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Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

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dc.citedby102
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorSaad M.H.en_US
dc.contributor.authorDong Z.Y.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid56997615100en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid56608244300en_US
dc.date.accessioned2023-05-29T08:06:57Z
dc.date.available2023-05-29T08:06:57Z
dc.date.issued2020
dc.description.abstractState of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions. � 2020, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4687
dc.identifier.doi10.1038/s41598-020-61464-7
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85081926043
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081926043&doi=10.1038%2fs41598-020-61464-7&partnerID=40&md5=115a4af10b3e38c0fb7558ab581a49ab
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25144
dc.identifier.volume10
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleToward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniquesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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