Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/43865
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dc.contributor.authorMan, Ka Lok-
dc.contributor.authorChen, C-
dc.contributor.authorTing, T. O-
dc.contributor.authorKrilavičius, Tomas-
dc.contributor.authorChang, J-
dc.contributor.authorPoon, S. H-
dc.coverage.spatialLT-
dc.date.accessioned2018-10-06T20:36:06Z-
dc.date.available2018-10-06T20:36:06Z-
dc.date.issued2012-
dc.identifier.issn18225934-
dc.identifier.otherVDU02-000012062-
dc.identifier.urihttp://www.bpti.lt/uploads/Publikacijos/ARTIFICIAL%20INTELLIGENCE%20APPROACH%20TO%20SoC%20ESTIMATION.pdf-
dc.description.abstractOne of the most important and indispensable parameters of a Battery Management Systems (BMS) is accurate estimates of the State of Charge (SoC) of the battery. It can prevent battery from damage or premature aging by avoiding over charge/discharge. Due to the limited capacity of a battery, advanced methods must be used to estimate precisely the SoC in order to keep battery safely being charged and discharged at a suitable level and to prolong its life cycle. In this paper, we review several effective approaches: Coulomb counting, Open Circuit Voltage (OCV) and Kalman Filter method for performing the SoC estimation; then we propose Artificial Intelligence (AI) approach that can be efficiently used to precisely determine the SoC estimation for the smart battery management system as presented in [1]. By using our proposed approach, a more accurate SoC measurement will be obtained for the smart battery management systemen
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 21-24-
dc.language.isoen-
dc.relation.ispartofECT-2012 : Electrical and control technologies : proceedings of the 7th international conference on electrical and control technologies, May 3-4, 2012, Kaunas, Lithuania. Kaunas : Technologija, 2012, no. 7-
dc.relation.isreferencedbyConference Proceedings Citation Index - Science (Web of Science)-
dc.subjectBattery management systemsen
dc.subjectBMSen
dc.subjectState of chargeen
dc.subjectSoCen
dc.subjectArtificial intelligenceen
dc.subjectAIen
dc.subject.classificationStraipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleArtificial intelligence approach to SoC estimation for smart BMSen
dc.typeresearch article-
dc.identifier.isiWOS:000333437000004-
dcterms.bibliographicCitation33-
dc.date.updated2021-08-23T09:42Z-
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local.typeP-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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