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Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter

Author

Listed:
  • Yuanwu Xu

    (School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Hao Shu

    (School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Hongchuan Qin

    (Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xiaolong Wu

    (School of Information Engineering, Nanchang University, Nanchang 330031, China
    Shenzhen Research Institute, Huazhong University of Science and Technology, Shenzhen 518063, China)

  • Jingxuan Peng

    (Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chang Jiang

    (Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhiping Xia

    (Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yongan Wang

    (State Grid Hubei Maintenance Company, Wuhan 430077, China)

  • Xi Li

    (Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
    Shenzhen Research Institute, Huazhong University of Science and Technology, Shenzhen 518063, China)

Abstract

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time Unscented Kalman Filter (UKF) based SOH estimation method is further proposed to eliminate the disturbance of calculating the SOH directly utilizing measurement and electric balance model. The results of real-time SOH estimation with an UKF under constant and varying load conditions demonstrate the feasibility and effectiveness of the SOFC performance degradation assessment method.

Suggested Citation

  • Yuanwu Xu & Hao Shu & Hongchuan Qin & Xiaolong Wu & Jingxuan Peng & Chang Jiang & Zhiping Xia & Yongan Wang & Xi Li, 2022. "Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter," Energies, MDPI, vol. 15(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2534-:d:783257
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    References listed on IDEAS

    as
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