An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capabili...

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Veröffentlicht in:	Electronics (Basel) Jg. 13; H. 8; S. 1423
Hauptverfasser:	Zhang, Wenwen, Jia, Jianfang, Pang, Xiaoqiong, Wen, Jie, Shi, Yuanhao, Zeng, Jianchao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.04.2024
Schlagworte:	Accuracy Algorithms Analysis Artificial intelligence Artificial neural networks Datasets Deep learning Discharge Electric transformers Electric vehicles Electrolytes Feature extraction Integrated approach Life prediction Lithium Lithium-ion batteries Machine learning Neural networks Noise reduction Prediction models Principal components analysis Rechargeable batteries Time series Transformers Trends Wavelet transforms
ISSN:	2079-9292, 2079-9292
Online-Zugang:	Volltext
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