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

Author

Liu, Zhitao ; Wang, Youyi ; Du, Jiani ; Chen, Can

Author_Institution

TUM CREATE Res. Centre, Singapore, Singapore

fYear

2012

fDate

18-20 July 2012

Firstpage

1673

Lastpage

1677

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.

Keywords

adaptive Kalman filters; battery management systems; battery powered vehicles; nonlinear filters; power engineering computing; radial basis function networks; secondary cells; RBF network aided adaptive Kalman filter; adaptive unscented Kalman filter; battery state of charge estimation; electric vehicles; lithium-ion battery SOC estimation; radial basis function networks; Batteries; Estimation; Hybrid electric vehicles; Kalman filters; Mathematical model; Radial basis function networks; System-on-a-chip; Lithium-ion battery; RBF networks; State-of-charge; adaptive unscented Kalman filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4577-2118-2

Type

conf

DOI

10.1109/ICIEA.2012.6360994

Filename

6360994