dc.contributor.authorLiu, Zhitao
dc.contributor.authorWang, Youyi
dc.contributor.authorDu, Jiani
dc.contributor.authorChen, Can
dc.date.accessioned2013-08-02T02:28:59Z
dc.date.available2013-08-02T02:28:59Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10220/12820
dc.description.abstractAn accurate battery State of Charge (SOC) estimation is very important for electric vehicles. In this paper, a method is proposed to estimate the SOC of the lithium-ion batteries using radial basis function (RBF) networks and the adaptive unscented Kalman filter (AUKF). The RBF networks are to model the battery-discharging process, then the AUKF is applied to estimate the SOC of the battery. Simulation results show that the proposed method has good performance in battery modeling and SOC estimation.en_US
dc.language.isoenen_US
dc.titleRBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehiclesen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICIEA.2012.6360994


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