Show simple item record

State of Health Estimation for Lithium-Ion Batteries Using Multiple Indirect Feature Extraction and Bayesian Optimization–Transformer Model

dc.contributor.authorLi, Cong
dc.contributor.authorPan, Rui
dc.contributor.authorSi, Ruibin
dc.contributor.authorLiu, Quanfeng
dc.contributor.authorZhou, Juan
dc.date.accessioned2026-08-20T11:18:09Z
dc.date.available2026-08-20T11:18:09Z
dc.date.copyright2025/12/26
dc.date.issued2026
dc.identifier.otherJLEED9.EYENG-6176.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311997
dc.description.abstractAbstractAccurate estimation of the state of health (SOH) of lithium-ion batteries is a critical prerequisite for ensuring their safe and stable operation. However, the complex operating environments in practical applications often result in raw data ...
dc.publisherAmerican Society of Civil Engineers
dc.titleState of Health Estimation for Lithium-Ion Batteries Using Multiple Indirect Feature Extraction and Bayesian Optimization–Transformer Model
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue2
dc.journal.titleJournal of Energy Engineering
dc.identifier.doi10.1061/JLEED9.EYENG-6176
dc.journal.fristpage04025118-1
dc.journal.lastpage04025118-11
dc.page11
dc.treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 002
dc.contenttypeFulltext


Files in this item

Thumbnail
Name:
JLEED9.EYENG-6176.pdf
Size:
2.443Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record