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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής

Κύριοι συγγραφείς:	Yue Dong, Guangcai Zhao, Zhangang Yang
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	Elsevier 2026-02-01
Σειρά:	Future Batteries
Θέματα:	Lithium-ion battery State-of-health (SOH) Transformer Unlabeled data Semi-supervised learning (SSL)
Διαθέσιμο Online:	http://www.sciencedirect.com/science/article/pii/S2950264025001121
Ετικέτες:	Προσθήκη ετικέτας Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!