TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

Umar Saleem, Wenjie Liu (), Saleem Riaz, Weilin Li, Ghulam Amjad Hussain (), Zeeshan Rashid and Zeeshan Ahmad Arfeen
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Umar Saleem: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Wenjie Liu: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Saleem Riaz: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Weilin Li: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Ghulam Amjad Hussain: College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates
Zeeshan Rashid: Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Zeeshan Ahmad Arfeen: Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Energies, 2024, vol. 17, issue 16, 1-24

Abstract: The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R 2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

Keywords: remaining useful life; transformer model; multi-head attention; lithium-ion battery; reliability; CALCE data; safety (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: 2024
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