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

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. (6 pages)