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Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

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dc.citedby3
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorAnsari S.en_US
dc.contributor.authorMiah M.S.en_US
dc.contributor.authorMeraj S.T.en_US
dc.contributor.authorHasan K.en_US
dc.contributor.authorShihavuddin A.S.M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57218906707en_US
dc.contributor.authorid57226266149en_US
dc.contributor.authorid57202610180en_US
dc.contributor.authorid57205215021en_US
dc.contributor.authorid25960374400en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid55582332500en_US
dc.contributor.authorid57208481391en_US
dc.date.accessioned2023-05-29T09:36:07Z
dc.date.available2023-05-29T09:36:07Z
dc.date.issued2022
dc.descriptionAutomotive industry; Charging (batteries); Deep learning; Digital storage; Electric vehicles; Health; Learning systems; Secondary batteries; And electric vehicle; Charge state; Deep learning; Electric vehicle batteries; Life estimation; Method implementations; Remaining useful lives; State of health; States of charges; System methods; Battery management systemsen_US
dc.description.abstractState of Charge (SOC), state of health (SOH), and remaining useful life (RUL) are the crucial indexes used in the assessment of electric vehicle (EV) battery management systems (BMS). The performance and efficiency of EVs are subject to the precise estimation of SOC, SOH, and RUL in BMS which enhances the battery reliability, safety, and longevity. However, the estimation of SOC, SOH, and RUL is challenging due to the battery capacity degradation and varying environmental conditions. Recently, deep learning (DL) has received wide attention for battery SOC, SOH, and RUL estimation due to the accessibility of a vast amount of data, large storage volume, and powerful computing processors. Nevertheless, the application of DL in SOC, SOH, and RUL estimation for EVs is still limited. Therefore, the novelty of this paper is to deliver a comprehensive review of DL-enabled SOC, SOH, and RUL estimation for BMS, focusing on methods, implementations, strengths, weaknesses, issues, accuracy, and contributions. Moreover, this study explores the numerous important implementation factors of DL methods concerning data type, features, size, preprocessing, algorithm operation, functions, hyperparameter adjustments, and performance evaluation. Additionally, the review explores various limitations and challenges of DL in BMS related to battery, algorithm, and operational issues. Finally, future opportunities and prospects are delivered that would support the EV engineers and automotive industries to establish an accurate and robust DL-based SOC, SOH, and RUL estimation technique towards smart BMS in future sustainable EV applications. � 2022 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo105752
dc.identifier.doi10.1016/j.est.2022.105752
dc.identifier.scopus2-s2.0-85138478861
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138478861&doi=10.1016%2fj.est.2022.105752&partnerID=40&md5=845dc6867ce01092b963946aaa457502
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26665
dc.identifier.volume55
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.titleDeep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospectsen_US
dc.typeReviewen_US
dspace.entity.typePublication
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