Multifeature‐based online remaining useful life prediction of lithium‐ion batteries in stages using cascaded data‐driven algorithm

Accurately predicting the remaining useful life (RUL) is crucial for the safety and stability of battery systems. Considering the inherent challenges in directly measuring the capacity of lithium‐ion batteries during operation, this paper proposes an online hybrid cascaded data‐driven prediction alg...

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Veröffentlicht in:	Quality and reliability engineering international Jg. 40; H. 5; S. 2527 - 2546
Hauptverfasser:	Tao, Liujun, Wu, Huaiyu, Zheng, Xiujuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bognor Regis Wiley Subscription Services, Inc 01.07.2024
Schlagworte:	Algorithms Artificial neural networks cascaded hybrid method Correlation analysis Errors health indicators Life prediction Lithium Lithium-ion batteries lithium‐ion battery Machine learning Prediction models Principal components analysis Redundancy remaining useful life Search algorithms Useful life
ISSN:	0748-8017, 1099-1638
Online-Zugang:	Volltext
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