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Physics-Informed Temperature Prediction of Lithium-Ion Batteries Using Decomposition-Enhanced LSTM and BiLSTM Models.

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Title: Physics-Informed Temperature Prediction of Lithium-Ion Batteries Using Decomposition-Enhanced LSTM and BiLSTM Models.
Authors: Madani, Seyed Saeed1, Shabeer, Yasmin2, Fowler, Michael2,3, Panchal, Satyam2,4, Ziebert, Carlos3,5, Chaoui, Hicham1,4,5, Allard, François1,2
Source: World Electric Vehicle Journal; Jan2026, Vol. 17 Issue 1, p2, 40p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191303910
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22World+Electric+Vehicle+Journal%22">World Electric Vehicle Journal</searchLink>; Jan2026, Vol. 17 Issue 1, p2, 40p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191303910
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/wevj17010002
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      – Code: eng
        Text: English
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        PageCount: 40
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      – TitleFull: Physics-Informed Temperature Prediction of Lithium-Ion Batteries Using Decomposition-Enhanced LSTM and BiLSTM Models.
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              Text: Jan2026
              Type: published
              Y: 2026
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