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Maximizing performance of fuel cell using artificial neural network approach for smart grid applications

Author

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  • Bicer, Y.
  • Dincer, I.
  • Aydin, M.

Abstract

This paper presents an artificial neural network (ANN) approach of a smart grid integrated proton exchange membrane (PEM) fuel cell and proposes a neural network model of a 6 kW PEM fuel cell. The data required to train the neural network model are generated by a model of 6 kW PEM fuel cell. After the model is trained and validated, it is used to analyze the dynamic behavior of the PEM fuel cell. The study results demonstrate that the model based on neural network approach is appropriate for predicting the outlet parameters. Various types of training methods, sample numbers and sample distribution methods are utilized to compare the results. The fuel cell stack efficiency considerably varies between 20% and 60%, according to input variables and models. The rapid changes in the input variables can be recovered within a short time period, such as 10 s. The obtained response graphs point out the load tracking features of ANN model and the projected changes in the input variables are controlled quickly in the study.

Suggested Citation

  • Bicer, Y. & Dincer, I. & Aydin, M., 2016. "Maximizing performance of fuel cell using artificial neural network approach for smart grid applications," Energy, Elsevier, vol. 116(P1), pages 1205-1217.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:1205-1217
    DOI: 10.1016/j.energy.2016.10.050
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    Citations

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    Cited by:

    1. 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.
    2. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    3. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Garcia-Villalobos, J., 2017. "Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems," Energy, Elsevier, vol. 123(C), pages 585-593.
    4. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    5. Taner, Tolga, 2018. "Energy and exergy analyze of PEM fuel cell: A case study of modeling and simulations," Energy, Elsevier, vol. 143(C), pages 284-294.
    6. Abu-Rayash, Azzam & Dincer, Ibrahim, 2020. "Development of an integrated energy system for smart communities," Energy, Elsevier, vol. 202(C).
    7. Santos, Diogo F.M. & Ferreira, Rui B. & Falcão, D.S. & Pinto, A.M.F.R., 2022. "Evaluation of a fuel cell system designed for unmanned aerial vehicles," Energy, Elsevier, vol. 253(C).
    8. Li, Tianyu & Liu, Huiying & Ding, Daolin, 2018. "Predictive energy management of fuel cell supercapacitor hybrid construction equipment," Energy, Elsevier, vol. 149(C), pages 718-729.
    9. Tanzim Meraj, Sheikh & Zaihar Yahaya, Nor & Hasan, Kamrul & Hossain Lipu, M.S. & Madurai Elavarasan, Rajvikram & Hussain, Aini & Hannan, M.A. & Muttaqi, Kashem M., 2022. "A filter less improved control scheme for active/reactive energy management in fuel cell integrated grid system with harmonic reduction ability," Applied Energy, Elsevier, vol. 312(C).
    10. Barzegari, Mohammad M. & Alizadeh, Ebrahim & Pahnabi, Amir H., 2017. "Grey-box modeling and model predictive control for cascade-type PEMFC," Energy, Elsevier, vol. 127(C), pages 611-622.
    11. Moazeni, Faegheh & Khazaei, Javad, 2020. "Electrochemical optimization and small-signal analysis of grid-connected polymer electrolyte membrane (PEM) fuel cells for renewable energy integration," Renewable Energy, Elsevier, vol. 155(C), pages 848-861.
    12. Yang, Duo & Pan, Rui & Wang, Yujie & Chen, Zonghai, 2019. "Modeling and control of PEMFC air supply system based on T-S fuzzy theory and predictive control," Energy, Elsevier, vol. 188(C).
    13. Danqi Su & Jiayang Zheng & Junjie Ma & Zizhe Dong & Zhangjie Chen & Yanzhou Qin, 2023. "Application of Machine Learning in Fuel Cell Research," Energies, MDPI, vol. 16(11), pages 1-32, May.
    14. Li, Tianyu & Huang, Lingtao & Liu, Huiying, 2019. "Energy management and economic analysis for a fuel cell supercapacitor excavator," Energy, Elsevier, vol. 172(C), pages 840-851.

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