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Degradation pattern prediction of a polymer electrolyte membrane fuel cell stack with series reliability structure via durability data of single cells

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  • Bae, Suk Joo
  • Kim, Seong-Joon
  • Lee, Jin-Hwa
  • Song, Inseob
  • Kim, Nam-In
  • Seo, Yongho
  • Kim, Ki Buem
  • Lee, Naesung
  • Park, Jun-Young

Abstract

The insufficient long-term durability of polymer electrolyte membrane fuel cell (PEMFC) stacks has been blocking commercialization of PEMFC technologies. An accelerated degradation test (ADT) is needed to facilitate the PEMFC development process by reducing the testing time. We propose an ADT procedure for a PEMFC stack with the concept of series reliability structure under startup–shutdown cycling testing conditions. The acceleration factor is estimated to fit the degradation paths of individual cells consisting of the PEMFC stack under normal use conditions via the accelerated degradation data of a single cell. We employ a nonparametric regression method to smooth the degradation curves observed from accelerated operating conditions. We illustrate the methodology for estimating the lifetime of the PEMFC stack using the theory of the smallest-order statistics. We propose a three-parameter Weibull distribution in fuel cell technology to fit the failure data of cells in a PEMFC stack.

Suggested Citation

  • Bae, Suk Joo & Kim, Seong-Joon & Lee, Jin-Hwa & Song, Inseob & Kim, Nam-In & Seo, Yongho & Kim, Ki Buem & Lee, Naesung & Park, Jun-Young, 2014. "Degradation pattern prediction of a polymer electrolyte membrane fuel cell stack with series reliability structure via durability data of single cells," Applied Energy, Elsevier, vol. 131(C), pages 48-55.
  • Handle: RePEc:eee:appene:v:131:y:2014:i:c:p:48-55
    DOI: 10.1016/j.apenergy.2014.05.064
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    References listed on IDEAS

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    Cited by:

    1. Saralees Nadarajah & Xiao Jiang & Jeffrey Chu, 2017. "Comparisons of smallest order statistics from Pareto distributions with different scale and shape parameters," Annals of Operations Research, Springer, vol. 254(1), pages 191-209, July.
    2. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    3. Alegre, Cinthia & Lozano, Antonio & Manso, Ángel Pérez & Álvarez-Manuel, Laura & Marzo, Florencio Fernández & Barreras, Félix, 2019. "Single cell induced starvation in a high temperature proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 250(C), pages 1176-1189.
    4. Pei, Pucheng & Chen, Dongfang & Wu, Ziyao & Ren, Peng, 2019. "Nonlinear methods for evaluating and online predicting the lifetime of fuel cells," Applied Energy, Elsevier, vol. 254(C).
    5. Yuan, Tao & Wu, Xinying & Bae, Suk Joo & Zhu, Xiaoyan, 2019. "Reliability assessment of a continuous-state fuel cell stack system with multiple degrading components," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 157-164.
    6. Wang, Junye, 2015. "Barriers of scaling-up fuel cells: Cost, durability and reliability," Energy, Elsevier, vol. 80(C), pages 509-521.
    7. Liu, Ze & Xu, Sichuan & Zhao, Honghui & Wang, Yupeng, 2022. "Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: Development and evaluation of machine learning models," Applied Energy, Elsevier, vol. 326(C).
    8. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    9. Zhang, Caizhi & Zeng, Tao & Wu, Qi & Deng, Chenghao & Chan, Siew Hwa & Liu, Zhixiang, 2021. "Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading," Renewable Energy, Elsevier, vol. 179(C), pages 929-944.
    10. Lorenzo, Charles & Bouquain, David & Hibon, Samuel & Hissel, Daniel, 2021. "Synthesis of degradation mechanisms and of their impacts on degradation rates on proton-exchange membrane fuel cells and lithium-ion nickel–manganese–cobalt batteries in hybrid transport applicati," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    11. Hu, Zunyan & Xu, Liangfei & Huang, Yiyuan & Li, Jianqiu & Ouyang, Minggao & Du, Xiaoli & Jiang, Hongliang, 2018. "Comprehensive analysis of galvanostatic charge method for fuel cell degradation diagnosis," Applied Energy, Elsevier, vol. 212(C), pages 1321-1332.
    12. Islam, Mohammad Rafiqul & Shabani, Bahman & Rosengarten, Gary, 2016. "Nanofluids to improve the performance of PEM fuel cell cooling systems: A theoretical approach," Applied Energy, Elsevier, vol. 178(C), pages 660-671.
    13. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    14. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.

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