Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/43865
Type of publication: Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Informatika / Informatics (N009)
Author(s): Man, Ka Lok;Chen, C;Ting, T. O;Krilavičius, Tomas;Chang, J;Poon, S. H
Title: Artificial intelligence approach to SoC estimation for smart BMS
Is part of: ECT-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
Extent: p. 21-24
Date: 2012
Keywords: Battery management systems;BMS;State of charge;SoC;Artificial intelligence;AI
Abstract: One 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 system
Internet: http://www.bpti.lt/uploads/Publikacijos/ARTIFICIAL%20INTELLIGENCE%20APPROACH%20TO%20SoC%20ESTIMATION.pdf
Affiliation(s): Informatikos fakultetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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