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Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions

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  • Shao, Qian
  • Gao, Enlai
  • Mara, Thierry
  • Hu, Heng
  • Liu, Tong
  • Makradi, Ahmed

Abstract

Uncertainties that commonly exist in mathematical models prevent accurate predictions of solid oxide fuel cell performances and consequently impede the development and application of solid oxide fuel cell technologies. Assessing the impact of uncertain input parameters on cell performance variability is of utmost importance to the improvement of fuel cell models. To this end, a global sensitivity analysis is performed on the electrochemical model of a fuel cell using the Bayesian sparse polynomial chaos expansion approach. With this approach, machine-learning models are constructed to approximate the input-output relationship of the electrochemical model. The first-order, second-order, and total Sobol’ indices are then computed analytically to quantify the individual impact of each parameter, the pairwise interaction between them and the total coupling effects over the entire input parameter space. These sensitivity indices show that the kinetic parameters of the electrochemical reaction, such as the activation energy, pre-exponential coefficients, and the electronic transfer coefficient, are the most sensitive parameters that significantly contribute to the variation of cell output voltage, which indicates the requirement for in-depth investigations of these parameters to enhance the accuracy in fuel cell model predictions. This work uncovers the possibility to apply data science techniques to the field of fuel cells. The results of this study not only demonstrate the effectiveness of the Bayesian approach for performing sensitivity analysis on the electrochemical model of a fuel cell, but also shed light on the rational design and optimization of solid oxide fuel cells.

Suggested Citation

  • Shao, Qian & Gao, Enlai & Mara, Thierry & Hu, Heng & Liu, Tong & Makradi, Ahmed, 2020. "Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions," Applied Energy, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919320057
    DOI: 10.1016/j.apenergy.2019.114318
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    References listed on IDEAS

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    Cited by:

    1. Li, Zheng & Yu, Jie & Wang, Chen & Bello, Idris Temitope & Yu, Na & Chen, Xi & Zheng, Keqing & Han, Minfang & Ni, Meng, 2024. "Multi-objective optimization of protonic ceramic electrolysis cells based on a deep neural network surrogate model," Applied Energy, Elsevier, vol. 365(C).
    2. Fan, Ruijia & Chang, Guofeng & Xu, Yiming & Xu, Jiamin, 2024. "Investigating and quantifying the effects of catalyst layer gradients, operating conditions, and their interactions on PEMFC performance through global sensitivity analysis," Energy, Elsevier, vol. 290(C).
    3. Kannan, Vishvak & Xue, Hansong & Raman, K. Ashoke & Chen, Jiasheng & Fisher, Adrian & Birgersson, Erik, 2020. "Quantifying operating uncertainties of a PEMFC – Monte Carlo-machine learning based approach," Renewable Energy, Elsevier, vol. 158(C), pages 343-359.
    4. Giugno, Andrea & Mantelli, Luca & Cuneo, Alessandra & Traverso, Alberto, 2020. "Performance analysis of a fuel cell hybrid system subject to technological uncertainties," Applied Energy, Elsevier, vol. 279(C).
    5. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).

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