Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 67; H. 7; S. 5695 - 5705
Hauptverfasser:	Zhang, Yongzhi, Xiong, Rui, He, Hongwen, Pecht, Michael G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Back propagation Computer simulation Data models deep learning Degradation Electric cells Learning Life prediction Lithium Lithium-ion batteries Lithium-ion battery long short-term memory Mathematical model Mathematical models Monte Carlo simulation Neural networks Predictive models Rechargeable batteries Recurrent neural networks Reliability analysis remaining useful life Support vector machines Useful life
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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