Estimation of Li-ion battery State of Charge based on extended Kalman filtering

Author

Yan, Ma ; Qingwen, Bai ; Liang, Liang ; Hong, Chen

Author_Institution

State Key Lab. of Automotive Simulation & Control, Jilin Univ., Changchun, China

fYear

2012

fDate

25-27 July 2012

Firstpage

6815

Lastpage

6819

Abstract

In order to obtain the accurate estimation of SOC (State of Charge), this article is based upon the 2-order RC equivalent circuit model of Li-ion battery. The nonlinear relationship between OCV (Open Circuit Voltage) and SOC of the battery is derived from a rapid test. Depending on the parameter identification of the equivalent circuit model, which the least square method is applied to, the extended Kalman filter for the estimation of SOC can be carried out. The final simulations and the result of the experiments show that the extended Kalman filter can reduce the influence of white noise and make the accuracy of SOC estimation within 1.4%.

Keywords

Kalman filters; least squares approximations; lithium; nonlinear filters; parameter estimation; secondary cells; Li; OCV; SOC estimation; extended Kalman filtering; least square method; lithium-ion battery; lithium-ion battery state of charge estimation; open circuit voltage; order RC equivalent circuit model; parameter identification; Batteries; Equivalent circuits; Estimation; Integrated circuit modeling; Kalman filters; System-on-a-chip; 2-order RC Equivalent Circuit Model; Extended Kalman Filter; Li-ion batteries; Parameter Identification; SOC;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2012 31st Chinese

Conference_Location

Hefei

ISSN

1934-1768

Print_ISBN

978-1-4673-2581-3

Type

conf

Filename

6391139