IEEE Access (Jan 2022)

Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

  • Daoquan Chen,
  • Weicong Hong,
  • Xiuze Zhou

DOI
https://doi.org/10.1109/ACCESS.2022.3151975
Journal volume & issue
Vol. 10
pp. 19621 – 19628

Abstract

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Accurately predicting the Remaining Useful Life (RUL) of a Li-ion battery plays an important role in managing the health and estimating the state of a battery. With the rapid development of electric vehicles, there is an increasing need to develop and improve the techniques for predicting RUL. To predict RUL, we designed a Transformer-based neural network. First, battery capacity data is always full of noise, especially during battery charge/discharge regeneration. To alleviate this problem, we applied a Denoising Auto-Encoder (DAE) to process raw data. Then, to capture temporal information and learn useful features, a reconstructed sequence was fed into a Transformer network. Finally, to bridge denoising and prediction tasks, we combined these two tasks into a unified framework. Results of extensive experiments conducted on two data sets and a comparison with some existing methods show that our proposed method performs better in predicting RUL. Our projects are all open source and are available at https://github.com/XiuzeZhou/RUL.

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