A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium‐ion batteries

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predictin...

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Bibliographic Details
Published in:	Energy science & engineering Vol. 12; no. 8; pp. 3390 - 3400
Main Authors:	Xia, Wei, Xu, Jinli, Liu, Baolei, Duan, Huiyun
Format:	Journal Article
Language:	English
Published:	London John Wiley & Sons, Inc 01.08.2024 Wiley
Subjects:	Algorithms Artificial neural networks CNN‐BiGRU Data acquisition Degradation denoising autoencoder Design Effectiveness Electric vehicles Life prediction Lithium Lithium batteries Lithium-ion batteries Methods Neural networks Noise reduction Prediction models reconstruction loss remaining useful life Useful life
ISSN:	2050-0505, 2050-0505
Online Access:	Get full text
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