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SOC Estimation of Li-ion Batteries with Learning Rate-Optimized Deep Fully Convolutional Network

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dc.citedby50
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorDong Z.Y.en_US
dc.contributor.authorMansur M.en_US
dc.contributor.authorBlaabjerg F.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57212923888en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid56608244300en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid7004992352en_US
dc.date.accessioned2023-05-29T09:07:02Z
dc.date.available2023-05-29T09:07:02Z
dc.date.issued2021
dc.descriptionCharging (batteries); Convolution; Convolutional neural networks; Deep learning; Ions; Learning systems; Lithium compounds; Lithium-ion batteries; Long short-term memory; Temperature; Battery temperature; Convolutional networks; Learning rates; Lithium ions; Novel architecture; Open sources; SOC estimations; State of charge; Battery management systemsen_US
dc.description.abstractIn this letter, we train deep learning (DL) models to estimate the state-of-charge (SOC) of lithium-ion (Li-ion) battery directly from voltage, current, and battery temperature values. The deep fully convolutional network model is proposed for its novel architecture with learning rate optimization strategies. The proposed model is capable of estimating SOC at constant and varying ambient temperature on different drive cycles without having to be retrained. The model also outperformed other commonly used DL models such as the LSTM, GRU, and CNN on an open source Li-ion battery dataset. The model achieves 0.85% root mean squared error (RMSE) and 0.7% mean absolute error (MAE) at 25 �C and 2.0% RMSE and 1.55% MAE at varying ambient temperature (-20-25 �C). � 1986-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9276459
dc.identifier.doi10.1109/TPEL.2020.3041876
dc.identifier.epage7353
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85097386309
dc.identifier.spage7349
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097386309&doi=10.1109%2fTPEL.2020.3041876&partnerID=40&md5=3867d267f060e0a60c1dea3a1cd4c1a8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26129
dc.identifier.volume36
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Power Electronics
dc.titleSOC Estimation of Li-ion Batteries with Learning Rate-Optimized Deep Fully Convolutional Networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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