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Low-Cost Method of Estimating Vehicl...

Forte, Livio, III.

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Low-Cost Method of Estimating Vehicle Sideslip and Tire Cornering Stiffness Using Global Positioning and Inertial Measurement.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Low-Cost Method of Estimating Vehicle Sideslip and Tire Cornering Stiffness Using Global Positioning and Inertial Measurement./
作者:	Forte, Livio, III.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	122 p.
附註:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
標題:	Automotive engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885598
ISBN:	9781392271018

Low-Cost Method of Estimating Vehicle Sideslip and Tire Cornering Stiffness Using Global Positioning and Inertial Measurement.
Forte, Livio, III.

Low-Cost Method of Estimating Vehicle Sideslip and Tire Cornering Stiffness Using Global Positioning and Inertial Measurement. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 122 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.)--State University of New York at Buffalo, 2019.

This item must not be sold to any third party vendors.

In this study a low-cost method of estimating the linear tire model for a road vehicle is developed, which can be used to make quantitative decisions for tire selection and vehicle modification. As a traditional tire model requires measuring the forces and moments of the tire, for a range of slip and camber angles and normal force, on a rotating belt assembly.A data acquisition (DAQ) system using consumer grade global position system (GPS) and inertial measurement unit (IMU) is designed and used to measure and record the vehicles response. The single-antenna GPS measures the vehicles track angle and speed, and the IMU measures the vehicles lateral and longitudinal acceleration and yaw rate.The GPS measurements are sampled at 5 Hz and the IMU measurements are sampled at 100 Hz. The measured data from the GPS is aligned with the IMU measurements using the time stamp. The measured IMU data is smoothed using a moving mean filter and the GPS data is splined to match the sampling rate of the IMU measurements. The processed data is then used to estimate the vehicles yaw angle, and lateral and longitudinal velocity using a cascading kinematic Kalman filter (KKF).The kinematic equations for the KKF are derived from the planar bicycle model. Using the estimated results from the KKF the linear tire model is calculated for a bicycle model. The approach was validated by modifying vehicle parameters in a simulation and on a test vehicle.Two main conclusions have been made in this study: (1) the estimated tire model can be used to quantify changes in the vehicle response due to changes in the vehicle and tire parameters; (2) the estimated results are repeatable and predict the true tire model.

ISBN: 9781392271018Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

Cornering stiffness

Low-Cost Method of Estimating Vehicle Sideslip and Tire Cornering Stiffness Using Global Positioning and Inertial Measurement.
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300 $a 122 p.
500 $a Source: Masters Abstracts International, Volume: 80-12.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Armstrong, Jason.
502 $a Thesis (M.S.)--State University of New York at Buffalo, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a In this study a low-cost method of estimating the linear tire model for a road vehicle is developed, which can be used to make quantitative decisions for tire selection and vehicle modification. As a traditional tire model requires measuring the forces and moments of the tire, for a range of slip and camber angles and normal force, on a rotating belt assembly.A data acquisition (DAQ) system using consumer grade global position system (GPS) and inertial measurement unit (IMU) is designed and used to measure and record the vehicles response. The single-antenna GPS measures the vehicles track angle and speed, and the IMU measures the vehicles lateral and longitudinal acceleration and yaw rate.The GPS measurements are sampled at 5 Hz and the IMU measurements are sampled at 100 Hz. The measured data from the GPS is aligned with the IMU measurements using the time stamp. The measured IMU data is smoothed using a moving mean filter and the GPS data is splined to match the sampling rate of the IMU measurements. The processed data is then used to estimate the vehicles yaw angle, and lateral and longitudinal velocity using a cascading kinematic Kalman filter (KKF).The kinematic equations for the KKF are derived from the planar bicycle model. Using the estimated results from the KKF the linear tire model is calculated for a bicycle model. The approach was validated by modifying vehicle parameters in a simulation and on a test vehicle.Two main conclusions have been made in this study: (1) the estimated tire model can be used to quantify changes in the vehicle response due to changes in the vehicle and tire parameters; (2) the estimated results are repeatable and predict the true tire model.
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