A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This article applies advanced machine-learning techniques to achieve effective future capacities and RUL predict...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 68; H. 4; S. 3170 - 3180
Hauptverfasser:	Liu, Kailong, Shang, Yunlong, Ouyang, Quan, Widanage, Widanalage Dhammika
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Battery cycles data-driven approach Decomposition Degradation Diagnosis Electric vehicles (EVs) Gaussian process Ground penetrating radar Lithium lithium-ion (Li-ion) batteries Lithium-ion batteries Machine learning Predictions Predictive models Rechargeable batteries Reliability remaining useful life (RUL) Uncertainty uncertainty management Useful life
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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