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Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework.

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Title: Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework.
Authors: Mohamud, Nur Mohamed1,2, Ayob, Shahrin Md1,2, Saimon, Siti Mahfuza1,3, Nahhas, Ahmed M.3,4, Arfeen, Zeeshan Ahmad4,5, m.aman@ubt.edu.sa, Masud, Muhammad I.5,6, Aman, Mohammed1,6
Source: Batteries; Jun2026, Vol. 12 Issue 6, p210, 34p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 194951797
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Batteries%22">Batteries</searchLink>; Jun2026, Vol. 12 Issue 6, p210, 34p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=194951797
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        Value: 10.3390/batteries12060210
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      – Code: eng
        Text: English
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        StartPage: 210
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      – TitleFull: Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework.
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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