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Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks.

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Title: Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks.
Authors: Zhao, Jingyuan1, jyzhao@ucdavis.edu, Qu, Xudong2, Li, Yuqi3, Nan, Jinrui4, Burke, Andrew F.1
Source: Energy; Aug2025, Vol. 328, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 185486546
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Energy%22">Energy</searchLink>; Aug2025, Vol. 328, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=185486546
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.energy.2025.136618
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      – Code: eng
        Text: English
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      – TitleFull: Real-time prediction of battery remaining useful life using hybrid-fusion deep neural networks.
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            NameFull: Zhao, Jingyuan
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            NameFull: Qu, Xudong
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            NameFull: Li, Yuqi
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              Text: Aug2025
              Type: published
              Y: 2025
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              Value: 328
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