Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Yunlong Han, Conghui Li, Linfeng Zheng, Gang Lei and Li Li ()
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Yunlong Han: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Conghui Li: Zibo Vocational Institute, Zibo 255314, China
Linfeng Zheng: Institute of Rail Transportation, Jinan University, Zhuhai 510632, China
Gang Lei: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Li Li: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia

Energies, 2023, vol. 16, issue 17, 1-16

Abstract: In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R 2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.

Keywords: Li-ion battery; remaining useful life; transformer; residual learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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