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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Vydané v:	IEEE transactions on industrial electronics (1982) Ročník 68; číslo 4; s. 3170 - 3180
Hlavní autori:	Liu, Kailong, Shang, Yunlong, Ouyang, Quan, Widanage, Widanalage Dhammika
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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