Ride comfort assessment is undoubtedly related to the interaction between the vehicle tires and the road surface. Indeed, the road profile represents the typical input for tire vertical load estimation in durability analysis and for active/semi-active suspension controller design. However, the road profile evaluation through direct experimental measurements involves long test time and excessive cost required by professional instrumentations to detect the road irregularities with sufficient accuracy. An alternative is shifting attention towards efficient and robust algorithms for indirect road profile evaluation. The object of this work aims at providing road profile estimation starting from vehicle dynamics measurements, through accessible and traditional sensors, with the application of a linear Kalman filter algorithm. The filter is designed and tuned by considering the pitch/bounce half-car models for the prediction phase and by measuring vertical accelerations and angular speeds for the correction phase. The estimator is then tested on experimental data, acquired driving a passenger car over a road bump at different vehicle speeds. The vehicle used in the experimental campaign is a two-passenger electric quadricycle involved in the demonstration phase of the European project STEVE.
On the Road Profile Estimation from Vehicle Dynamics Measurements
Sae Technical Papers
Noise and Vibration Conference & Exhibition ; 2021
2021-08-31
Conference paper
English
On the Road Profile Estimation from Vehicle Dynamics Measurements
British Library Conference Proceedings | 2021
|Road profile estimation in heavy vehicle dynamics simulation
Automotive engineering | 2008
|Road profile input estimation in vehicle dynamics simulation
Online Contents | 2006
|Road profile estimation in heavy vehicle dynamics simulation
Online Contents | 2008
|Road profile input estimation in vehicle dynamics simulation
Automotive engineering | 2006
|