Exploiting unlabeled data for battery state-of-health estimation using transformer-LSTM neural network with semi-supervised learning

Accurate estimation of the state of health (SOH) is critical to ensure the safe and reliable operation of lithium-ion batteries. Existing SOH estimation methods are typically based on supervised learning and require large-scale labeled battery aging datasets. However, the acquisition of accurate dat...

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Bibliografische Detailangaben

Hauptverfasser:	Yue Dong, Guangcai Zhao, Zhangang Yang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Elsevier 2026-02-01
Schriftenreihe:	Future Batteries
Schlagworte:	Lithium-ion battery State-of-health (SOH) Transformer Unlabeled data Semi-supervised learning (SSL)
Online-Zugang:	http://www.sciencedirect.com/science/article/pii/S2950264025001121
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