@article{ref1,
title="Longitudinal velocity and road slope estimation in hybrid electric vehicles employing early detection of excessive wheel slip",
journal="Vehicle system dynamics",
year="2014",
author="Klomp, Matthijs and Gao, Yunlong and Bruzelius, Fredrik",
volume="52",
number="Suppl 1",
pages="172-188",
abstract="Vehicle speed is one of the important quantities in vehicle dynamics control. Estimation of the slope angle is in turn a necessity for correct dead reckoning from vehicle acceleration. In the present work, estimation of vehicle speed is applied to a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque information, provided by electric motor, is used to early detect excessive wheel slip and improve the accuracy of the estimate. A best-wheel selection approach is applied as the observation variable of a Kalman filter which reduces the influence of slipping wheels as well as reducing the computational effort. The performance of the proposed algorithm is illustrated on a test data recorded at a winter test ground with excellent results, even for extreme conditions such as when all four wheels are spinning.<p />",
language="en",
issn="0042-3114",
doi="10.1080/00423114.2014.887737",
url="http://dx.doi.org/10.1080/00423114.2014.887737"
}