Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments

TLDR: In this article, an ad hoc modification of the update law for the gain in the recursive least square (RLS) scheme is proposed and used in simulation and experiments, demonstrating that the proposed scheme estimates mass within 5% of its actual value and tracks grade with good accuracy.

Abstract: Good estimates of vehicle mass and road grade are important in automation of heavy duty vehicles, vehicle following manoeuvres or traditional powertrain control schemes. Recursive least square (RLS) with multiple forgetting factors accounts for different rates of change for different parameters and thus, enables simultaneous estimation of the time-varying grade and the piece-wise constant mass. An ad hoc modification of the update law for the gain in the RLS scheme is proposed and used in simulation and experiments. We demonstrate that the proposed scheme estimates mass within 5% of its actual value and tracks grade with good accuracy provided that inputs are persistently exciting. The experimental setups, signals, their source and their accuracy are discussed. Issues like lack of persistent excitations in certain parts of the run or difficulties of parameter tracking during gear shift are explained and suggestions to bypass these problems are made.