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Degradation trajectories prognosis for fuel cell based on MP-NBEATS
ID：31 View Protection：ATTENDEE Updated Time：2023-11-20 13:45:34 Hits：977 Oral Presentation

Start Time：2023-12-10 11:15（Asia/Shanghai）

Duration：15min

Session：S10 Electric Machine Design and control » S10Electric Machine Design and control

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Abstract
The performance degradation trajectory of fuel cells has strong nonlinear characteristics, and accurate and efficient long-term prediction of fuel cell performance degradation is of great significance to protect the safe operation of batteries. Since long-term forecasting of time series is difficult to predict its trend and fluctuation, this paper proposes a multi periodic neural basis expansion analysis for interpretable time series forecasting (MP-NBEATS). This method obtains multiple periods of the series by decomposing the voltage series, and integrates the prediction results of neural basis expansion analysis for interpretable time series forecasting (NBEATS) under different periods, and finally realizes long-term prediction. Compared with the traditional method, this method can better predict the trend and seasonal characteristics of the time series. Finally, through experimental verification, the error of the proposed method can reach 0.983%.
Keywords
Fuel cell,Degradation trajectories,MP-NBEATS
Speaker
Yuxuan Zheng
Mr. University of Electronic Science and Technology of China

Submission Author
Yuxuan Zheng University of Electronic Science and Technology of China
Huiwen Deng Sichuan Energy Industry Investment Group CO, LTD,
Jianjun Chen University of Electronic Science and Technology of China
Jiaxiang Hu University of Electronic Science and Technology of China
Weihao Hu University of Electronic Science and Technology of China
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Important Date

  • Conference Date

    Dec 08

    2023

    to

    Dec 10

    2023

  • Nov 01 2023

    Draft paper submission deadline

  • Dec 10 2023

    Registration deadline

Sponsored By
IEEE IAS
Organized By
Southwest Jiaotong University (SWJTU)
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