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Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA

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  • Chen, Hong
  • Zhan, Zhigang
  • Jiang, Panxing
  • Sun, Yahao
  • Liao, Liwen
  • Wan, Xiongbiao
  • Du, Qing
  • Chen, Xiaosong
  • Song, Hao
  • Zhu, Ruijie
  • Shu, Zhanhong
  • Li, Shang
  • Pan, Mu

Abstract

Understanding the degradation mechanism and accurate prediction of the remaining useful life (RUL) of proton exchange membrane fuel cells (FCs) can provide guidance for improving durability and reducing their operation and maintenance costs. In this paper, we measured the actual lifetime of an FC for more than 7000 h until the end of its life and developed an RUL model based on the degradation mechanism. The particle filter (PF) algorithm was used to eliminate the influence of random errors on the key model parameters and on the prediction of RUL. RUL prediction was carried out and the contributions of key factors to FC voltage degradation were calculated and analyzed. The results demonstrated that the RUL of the FC could be accurately predicted with the RUL model and the PF algorithm. Over the lifetime of the FC, catalyst degradation was responsible for the majority of FC voltage degradation, contributing as much as 84. 3% of the performance degradation. In the latter stage of the FC lifetime, the rapid increase in leakage current caused by membrane degradation and the increase in MEA mass transfer resistance became increasingly significant, with their voltage degradation contribution rates increasing from 0.12% to 35.19% and from −4.04% to 14.32%, respectively. The influence of evolutions to the internal resistance on FC performance degradation was determined to be negligible.

Suggested Citation

  • Chen, Hong & Zhan, Zhigang & Jiang, Panxing & Sun, Yahao & Liao, Liwen & Wan, Xiongbiao & Du, Qing & Chen, Xiaosong & Song, Hao & Zhu, Ruijie & Shu, Zhanhong & Li, Shang & Pan, Mu, 2022. "Whole life cycle performance degradation test and RUL prediction research of fuel cell MEA," Applied Energy, Elsevier, vol. 310(C).
  • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000423
    DOI: 10.1016/j.apenergy.2022.118556
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    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. Gong, Zhichao & Wang, Bowen & Xu, Yifan & Ni, Meng & Gao, Qingchen & Hou, Zhongjun & Cai, Jun & Gu, Xin & Yuan, Xinjie & Jiao, Kui, 2022. "Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle," Applied Energy, Elsevier, vol. 325(C).
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    5. Fan, Lixin & liu, Yang & Luo, Xiaobing & Tu, Zhengkai & Chan, Siew Hwa, 2023. "A novel gas supply configuration for hydrogen utilization improvement in a multi-stack air-cooling PEMFC system with dead-ended anode," Energy, Elsevier, vol. 282(C).

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