Performance analysis of particle filter for SOC estimation of LiFeP04 battery pack for electric vehicles

Author

Zahid, Taimoor ; Guoqing Xu ; Weimin Li ; Lei Zhao ; Kun Xu

Author_Institution

Shenzhen Inst. of Adv. Technol., Shenzhen, China

fYear

2014

fDate

28-30 July 2014

Firstpage

1061

Lastpage

1065

Abstract

For the development of an innovative battery management system an accurate and a reliable technique for reliability of battery operations for a hybrid/electric vehicle. This paper illustrates an online SOC estimation method of a LiFePo₄ battery for applications in electric vehicles by using a particle filter. Additionally, a five comparison experiments with different open circuit voltage curves exhibits that the particle filter is a promising alternative, even if it is computationally more demanding then extended Kalman filter.

Keywords

Kalman filters; battery management systems; battery powered vehicles; hybrid electric vehicles; lithium compounds; particle filtering (numerical methods); reliability; secondary cells; LiFePO₄; battery management system; electric vehicles; extended Kalman filter; hybrid electric vehicle; open circuit voltage; particle filter; performance analysis; secondary battery pack; state of charge estimation; Batteries; Battery management systems; Decision support systems; Estimation; Kalman filters; Particle filters; Thevenin equivalent circuit model; battery management system (BMS); extended Kalman filter(EKF); open circuit voltage(OCV); particle filter (PF); state of charge(SOC);

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation (ICIA), 2014 IEEE International Conference on

Conference_Location

Hailar

Type

conf

DOI

10.1109/ICInfA.2014.6932806

Filename

6932806