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A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm

Author

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  • Changhee Song

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Sanghoon Lee

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Bonhyun Gu

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

  • Ikwhang Chang

    (Department of Mechanical and Automotive Engineering, Won-Kwang University, 460 Iksan-daero, Iksan, Jeonbuk 54538, Korea)

  • Gu Young Cho

    (Department of Mechanical Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si, Gyeonggi-do 16890, Korea)

  • Jong Dae Baek

    (Department of Automotive Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 38541, Korea)

  • Suk Won Cha

    (Department of Mechanical and Aerospace Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea)

Abstract

Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.

Suggested Citation

  • Changhee Song & Sanghoon Lee & Bonhyun Gu & Ikwhang Chang & Gu Young Cho & Jong Dae Baek & Suk Won Cha, 2020. "A Study of Anode-Supported Solid Oxide Fuel Cell Modeling and Optimization Using Neural Network and Multi-Armed Bandit Algorithm," Energies, MDPI, vol. 13(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1621-:d:340239
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    References listed on IDEAS

    as
    1. Fong, K.F. & Lee, C.K., 2019. "Performance investigation of a SOFC-primed micro-combined hybrid cooling and power system in hot and humid regions," Energy, Elsevier, vol. 189(C).
    2. Park, Taehyun & Chang, Ikwhang & Lee, Yoon Ho & Ji, Sanghoon & Cha, Suk Won, 2014. "Analysis of operational characteristics of polymer electrolyte fuel cell with expanded graphite flow-field plates via electrochemical impedance investigation," Energy, Elsevier, vol. 66(C), pages 77-81.
    3. Cuneo, A. & Zaccaria, V. & Tucker, D. & Sorce, A., 2018. "Gas turbine size optimization in a hybrid system considering SOFC degradation," Applied Energy, Elsevier, vol. 230(C), pages 855-864.
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    Cited by:

    1. Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.
    2. Khadijeh Hooshyari & Bahman Amini Horri & Hamid Abdoli & Mohsen Fallah Vostakola & Parvaneh Kakavand & Parisa Salarizadeh, 2021. "A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells," Energies, MDPI, vol. 14(17), pages 1-38, September.
    3. Ong, Samuel & Al-Othman, Amani & Tawalbeh, Muhammad, 2023. "Emerging technologies in prognostics for fuel cells including direct hydrocarbon fuel cells," Energy, Elsevier, vol. 277(C).
    4. Mohsen Fallah Vostakola & Bahman Amini Horri, 2021. "Progress in Material Development for Low-Temperature Solid Oxide Fuel Cells: A Review," Energies, MDPI, vol. 14(5), pages 1-53, February.
    5. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).

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