IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v216y2021ics0360544220321630.html
   My bibliography  Save this article

Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm

Author

Listed:
  • Sun, Zhe
  • Cao, Dan
  • Ling, Yawen
  • Xiang, Feng
  • Sun, Zhixin
  • Wu, Fan

Abstract

This paper firstly proposes a dynamic differential evolution algorithm (DDE-CGF) with a collective guiding factor to solve the problem of parameter identification and optimization of proton exchange membrane fuel cell (PEMFC) model. Inspired by the swarm intelligence scheme, a collective guidance factor is designed to accelerate the convergence speed without affecting the convergence accuracy. Moreover, a dynamic scaling factor and the dynamic crossover probability based on evolutionary mechanism are introduced to enhance the diversity of population as well as improve the global searching performance. Through testing eight benchmark functions, the DDE-CGF algorithm exhibits superior performance in both convergence accuracy and speed. Based on the excellent global performance, applying DDE-CGF algorithm to the parameter identification of the PEMFC model, and more accurate parameter values are obtained. Comparing with other algorithms, the result proves that the DDE-CGF algorithm could accurately estimate model parameters and the identified model could greatly describe the dynamical characteristic of the PEMFC model.

Suggested Citation

  • Sun, Zhe & Cao, Dan & Ling, Yawen & Xiang, Feng & Sun, Zhixin & Wu, Fan, 2021. "Proton exchange membrane fuel cell model parameter identification based on dynamic differential evolution with collective guidance factor algorithm," Energy, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220321630
    DOI: 10.1016/j.energy.2020.119056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220321630
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119056?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Priya, K. & Sathishkumar, K. & Rajasekar, N., 2018. "A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 121-144.
    2. Blal, Mohamed & Benatiallah, Ali & NeÇaibia, Ammar & Lachtar, Salah & Sahouane, Nordine & Belasri, Ahmed, 2019. "Contribution and investigation to compare models parameters of (PEMFC), comprehensives review of fuel cell models and their degradation," Energy, Elsevier, vol. 168(C), pages 182-199.
    3. González-Espasandín, Óscar & Leo, Teresa J. & Raso, Miguel A. & Navarro, Emilio, 2019. "Direct methanol fuel cell (DMFC) and H2 proton exchange membrane fuel (PEMFC/H2) cell performance under atmospheric flight conditions of Unmanned Aerial Vehicles," Renewable Energy, Elsevier, vol. 130(C), pages 762-773.
    4. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
    5. Ijaodola, O.S. & El- Hassan, Zaki & Ogungbemi, E. & Khatib, F.N. & Wilberforce, Tabbi & Thompson, James & Olabi, A.G., 2019. "Energy efficiency improvements by investigating the water flooding management on proton exchange membrane fuel cell (PEMFC)," Energy, Elsevier, vol. 179(C), pages 246-267.
    6. Sharifi Asl, S.M. & Rowshanzamir, S. & Eikani, M.H., 2010. "Modelling and simulation of the steady-state and dynamic behaviour of a PEM fuel cell," Energy, Elsevier, vol. 35(4), pages 1633-1646.
    7. Yang, Shipin & Chellali, Ryad & Lu, Xiaohua & Li, Lijuan & Bo, Cuimei, 2016. "Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm," Energy, Elsevier, vol. 109(C), pages 569-577.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Fan & Li, Yuehua & Chen, Dongfang & Hu, Song & Xu, Xiaoming, 2024. "Parameter identification of PEMFC steady-state model based on p-dimensional extremum seeking via simplex tuning optimization method," Energy, Elsevier, vol. 292(C).
    2. Hachana, Oussama & El-Fergany, Attia A., 2022. "Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer," Energy, Elsevier, vol. 250(C).
    3. Gouda, Eid A. & Kotb, Mohamed F. & El-Fergany, Attia A., 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis," Energy, Elsevier, vol. 221(C).
    4. Hassan Ali, Hossam & Fathy, Ahmed, 2024. "Reliable exponential distribution optimizer-based methodology for modeling proton exchange membrane fuel cells at different conditions," Energy, Elsevier, vol. 292(C).
    5. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    6. Fan Yang & Xiaoming Xu & Yuehua Li & Dongfang Chen & Song Hu & Ziwen He & Yi Du, 2023. "A Review on Mass Transfer in Multiscale Porous Media in Proton Exchange Membrane Fuel Cells: Mechanism, Modeling, and Parameter Identification," Energies, MDPI, vol. 16(8), pages 1-24, April.
