Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

[Display omitted] •Test optimization for Li-ion battery formulations reduces the cost of testing.•Deep learning is used to predict battery lifespan in high-temperature testing.•The transfer learning method is based on a stacked denoising autoencoder.•A modified Arrhenius model is used for standard-t...

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Bibliographic Details
Published in:	Applied energy Vol. 262; p. 114490
Main Authors:	Ma, Jian, Xu, Shu, Shang, Pengchao, ding, Yu, Qin, Weili, Cheng, Yujie, Lu, Chen, Su, Yuzhuan, Chong, Jin, Jin, Haizu, Lin, Yongshou
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.03.2020
Subjects:	Arrhenius model batteries business enterprises Cycle life test optimization Deep learning heat stress Li-ion power battery lithium prediction Remaining useful life prediction temperature Transfer learning Deep learning Remaining useful life prediction Cycle life test optimization Li-ion power battery Transfer learning Arrhenius model
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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