State-of-charge estimation for lithium-ion battery using AUKF and LSSVM

Author

Jinhao Meng ; Guangzhao Luo ; Fei Gao

Author_Institution

Sch. of Autom., Northwestern Polytech. Univ., Xi´an, China

fYear

2014

fDate

Aug. 31 2014-Sept. 3 2014

Firstpage

1

Lastpage

6

Abstract

A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unscented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.

Keywords

adaptive Kalman filters; least squares approximations; nonlinear filters; power engineering computing; secondary cells; support vector machines; AUKF; LSSVM; SOC estimation accuracy; adaptive unscented Kalman filter; least squares support vector machine; lithium-ion battery; measurement equation; noise covariance; state-of-charge estimation; Accuracy; Batteries; Battery charge measurement; Equations; Estimation; Mathematical model; System-on-chip; Battery; adaptive unscented Kalman filter (AUKF); least squares support vector machine (LSSVM); state of charge (SOC);

fLanguage

English

Publisher

ieee

Conference_Titel

Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), 2014 IEEE Conference and Expo

Conference_Location

Beijing

Print_ISBN

978-1-4799-4240-4

Type

conf

DOI

10.1109/ITEC-AP.2014.6940680

Filename

6940680