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Long-Term Degradation Trend Prediction and Remaining Useful Life Estimation for Solid Oxide Fuel Cells

Author

Listed:
  • Lixiang Cui

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Haibo Huo

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Genhui Xie

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Jingxiang Xu

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Xinghong Kuang

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

  • Zhaopeng Dong

    (College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China)

Abstract

During the actual operation of the solid oxide fuel cell (SOFC), degradation is one of the most difficult technical problems to overcome. Predicting the degradation trend and estimating the remaining useful life (RUL) can effectively diagnose the potential failure and prolong the useful life of the fuel cell. To study the degradation trend of the SOFC under constant load conditions, a SOFC degradation model based on the ohmic area specific resistance (ASR) is presented first in this paper. Based on this model, a particle filter (PF) algorithm is proposed to predict the long-term degradation trend of the SOFC. The prediction performance of the PF is compared with that of the Kalman filter, which shows that the proposed algorithm is equipped with better accuracy and superiority. Furthermore, the RUL of the SOFC is estimated by using the obtained degradation prediction data. The results show that the model-based RUL estimation method has high accuracy, while the excellence of the PF algorithm for degradation trend prediction and RUL estimation is proven.

Suggested Citation

  • Lixiang Cui & Haibo Huo & Genhui Xie & Jingxiang Xu & Xinghong Kuang & Zhaopeng Dong, 2022. "Long-Term Degradation Trend Prediction and Remaining Useful Life Estimation for Solid Oxide Fuel Cells," Sustainability, MDPI, vol. 14(15), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9069-:d:870558
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    References listed on IDEAS

    as
    1. Liu, Hao & Chen, Jian & Hissel, Daniel & Su, Hongye, 2019. "Remaining useful life estimation for proton exchange membrane fuel cells using a hybrid method," Applied Energy, Elsevier, vol. 237(C), pages 910-919.
    2. Yan, Dong & Liang, Lingjiang & Yang, Jiajun & Zhang, Tao & Pu, Jian & Chi, Bo & Li, Jian, 2017. "Performance degradation and analysis of 10-cell anode-supported SOFC stack with external manifold structure," Energy, Elsevier, vol. 125(C), pages 663-670.
    3. Wu, Xiao-long & Xu, Yuan-Wu & Xue, Tao & Zhao, Dong-qi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2019. "Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment," Applied Energy, Elsevier, vol. 248(C), pages 126-140.
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    Cited by:

    1. Luka Žnidarič & Žiga Gradišar & Đani Juričić, 2024. "Predicting the Remaining Useful Life of Solid Oxide Fuel Cell Systems Using Adaptive Trend Models of Health Indicators," Energies, MDPI, vol. 17(11), pages 1-20, June.
    2. Petronilla Fragiacomo & Francesco Piraino & Matteo Genovese & Orlando Corigliano & Giuseppe De Lorenzo, 2023. "Experimental Activities on a Hydrogen-Powered Solid Oxide Fuel Cell System and Guidelines for Its Implementation in Aviation and Maritime Sectors," Energies, MDPI, vol. 16(15), pages 1-25, July.

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