DocumentCode :
1936161
Title :
Sigma-point Kalman filter application on estimating battery SOC
Author :
Wang, Liye ; Wang, Lifang ; Liao, Chenglin ; Liu, Jun
Author_Institution :
Inst. of Electr. Eng., Chinese Acad. of Sci., Beijing, China
fYear :
2009
fDate :
7-10 Sept. 2009
Firstpage :
1592
Lastpage :
1595
Abstract :
For the Extended Kalman Filter (EKF) not easy to adjust, difficult to apply on system of updating step time, and its linearization process may generate error of approximation, in recent year, some new methods about expansion Kalman filter to nonlinear system have been proposed. This paper present a new method that Sigma-point Kalman filter estimate SOC through the use of weighted statistical linear regression (WSLR) method for solving linear equations. So this method compared with the traditional EKF method can expect to receive a smaller linearization error. Design a test and apply this method on estimate battery SOC.
Keywords :
Kalman filters; approximation theory; regression analysis; secondary cells; approximation error; battery SOC estimation; extended Kalman filter; linear equations; sigma-point Kalman filter; step time update; weighted statistical linear regression; Battery charge measurement; Filtering; Integral equations; Kalman filters; Nonlinear equations; Nonlinear filters; Nonlinear systems; Polarization; Power system modeling; Voltage; CDKF; EKF; SOC; filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Vehicle Power and Propulsion Conference, 2009. VPPC '09. IEEE
Conference_Location :
Dearborn, MI
Print_ISBN :
978-1-4244-2600-3
Electronic_ISBN :
978-1-4244-2601-0
Type :
conf
DOI :
10.1109/VPPC.2009.5289604
Filename :
5289604
Link To Document :
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