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State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm

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dc.citedby3
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorFaisal M.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57215018777en_US
dc.date.accessioned2023-05-29T06:49:58Z
dc.date.available2023-05-29T06:49:58Z
dc.date.issued2018
dc.descriptionCharging (batteries); Decision trees; Electric vehicles; Forestry; Ions; Learning algorithms; Lithium-ion batteries; Radial basis function networks; Gravitational search algorithm (GSA); Gravitational search algorithms; Radial basis function neural networks; Random forests; State of charge; State of charge estimations (SOC); State-of-charge estimation; Sustainable transportation systems; Battery management systemsen_US
dc.description.abstractAn accurate state of charge (SOC) estimation for lithium-ion battery has been an intensively researched subject in electric vehicle (EV) application towards the advancement of the sustainable transportation system. However, SOC estimation with high accuracy is challenging because of the complex internal characteristics of the lithium-ion battery which is changed by different environmental situations. This paper develops an accurate method for the state of charge (SOC) estimation of a lithium-ion battery using random forests (RFs) algorithm. However, the accuracy of RFs highly depends on the appropriate selection of trees and leaves per tree in a forest. Thus, this research develops an enhanced model with RFs based gravitational search algorithm (GSA). The aim of GSA is to find the best value of trees and leaves per tree. The robustness and accuracy of the proposed model are tested under different temperatures. The model training and validation are executed using federal urban driving schedule (FUDS). The effectiveness of the proposed method is compared with the conventional RFs and radial basis function neural network (RBFNN) and optimal RBFNN-GSA models using different statistical error terms and computational cost. The proposed RFs based GSA model offers higher robustness and accuracy in reducing RMSE by 55.4 % , 67.4%, and MAE by 39.1% and 78.1% than conventional RFs and RBFNN based GSA model, respectively at 25�C. � 2018 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8566648
dc.identifier.doi10.1109/APPEEC.2018.8566648
dc.identifier.epage50
dc.identifier.scopus2-s2.0-85060375580
dc.identifier.spage45
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060375580&doi=10.1109%2fAPPEEC.2018.8566648&partnerID=40&md5=6e4aff5b9fa26b590a19971902c87977
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23504
dc.identifier.volume2018-October
dc.publisherIEEE Computer Societyen_US
dc.sourceScopus
dc.sourcetitleAsia-Pacific Power and Energy Engineering Conference, APPEEC
dc.titleState of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithmen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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