IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v361y2024ics030626192400240x.html
   My bibliography  Save this article

Parameter identification of PEMFC via feedforward neural network-pelican optimization algorithm

Author

Listed:
  • Yang, Bo
  • Liang, Boxiao
  • Qian, Yucun
  • Zheng, Ruyi
  • Su, Shi
  • Guo, Zhengxun
  • Jiang, Lin

Abstract

Parameter identification is a critical task in the research of proton exchange membrane fuel cells (PEMFC), which provides the basis for establishing an accurate and reliable PEMFC model. However, the nonlinear characteristics of PEMFC model as well as inevitable noise data and insufficient measurement data often overwhelm traditional optimization techniques. In particular, noise data and inadequate measurement data can introduce bias or lead to data loss. To address this problem, a novel hybrid optimization strategy is proposed. Firstly, a feedforward neural network (FNN) is employed to preprocess the measured data (i.e., reducing noise data and enriching measurement data). Furthermore, Gaussian noise and Rayleigh noise with three signal-to-noise ratio levels are introduced to simulate various disturbances of noise. Then, the pelican optimization algorithm (POA) is used to identify the parameters of PEMFC based on preprocessed data. Lastly, the effectiveness of the proposed strategy named FNN-POA is verified by comparing it with seven advanced competitive algorithms. Simulation results demonstrate that FNN-POA has higher robustness and optimization quality by comparing original data and preprocessed data. For instance, the root-mean-square error obtained by FNN-POA is reduced by 99.44% under medium temperature and medium pressure through noise reduction.

Suggested Citation

  • Yang, Bo & Liang, Boxiao & Qian, Yucun & Zheng, Ruyi & Su, Shi & Guo, Zhengxun & Jiang, Lin, 2024. "Parameter identification of PEMFC via feedforward neural network-pelican optimization algorithm," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s030626192400240x
    DOI: 10.1016/j.apenergy.2024.122857
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192400240X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.122857?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. Salim, Reem & Nabag, Mahmoud & Noura, Hassan & Fardoun, Abbas, 2015. "The parameter identification of the Nexa 1.2 kW PEMFC's model using particle swarm optimization," Renewable Energy, Elsevier, vol. 82(C), pages 26-34.
    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. 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).
    5. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    6. Fathy, Ahmed & Rezk, Hegazy, 2018. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model," Energy, Elsevier, vol. 143(C), pages 634-644.
    7. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
    8. Chen, Xiang & Ding, Kun & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Jiang, Meng & Gao, Ruiguang & Liu, Zengquan, 2023. "Research on real-time identification method of model parameters for the photovoltaic array," Applied Energy, Elsevier, vol. 342(C).
    9. Antolini, Ermete, 2011. "The stability of molten carbonate fuel cell electrodes: A review of recent improvements," Applied Energy, Elsevier, vol. 88(12), pages 4274-4293.
    10. El-Fergany, Attia A., 2018. "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer," Renewable Energy, Elsevier, vol. 119(C), pages 641-648.
    11. Rezk, Hegazy & Ferahtia, Seydali & Djeroui, Ali & Chouder, Aissa & Houari, Azeddine & Machmoum, Mohamed & Abdelkareem, Mohammad Ali, 2022. "Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer," Energy, Elsevier, vol. 239(PC).
    Full references (including those not matched with items on IDEAS)

    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. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Mohammad Ali Abdelkareem & Enas Taha Sayed, 2023. "Fuzzy Modelling and Optimization to Decide Optimal Parameters of the PEMFC," Energies, MDPI, vol. 16(12), pages 1-16, June.
    2. Rezk, Hegazy & Olabi, A.G. & Ferahtia, Seydali & Sayed, Enas Taha, 2022. "Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell," Energy, Elsevier, vol. 255(C).
    3. Fathy, Ahmed & Elaziz, Mohamed Abd & Alharbi, Abdullah G., 2020. "A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell," Renewable Energy, Elsevier, vol. 146(C), pages 1833-1845.
    4. 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).
    5. 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.
    6. Fathy, Ahmed & Rezk, Hegazy & Alharbi, Abdullah G. & Yousri, Dalia, 2023. "Proton exchange membrane fuel cell model parameters identification using Chaotically based-bonobo optimizer," Energy, Elsevier, vol. 268(C).
    7. Hegazy Rezk & Tabbi Wilberforce & A. G. Olabi & Rania M. Ghoniem & Enas Taha Sayed & Mohammad Ali Abdelkareem, 2023. "Optimal Parameter Identification of a PEM Fuel Cell Using Recent Optimization Algorithms," Energies, MDPI, vol. 16(14), pages 1-20, July.
    8. Alaa A. Zaky & Rania M. Ghoniem & F. Selim, 2023. "Precise Modeling of Proton Exchange Membrane Fuel Cell Using the Modified Bald Eagle Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    9. Ahmed M. Agwa & Attia A. El-Fergany & Gamal M. Sarhan, 2019. "Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer," Energies, MDPI, vol. 12(10), pages 1-14, May.
    10. 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).
    11. Rezk, Hegazy & Ferahtia, Seydali & Djeroui, Ali & Chouder, Aissa & Houari, Azeddine & Machmoum, Mohamed & Abdelkareem, Mohammad Ali, 2022. "Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer," Energy, Elsevier, vol. 239(PC).
    12. 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).
    13. 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.
    14. Wilberforce, Tabbi & Rezk, Hegazy & Olabi, A.G. & Epelle, Emmanuel I. & Abdelkareem, Mohammad Ali, 2023. "Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms," Energy, Elsevier, vol. 262(PB).
    15. Andrew J. Riad & Hany M. Hasanien & Rania A. Turky & Ahmed H. Yakout, 2023. "Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    16. 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.
    17. 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.
    18. Liu, Hongwei & Ren, He & Gu, Yajing & Lin, Yonggang & Hu, Weifei & Song, Jiajun & Yang, Jinhong & Zhu, Zengxin & Li, Wei, 2023. "Design and on-site implementation of an off-grid marine current powered hydrogen production system," Applied Energy, Elsevier, vol. 330(PB).
    19. 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).
    20. En-Jui Liu & Yi-Hsuan Hung & Che-Wun Hong, 2021. "Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction," Energies, MDPI, vol. 14(3), pages 1-16, January.

    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:appene:v:361:y:2024:i:c:s030626192400240x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.