In this paper application of recursive least squares with multiple forgetting factors is explained for online estimation of Heavy Duty Vehicle mass and road grade. The test data is obtained from highway experiments with a Freightliner truck. The experimental setup is explained in detail. This data is used to validate the longitudinal dynamics model of the truck. Then two distinct driving cycles are used to investigate the performance of the mass and grade estimation scheme. In he first scheme no gear shift occurs and the only concern is persistence of excitations. It is shown that if the excitations are persistent, mass and time-varying grade are estimated with good accuracy. In the second cycle gearshifts occur and the challenge is the unmodelled dynamics during gearshifts which cause large overshoots in the estimates. A method is proposed to circumvent this problem and good estimation results are shown with this provision.
Intelligent Two-Step Estimation Approach for Vehicle Mass and Road Grade
