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State of charge estimation in lithium-ion batteries: A neural network optimization approach

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dc.citedby32
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.authorMuttaqi K.M.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T08:07:41Z
dc.date.available2023-05-29T08:07:41Z
dc.date.issued2020
dc.description.abstractThe development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2 ) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2 ) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences. � 2020, MDPI AG. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1546
dc.identifier.doi10.3390/electronics9091546
dc.identifier.epage24
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85091651587
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091651587&doi=10.3390%2felectronics9091546&partnerID=40&md5=9b3b6b3d9c897de330eb4df58879f650
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25265
dc.identifier.volume9
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleElectronics (Switzerland)
dc.titleState of charge estimation in lithium-ion batteries: A neural network optimization approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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