmalarına göre %3.88, %24.02 ve %6.51 oranında performans iyileşmesi sağladığını göstermektedir. Bu çalışmanın temel katkısı, PEM yakıt pili parametre tahmini için SYO algoritmasına dayalı güncel, etkili ve pratik bir sezgisel optimizasyon yaklaşımının sunulmasıdır. Farklı PEM yakıt pili modelleri için elde edilen en iyi SSE değerleri ve literatürde rapor edilen güncel optimizasyon yöntemlerine göre sağlanan üstünlük, önerilen yaklaşımın deneysel ölçümlere dayalı yarı ampirik parametrelerin tahmininde rekabetçi ve etkili bir alternatif sunduğunu ortaya koymaktadır.

Keywords

Proton değişim membranlı yakıt pili, Parametre tahmini, Süzülen yılan optimizasyon algoritması

Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm

Abstract

This study proposes a novel approach based on the Glider Snake Optimization (GSO) algorithm for estimating the seven unknown parameters of proton exchange membrane fuel cell models. The proposed GSO method was tested on three different fuel cell models: Nedstack PS6, BCS 500W and Ballard Mark V. Using the experimental voltage-current (V-I) polarization data of the fuel cells, the seven unknown parameters of the semi-empirical model (ξ₁, ξ₂, ξ₃, ξ₄, λ, Rc, β) were estimated by the proposed GSO method. With the help of the obtained parameters, the output voltage of the fuel cell was calculated with high accuracy. By using the proposed GSO method, Sum of Squared Error (SSE) values of 2.0655, 0.0193 and 0.7598 were obtained for the Nedstack PS6, BCS 500W and Ballard Mark V models, respectively. Comparative results with recent literature methods indicate that the proposed method provides performance improvements of 3.88%, 24.02% and 6.51% over the E-ABC, TDE and EMEDEA algorithms for the Nedstack PS6, BCS 500W and Ballard Mark V models, respectively. The main contribution of this study lies in introducing a recent, effective and practical heuristic optimization approach based on the GSO algorithm for PEM fuel cell parameter estimation. The best SSE values obtained for different PEM fuel cell models and the superiority achieved over current optimization methods reported in the literature demonstrate that the proposed method offers a competitive and effective alternative for estimating semi-empirical parameters based on experimental measurements.

Keywords

Proton exchange membrane fuel cell, Parameter estimation, Glider snake optimization

Supporting Institution

This research received no external funding.

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

Thanks

The author does not wish to acknowledge any individual or institution.

References

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Details

Primary Language

English

Subjects

Electrical Energy Storage, Electrical Engineering (Other)

Journal Section

Research Article

Authors

Cenk Andiç ^*
0000-0003-1123-899X
Türkiye

Publication Date

July 24, 2026

Submission Date

April 15, 2026

Acceptance Date

June 19, 2026

Published in Issue

Year 2026 Volume: 14 Number: 3

DOI

https://doi.org/10.29130/dubited.1930735

IZ

https://izlik.org/JA89SK46SR

APA

Andiç, C. (2026). Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm. Duzce University Journal of Science and Technology, 14(3), 878-896. https://doi.org/10.29130/dubited.1930735

AMA

1.Andiç C. Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm. DUBİTED. 2026;14(3):878-896. doi:10.29130/dubited.1930735

Chicago

Andiç, Cenk. 2026. “Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm”. Duzce University Journal of Science and Technology 14 (3): 878-96. https://doi.org/10.29130/dubited.1930735.

EndNote

Andiç C (July 1, 2026) Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm. Duzce University Journal of Science and Technology 14 3 878–896.

IEEE

[1]C. Andiç, “Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm”, DUBİTED, vol. 14, no. 3, pp. 878–896, July 2026, doi: 10.29130/dubited.1930735.

ISNAD

Andiç, Cenk. “Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm”. Duzce University Journal of Science and Technology 14/3 (July 1, 2026): 878-896. https://doi.org/10.29130/dubited.1930735.

JAMA

1.Andiç C. Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm. DUBİTED. 2026;14:878–896.

MLA

Andiç, Cenk. “Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm”. Duzce University Journal of Science and Technology, vol. 14, no. 3, July 2026, pp. 878-96, doi:10.29130/dubited.1930735.

Vancouver

1.Cenk Andiç. Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm. DUBİTED. 2026 Jul. 1;14(3):878-96. doi:10.29130/dubited.1930735

Süzülen Yılan Algoritması ile PEMFC Modelinin Parametre Tahmini

Abstract

Keywords

Parameter Estimation of PEMFC Model Via Glider Snake Optimization Algorithm

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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