RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles
|dc.description.abstract||An 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.title||RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles||en_US|
|dc.contributor.conference||IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore)||en_US|
|dc.contributor.school||School of Electrical and Electronic Engineering||en_US|
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