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Advanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectives

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dc.citedby2
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorAnsari S.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorUstun T.S.en_US
dc.contributor.authorKalam A.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid58562396100en_US
dc.contributor.authorid57218906707en_US
dc.contributor.authorid57216824752en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid43761679200en_US
dc.contributor.authorid55543249600en_US
dc.date.accessioned2025-03-03T07:41:25Z
dc.date.available2025-03-03T07:41:25Z
dc.date.issued2024
dc.description.abstractHazards in electric vehicles (EVs) often stem from lithium-ion battery (LIB) packs during operation, aging, or charging. Robust early fault diagnosis algorithms are essential for enhancing safety, efficiency, and reliability. LIB fault types involve internal batteries, sensors, actuators, and system faults, managed by the battery management system (BMS), which handles state estimation, cell balancing, thermal management, and fault diagnosis. Prompt identification and isolation of defective cells, coupled with early warning measures, are critical for safety. This review explores data-driven methods for fault diagnosis in LIB management systems, covering implementation, classification, fault types, and feature extraction. It also discusses BMS roles, sensor types, challenges, and future trends. The findings aim to guide researchers and the automotive industry in advancing fault diagnosis methods to support sustainable EV transportation. ? 2024 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100374
dc.identifier.doi10.1016/j.etran.2024.100374
dc.identifier.scopus2-s2.0-85207692948
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207692948&doi=10.1016%2fj.etran.2024.100374&partnerID=40&md5=69c07496ee53e519d8a3ec94b2329de6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36126
dc.identifier.volume22
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleeTransportation
dc.subjectBattery management systems
dc.subjectBattery Management
dc.subjectData driven
dc.subjectData-driven fault diagnosis
dc.subjectFault types
dc.subjectFaults diagnosis
dc.subjectFuture perspectives
dc.subjectIon batteries
dc.subjectLithium ions
dc.subjectMachine-learning
dc.subjectManagement systems
dc.subjectBattery Pack
dc.titleAdvanced data-driven fault diagnosis in lithium-ion battery management systems for electric vehicles: Progress, challenges, and future perspectivesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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