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

By Daoquan Chen, Wei-Cheng Hong, Xiuze Zhou. IEEE Access, 2022

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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.

Subfields: Automotive Engineering, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/82e24b3050046c8403b49100c6eee3a772fd1a68