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Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues

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dc.citedby14
dc.contributor.authorReza M.S.en_US
dc.contributor.authorMannan M.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorid59055914200en_US
dc.contributor.authorid57224923024en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid56997615100en_US
dc.contributor.authorid7103014445en_US
dc.date.accessioned2025-03-03T07:42:38Z
dc.date.available2025-03-03T07:42:38Z
dc.date.issued2024
dc.description.abstractThe remaining useful life (RUL) prediction of lithium-ion batteries (LIBs) plays a crucial role in battery management, safety assurance, and the anticipation of maintenance needs for reliable electric vehicle (EV) operation. An efficient prediction of RUL can ensure its safe operation and prevent both internal and external failures, as well as avoid any unwanted catastrophic events. However, achieving precise RUL prediction for electric vehicles presents a challenging task due to several issues related to intricate operational characteristics and dynamic shifts in model parameters throughout the aging process, battery parameters data extraction, data preprocessing, and hyperparameters tuning of the prediction model. This phenomenon significantly impacts the advancement of electric vehicle technology. To address these challenges, this study offers a comprehensive overview of various RUL prediction methods, presenting a comparative analysis of their outcomes, advantages, drawbacks, and associated research constraints. Emphasis is placed on the necessity of a battery management system (BMS) to ensure the safe and reliable functioning of LIBs. The review delves into crucial implementation factors, including battery test bench considerations, data selection, feature extraction, data preprocessing, performance evaluation indicators, and hyperparameter tuning. Additionally, the issues and challenges related to RUL prediction approaches such as; thermal runaway, material selection, cell balancing, battery aging, relaxation impact, training algorithms, data acquisition, and hyperparameter tuning were outlined to provide an in-depth understanding of the recent situations. The outcome of this review comprehensively examines various methods for predicting the RUL of LIB in EV applications, offering insights into their advantages, limitations, and research challenges. Recommendations for future trends in LIBs technology comprise enhancing prognostic accuracy and developing robust approaches to guarantee sustainable operation and management. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.egyr.2024.04.039
dc.identifier.epage4848
dc.identifier.scopus2-s2.0-85191656378
dc.identifier.spage4824
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85191656378&doi=10.1016%2fj.egyr.2024.04.039&partnerID=40&md5=48835e996161795ff599ce8aa744507f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36480
dc.identifier.volume11
dc.pagecount24
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEnergy Reports
dc.subjectBalancing
dc.subjectBattery management systems
dc.subjectData acquisition
dc.subjectElectric vehicles
dc.subjectExtraction
dc.subjectFeature extraction
dc.subjectForecasting
dc.subjectIons
dc.subjectTuning
dc.subjectBattery aging
dc.subjectData preprocessing
dc.subjectHyper-parameter
dc.subjectModel mechanisms
dc.subjectNetwork configuration
dc.subjectPrediction methods
dc.subjectRemaining useful life predictions
dc.subjectRemaining useful lives
dc.subjectTraining algorithms
dc.subjectVehicle applications
dc.subjectLithium-ion batteries
dc.titleRecent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issuesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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