Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries describe the current aging degree of the batteries from different perspectives, and accurate and efficient battery health estimation is essential for their safe use. To improve the effectiveness and accuracy of the ba...

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Veröffentlicht in:	Journal of algorithms & computational technology Jg. 16
Hauptverfasser:	Zhu, Zhenyu, Yang, Qing, Liu, Xin, Gao, Dexin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London, England SAGE Publications 01.10.2022 Sage Publications Ltd SAGE Publishing
Schlagworte:	Accuracy Artificial neural networks Battery cycles Degradation Life cycle engineering Lithium Lithium-ion batteries Rechargeable batteries Lithium-ion batteries remaining useful life estimation state of health attention mechanism hybrid neural network
ISSN:	1748-3018, 1748-3026
Online-Zugang:	Volltext
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