Publication:
Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

Simple item page
dc.citedby120
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorKarim T.F.en_US
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid36518950900en_US
dc.contributor.authorid57212923888en_US
dc.date.accessioned2023-05-29T08:06:37Z
dc.date.available2023-05-29T08:06:37Z
dc.date.issued2020
dc.descriptionAutomotive batteries; Charging (batteries); Digital storage; Electric automobiles; Fossil fuels; Fuel storage; Ions; Lithium-ion batteries; Battery storage system; Data-driven algorithm; Detailed classification; Evaluation indicators; Global carbon emission; High energy densities; State-of-charge estimation; Vehicle applications; Battery management systemsen_US
dc.description.abstractGlobal carbon emissions caused by fossil fuels and diesel-based vehicles have urged the necessity to move toward the development of electric vehicles and related battery storage systems. Lithium-ion batteries are the ideal candidate for electric vehicle due to their superior performance with regard to high energy density and long lifespan. The state of charge of lithium-ion batteries is one of the crucial evaluation indicators of the battery management system that confirms the extended battery life, better charging-discharging profiles, and safe driving of electric vehicles. However, the accuracy of the state of charge is influenced by several issues such as battery aging cycles, noise effects, and temperature impacts. Therefore, this review presents a detailed classification of the recent data-driven state of charge estimation highlighting algorithm, input features, configuration, execution process, strength, weakness and estimation error. This review critically investigates the various key implementation factors of the data-driven algorithms in terms of data preprocessing, hyperparameter adjustment, activation function, evaluation criteria, computational cost and robustness validation under uncertainties. In addition, the review explores the deficiencies of existing data-driven state of charge estimation algorithms to identify the gaps for future research. Finally, the review provides some effective future directions that would be beneficial to the automobile researchers and industrialists to design an accurate and robust state of charge estimation technique toward future sustainable electric vehicle applications. � 2020 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo124110
dc.identifier.doi10.1016/j.jclepro.2020.124110
dc.identifier.scopus2-s2.0-85091229921
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091229921&doi=10.1016%2fj.jclepro.2020.124110&partnerID=40&md5=7a8872cfddb2c7ef86d5c0611963d9c1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25063
dc.identifier.volume277
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Cleaner Production
dc.titleData-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trendsen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS