The tires are among the most important components of a road vehicle. In this investigation, the suspension arms of a race vehicle were instrumented with strain gauges to measure the forces developed in the tire-road contact patch. The vehicle was also equipped with displacement transducers to calculate the wheel angles and with speed sensors to evaluate the tire slips. Considering the acquired tire data and the Pacejka’s Magic Formula (MF) models provided by the tire manufacturer, the scaling factors were identified using a dedicated rou-tine, which minimizes the difference between the tire forces measured from the instrumented suspensions and the corresponding values calculated from the MF. To ensure accurate results, data pre-processing steps were implemented to ex-clude transient slip observations and to reduce redundancies. Subsequently, two identification approaches were compared, namely the step-by-step (SBS) and the all-in-one (AIO). In the SBS approach, scaling factors related to pure slips were first identified from appropriately filtered racetrack data; then, the identification for combined slip conditions was performed. Conversely, the AIO approach en-abled direct identification of all relevant scaling factors using pure and combined slip data. The scaling factors obtained with both approaches significantly en-hanced the agreement between the prediction of the MF models and the experi-mental data. However, the AIO technique demonstrated advantages over the SBS method, because of the reduced number of pre-processing steps and better gen-eralization across various tire working conditions.

Development of a tire characterization procedure from track acquisitions with an instrumented race vehicle

cortivo davide
;
meneghetti giovanni;massaro matteo;
2023

Abstract

The tires are among the most important components of a road vehicle. In this investigation, the suspension arms of a race vehicle were instrumented with strain gauges to measure the forces developed in the tire-road contact patch. The vehicle was also equipped with displacement transducers to calculate the wheel angles and with speed sensors to evaluate the tire slips. Considering the acquired tire data and the Pacejka’s Magic Formula (MF) models provided by the tire manufacturer, the scaling factors were identified using a dedicated rou-tine, which minimizes the difference between the tire forces measured from the instrumented suspensions and the corresponding values calculated from the MF. To ensure accurate results, data pre-processing steps were implemented to ex-clude transient slip observations and to reduce redundancies. Subsequently, two identification approaches were compared, namely the step-by-step (SBS) and the all-in-one (AIO). In the SBS approach, scaling factors related to pure slips were first identified from appropriately filtered racetrack data; then, the identification for combined slip conditions was performed. Conversely, the AIO approach en-abled direct identification of all relevant scaling factors using pure and combined slip data. The scaling factors obtained with both approaches significantly en-hanced the agreement between the prediction of the MF models and the experi-mental data. However, the AIO technique demonstrated advantages over the SBS method, because of the reduced number of pre-processing steps and better gen-eralization across various tire working conditions.
2023
Lecture Notes in Mobility
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3492324
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