Publication:
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm

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dc.citedby37
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorAnsari S.en_US
dc.contributor.authorRahman S.A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorid58562396100en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid57218906707en_US
dc.contributor.authorid36609854400en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2024-10-14T03:22:09Z
dc.date.available2024-10-14T03:22:09Z
dc.date.issued2023
dc.description.abstractThis paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and current. The developed approach is validated at room temperature using two types of lithium-ion batteries under a pulse discharge test. In addition, the proposed model is verified under varying temperature settings under EV drive cycles. The experimental results demonstrate that the DSA optimized RFR algorithm achieves RMSE of 0.382% in the HPPC test using LiNMC battery. Besides, the proposed method obtains satisfactory outcomes in EV drive cycles, estimating MAE of 0.193% and 0.346% in DST and FUDS cycles, respectively, at 25�C. � 2016 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TIV.2022.3161301
dc.identifier.epage648
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85127075648
dc.identifier.spage639
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127075648&doi=10.1109%2fTIV.2022.3161301&partnerID=40&md5=42188789a0477d5ce9547e15602fcf79
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34732
dc.identifier.volume8
dc.pagecount9
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Intelligent Vehicles
dc.subjectDifferential search algorithm
dc.subjectelectric vehicle
dc.subjectlithium-ion battery
dc.subjectrandom forest regression
dc.subjectstate of charge
dc.subjectBattery management systems
dc.subjectCharging (batteries)
dc.subjectData handling
dc.subjectDecision trees
dc.subjectDigital storage
dc.subjectElectric discharges
dc.subjectElectric vehicles
dc.subjectEstimation
dc.subjectIons
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectRegression analysis
dc.subjectBattery
dc.subjectDifferential search algorithm
dc.subjectPrediction algorithms
dc.subjectRandom forest regression
dc.subjectRandom forests
dc.subjectRegression algorithms
dc.subjectSearch Algorithms
dc.subjectStates of charges
dc.subjectLithium-ion batteries
dc.titleReal-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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