Open Access
Volume 41, Number 3, June 2023
Page(s) 464 - 470
Published online 01 August 2023
  1. HOU Ming, YI Baolian. Progress and perspective of fuel cell technology[J]. Journal of Electrochemistry, 2012, 18(1): 1–13 [Article] (in Chinese) [Google Scholar]
  2. SHAO Zhigang, YI Baolian. Developing trend and present status of hydrogen energy and fuel cell development[J]. Bulletin of Chinese Academy of Sciences, 2019, 34(4): 469–477 [Article] (in Chinese) [Google Scholar]
  3. FUTTER G A, LATZ A, JAHNKE T. Physical modeling of chemical membrane degradation in polymer electrolyte membrane fuel cells: influence of pressure, relative humidity and cell voltage[J]. Journal of Power Sources, 2019, 410: 78–90 [CrossRef] [Google Scholar]
  4. HU Z, XU L, LI J, et al. A reconstructed fuel cell life-prediction model for a fuel cell hybrid city bus[J]. Energy Conversion & Management, 2018, 156: 723–732 [CrossRef] [Google Scholar]
  5. BRESSEL M, HILAIRET M, HISSEL D, et al. Remaining useful life prediction and uncertainty quantification of proton exchange membrane fuel cell under variable load[J]. IEEE Trans on Industrial Electronics, 2016, 63(4): 2569–2577 [Article] [CrossRef] [Google Scholar]
  6. BRESSEL M, HILAIRET M, HISSEL D, et al. Extended Kalman filter for prognostic of proton exchange membrane fuel cell[J]. Applied Energy, 2016, 164: 220 [Article] [CrossRef] [Google Scholar]
  7. MA R, YANG T, BREAZ E, et al. Data-driven proton exchange membrane fuel cell degradation predication through deep learning method[J]. Applied Energy, 2018, 231: 102–115 [Article] [CrossRef] [Google Scholar]
  8. LI Z, ZHENG Z, OUTBIB R. Adaptive prognostic of fuel cells by implementing ensemble echo state networks in time-varying model space[J]. IEEE Trans on Industrial Electronics, 2020, 67(1): 379–389 [Article] [CrossRef] [Google Scholar]
  9. CHEN K, LAGHROUCHE S, DJERDIR A. Health state prognostic of fuel cell based on wavelet neural network and cuckoo search algorithm[J]. ISA Transactions, 2021(113): 175–184 [CrossRef] [Google Scholar]
  10. HUA Z, ZHENG Z, MARIE-CÉCILE PÉRA, et al. Remaining useful life prediction of PEMFC systems based on the multi-input echo state network[J]. Applied Energy, 2020, 265: 114791 [Article] [CrossRef] [Google Scholar]
  11. LIUH, CHEN J, HISSEL D, et al. Remaining useful life estimation for proton exchange membrane fuel cells using a hybrid method[J]. Applied Energy, 2019, 237: 910–919 [Article] [CrossRef] [Google Scholar]
  12. WANG Y, WUK, ZHAO H. Degradation prediction of proton exchange membrane fuel cell stack using semi-empirical and data-driven methods[J]. Energy and AI, 2023, 11: 100205 [CrossRef] [Google Scholar]
  13. MORANDO S, JEMEI S, HISSEL D, et al. ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network[J]. Mathematics & Computers in Simulation, 2017, 131: 283–294 [Google Scholar]
  14. YANG Shuzi, WU Ya, XUAN Jianping. Time series analysis in engineering application[M]. 2nd ed. Wuhan: Huazhong University of Science and Technology Press, 2007 (in Chinese) [Google Scholar]
  15. JIHYUN K, LE T, HOWON K. Nonintrusive load monitoring based on advanced deep learning and novel signature[J]. Computational Intelligence & Neuroscience, 2017, 2017: 4216281 [Google Scholar]

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