Life prediction of lithium-ion batteries based on stacked denoising autoencoders

•Introduce the CFS clustering model for feature selection.•Using the SDAE for life prediction at early life stage.•Extracting the original features using discharge capacity and temperature only. Accurate life prediction of lithium-ion batteries is important to help assess battery quality in advance,...

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Veröffentlicht in:	Reliability engineering & system safety Jg. 208; S. 107396
Hauptverfasser:	Xu, Fan, Yang, Fangfang, Fei, Zicheng, Huang, Zhelin, Tsui, Kwok-Leung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Barking Elsevier Ltd 01.04.2021 Elsevier BV
Schlagworte:	Battery cycles Clustering Clustering by fast search Computer architecture Deep learning Feature extraction Life cycles Life prediction Lithium Lithium-ion batteries Lithium-ion battery Noise reduction Predictions Product safety Quality assessment Rechargeable batteries Reliability engineering Stacked denoising autoencoder Life prediction Lithium-ion battery LSTM Stacked denoising autoencoder DAE SVM RMSE SOH BP Deep learning MAE CFS Clustering by fast search SDAE RF SAE
ISSN:	0951-8320, 1879-0836
Online-Zugang:	Volltext
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