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State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

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dc.citedby92
dc.contributor.authorHow D.N.T.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorid57212923888en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T08:07:40Z
dc.date.available2023-05-29T08:07:40Z
dc.date.issued2020
dc.descriptionAutomotive batteries; Charging (batteries); Deep neural networks; Electric vehicles; Ions; Lithium compounds; Lithium-ion batteries; Neural networks; Ambient environment; Battery chemistries; Crucial parameters; Federal test procedures; State of charge; State-of-charge estimation; Training algorithms; Vehicle applications; Battery management systemsen_US
dc.description.abstractThe state of charge (SOC) is a crucial parameter of a battery management system for Li-ion batteries. The SOC indicates the amount of charge left in the battery of electric vehicles-akin to the fuel gauge in combustion vehicles. An accurate SOC knowledge contributes largely to the longevity, performance, and reliability of the battery. However, the SOC of Li-ion batteries cannot be easily measured by any apparatus. Furthermore, the SOC can also be influenced by numerous incalculable factors such as battery chemistry, ambient environment, aging factor, etc. In this article, we propose an SOC estimation model for a Li-ion battery using an improved deep neural network (DNN) approach for electric vehicle applications. We found that a DNN with a sufficient number of hidden layers is capable of predicting the SOC of the unseen drive cycles during training. We developed a series of DNN models with a varying number of hidden layers, and its training algorithm was to investigate their respective performance when evaluated on different drive cycles. We observe that the increasing number of hidden layers in the DNN (up to four hidden layers) decreases the error rate and improves SOC estimation. An additional increase in the number of hidden layers beyond that increases the error rate. In this study, we show that a four-hidden-layer DNN trained on Dynamic Stress Test drive cycle is capable of predicting SOC values unexpectedly well of other unseen drive cycles such as Federal Urban Driving Schedule, Beijing Dynamic Stress Test, and Supplemental Federal Test Procedure, respectively. � 1972-2012 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9122534
dc.identifier.doi10.1109/TIA.2020.3004294
dc.identifier.epage5574
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85091776896
dc.identifier.spage5565
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091776896&doi=10.1109%2fTIA.2020.3004294&partnerID=40&md5=cfa56c5953537c5c5c1b5375bfe42a32
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25262
dc.identifier.volume56
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.titleState-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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