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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on vehicular technology Ročník 67; číslo 7; s. 5695 - 5705
Hlavní autori:	Zhang, Yongzhi, Xiong, Rui, He, Hongwen, Pecht, Michael G.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!