RBF network-aided adaptive unscented kalman filter for lithium-ion battery SOC estimation in electric vehicles

• Main Authors: Liu, Zhitao, Wang, Youyi, Du, Jiani, Chen, Can
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Online Access: https://hdl.handle.net/10356/99709; http://hdl.handle.net/10220/12820

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...