State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers

Author

Bhangu, B.S. ; Bentley, P. ; Stone, D.A. ; Bingham, C.M.

Author_Institution

Dept. of Electron. & Electr. Eng., Univ. of Sheffield

fYear

2005

fDate

11-14 Sept. 2005

Abstract

The paper describes the application of state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Approaches based on the extended Kalman filter (EKF) are presented to provide correction for offset, drift and state divergence - an unfortunate feature of more traditional coulomb-counting techniques. Experimental results are employed to demonstrate the relative attributes of the proposed methodology

Keywords

Kalman filters; battery powered vehicles; hybrid electric vehicles; lead acid batteries; nonlinear filters; observers; power filters; EKF; extended Kalman filter; hybrid electric vehicles; lead-acid batteries; nonlinear observers; state-estimation techniques; state-of-charge prediction; state-of-health prediction; Battery powered vehicles; Capacitors; Electronic mail; Hybrid electric vehicles; Predictive models; State estimation; Surface resistance; Uniform resource locators; Vehicle driving; Vehicle dynamics; Battery management systems (BMS); Energy storage; Estimation technique; Hybrid electric vehicle (HEV); Modelling;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics and Applications, 2005 European Conference on

Conference_Location

Dresden

Print_ISBN

90-75815-09-3

Type

conf

DOI

10.1109/EPE.2005.219601

Filename

1665791