    7. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    8. Ćalasan, Martin & Micev, Mihailo & Hasanien, Hany M. & Abdel Aleem, Shady H.E., 2024. "PEM fuel cells: Two novel approaches for mathematical modeling and parameter estimation," Energy, Elsevier, vol. 290(C).
    9. Peng He & Xin Zhou & Mingqun Liu & Kewei Xu & Xian Meng & Bo Yang, 2023. "Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 16(14), pages 1-30, July.
    10. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).
    11. Abdel-Basset, Mohamed & Mohamed, Reda & El-Fergany, Attia & Chakrabortty, Ripon K. & Ryan, Michael J., 2021. "Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis," Energy, Elsevier, vol. 233(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kandidayeni, M. & Macias, A. & Khalatbarisoltani, A. & Boulon, L. & Kelouwani, S., 2019. "Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms," Energy, Elsevier, vol. 183(C), pages 912-925.
    2. Fofana, Daouda & Natarajan, Sadesh Kumar & Hamelin, Jean & Benard, Pierre, 2014. "Low platinum, high limiting current density of the PEMFC (proton exchange membrane fuel cell) based on multilayer cathode catalyst approach," Energy, Elsevier, vol. 64(C), pages 398-403.
    3. Sun, Zhe & Wang, Ning & Bi, Yunrui & Srinivasan, Dipti, 2015. "Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm," Energy, Elsevier, vol. 90(P2), pages 1334-1341.
    4. Xu, Liangfei & Fang, Chuan & Hu, Junming & Cheng, Siliang & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2017. "Parameter extraction of polymer electrolyte membrane fuel cell based on quasi-dynamic model and periphery signals," Energy, Elsevier, vol. 122(C), pages 675-690.
    5. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).
    6. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    7. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).
    8. Priya, K. & Sathishkumar, K. & Rajasekar, N., 2018. "A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 121-144.
    9. Ángel Encalada-Dávila & Samir Echeverría & Jordy Santana-Villamar & Gabriel Cedeño & Mayken Espinoza-Andaluz, 2021. "Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity," Energies, MDPI, vol. 14(18), pages 1-21, September.
    10. Gouda, Eid A. & Kotb, Mohamed F. & El-Fergany, Attia A., 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis," Energy, Elsevier, vol. 221(C).
    11. El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    12. Miao, Di & Chen, Wei & Zhao, Wei & Demsas, Tekle, 2020. "Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method," Energy, Elsevier, vol. 193(C).
    13. Yang, Bo & Li, Danyang & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Wang, Jingbo & Shu, Hongchun & Yu, Tao & Zhu, Jiawei, 2021. "Parameter extraction of PEMFC via Bayesian regularization neural network based meta-heuristic algorithms," Energy, Elsevier, vol. 228(C).
    14. Yang, Zirong & Du, Qing & Jia, Zhiwei & Yang, Chunguang & Jiao, Kui, 2019. "Effects of operating conditions on water and heat management by a transient multi-dimensional PEMFC system model," Energy, Elsevier, vol. 183(C), pages 462-476.
    15. Zhang, Yong & He, Shirong & Jiang, Xiaohui & Wang, Zhuo & Wang, Yonggang & Gu, Meng & Yang, Xi & Zhang, Shuanyang & Cao, Jing & Fang, Haoyan & Li, Qiming, 2024. "Performance and configuration optimization of proton exchange membrane fuel cell considering dual symmetric Tesla valve flow field," Energy, Elsevier, vol. 288(C).
    16. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    17. Chen Zhang & Wen Li & Mingruo Hu & Xiaofang Cheng & Kai He & Lei Mao, 2020. "A Comparative Study of Using Polarization Curve Models in Proton Exchange Membrane Fuel Cell Degradation Analysis," Energies, MDPI, vol. 13(15), pages 1-16, July.
    18. Pan, Mingzhang & Li, Chao & Liao, Jinyang & Lei, Han & Pan, Chengjie & Meng, Xianpan & Huang, Haozhong, 2020. "Design and modeling of PEM fuel cell based on different flow fields," Energy, Elsevier, vol. 207(C).
    19. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    20. Cha, Dowon & Yang, Wonseok & Kim, Yongchan, 2019. "Performance improvement of self-humidifying PEM fuel cells using water injection at various start-up conditions," Energy, Elsevier, vol. 183(C), pages 514-524.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220321630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.