Vehicle Mass and Road Grade Estimation using Kalman Filter
This master thesis presents a method for on-line estimation of vehicle mass and
road grade using Kalman filter. Many control strategies aiming for better fuel
economy, drivability and safety in today’s vehicles rely on precise vehicle operating
information. In this context, vehicle mass and road grade are crucial parameters.
The method is based on an extended Kalman filter (EKF) and a longitudinal
vehicle model. The main advantage of this method is its applicability on drivelines
with continuous power output during gear shifts and cost effectiveness compared
to hardware solutions.
The performance has been tested on both simulated data and on real measurement
data, collected with a truck on road. Two estimators were developed; one
estimates both vehicle mass and road grade and the other estimates only mass
using an inclination sensor as an additional measurement. Tests of the former estimator
demonstrate that a reliable mass estimate with less than 5% error is often
achievable within 5 minutes of driving. Furthermore, the root mean square error
of the grade estimate is often within 0.6. Tests of the latter estimator show that
this is more accurate and robust than the former estimator with a mass error often
within 2 %. A sensitivity analysis shows that the former estimator is fairly robust
towards minor modelling errors. Also, an observability analysis shows under which
circumstances simultaneous vehicle mass and road grade is possible.
Erik Holm
2011
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Last updated: 2021-11-10
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