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A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control

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  • Sun, Li
  • Li, Guanru
  • Hua, Q.S.
  • Jin, Yuhui

Abstract

The cooling of the open-cathode proton exchange membrane fuel cell (PEMFC) is critical for the operational safety and overall efficiency. However, its control is challenging because of the model uncertainties and frequent disturbances caused by the power adjustment. To this end, this paper proposes a hybrid cooling control strategy by combining the merits of the model-based and data-driven methods. Firstly, a simplified nonlinear mechanistic model is used to exhibit the dynamic perturbations in terms of the different fan speeds and power conditions. Secondly, a modified active disturbance rejection control (ADRC) is developed by incorporating an identified nominal linear model into extended state observer. The external disturbances and the internal uncertainties beyond the nominal model are lumped as a total term, which will be estimated and mitigated in a real-time data-driven manner. The simulation results show that the proposed hybrid method is able to give a faster response with stronger robustness and less noise sensitivity against the uncertainties than the conventional PI and ADRC methods. The experimental test on a 500W open-cathode PEMFC verifies the simulation merits in both set-point tracking and disturbance rejection, depicting a promising prospect of the proposed hybrid method in the open-cathode PEMFC cooling control practice.

Suggested Citation

  • Sun, Li & Li, Guanru & Hua, Q.S. & Jin, Yuhui, 2020. "A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control," Renewable Energy, Elsevier, vol. 147(P1), pages 1642-1652.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1642-1652
    DOI: 10.1016/j.renene.2019.09.048
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    References listed on IDEAS

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    Cited by:

    1. Deng, Zhihua & Chen, Qihong & Zhang, Liyan & Zhou, Keliang & Zong, Yi & Fu, Zhichao & Liu, Hao, 2021. "Data-driven reconstruction of interpretable model for air supply system of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 299(C).
    2. Rahimi, Mohammad & Abbaspour-Fard, Mohammad Hossein & Rohani, Abbas, 2021. "A multi-data-driven procedure towards a comprehensive understanding of the activated carbon electrodes performance (using for supercapacitor) employing ANN technique," Renewable Energy, Elsevier, vol. 180(C), pages 980-992.
    3. Deng, Bo & Huang, Wentao & Jian, Qifei, 2023. "An open-cathode PEMFC efficiency optimization strategy based on exergy analysis and data-driven modeling," Energy, Elsevier, vol. 264(C).
    4. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Won, Jinyeon & Oh, Hwanyeong & Hong, Jongsup & Kim, Minjin & Lee, Won-Yong & Choi, Yoon-Young & Han, Soo-Bin, 2021. "Hybrid diagnosis method for initial faults of air supply systems in proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 180(C), pages 343-352.
    6. Pei Cai & Youxue Jiang & He Wang & Liangyu Wu & Peng Cao & Yulong Zhang & Feng Yao, 2020. "Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    7. Qianchao Wang & Hongcan Xu & Lei Pan & Li Sun, 2020. "Active Disturbance Rejection Control of Boiler Forced Draft System: A Data-Driven Practice," Sustainability, MDPI, vol. 12(10), pages 1-18, May.

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