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 / Vella, Angelo Domenico; Tota, Antonio; Vigliani, Alessandro. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - (2021). ((Intervento presentato al convegno Noise and Vibration Conference & Exhibition [10.4271/2021-01-1115].

On the Road Profile Estimation from Vehicle Dynamics Measurements

Vella, Angelo Domenico;Tota, Antonio;Vigliani, Alessandro
2021

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2921761