Accurate estimation of electric vehicle speed using Kalman Filtering in the presence of parameter variations

Author

Hodgson, D. ; Mecrow, B.C. ; Gadoue, S.M. ; Slater, H.J. ; Barrass, P.G. ; Giaouris, D.

Author_Institution

Newcastle Univ., Newcastle upon Tyne, UK

fYear

2012

fDate

27-29 March 2012

Firstpage

1

Lastpage

6

Abstract

The mechanical drivetrain dynamics of electric vehicles can have a detrimental effect on the performance of the vehicle speed controller. This is mainly caused by the feedback only being available from the motor encoder, with no measurement of the actual vehicle speed. In this paper it is shown how the vehicle driveability can be greatly improved if estimates of vehicle speed and mass are obtained. This has been realised using a Kalman Filter (KF) and a Recursive Least Squares (RLS) estimator, and validated with experimental results.

Keywords

Kalman filters; angular velocity control; electric vehicles; least squares approximations; recursive estimation; KF; Kalman filtering; RLS estimator; electric vehicle speed accurate estimation; mechanical drivetrain dynamics; motor encoder; recursive least squares estimator; vehicle driveability; vehicle speed controller; Electric vehicles; Kalman filter; mass estimation;

fLanguage

English

Publisher

iet

Conference_Titel

Power Electronics, Machines and Drives (PEMD 2012), 6th IET International Conference on

Conference_Location

Bristol

Electronic_ISBN

978-1-84919-616-1

Type

conf

DOI

10.1049/cp.2012.0315

Filename

6242167