• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.5
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• pp.512-519
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• 2016

In this study, the tire road friction estimation(TRFE) algorithm for controlling the rear wheel driving force of a 4WD vehicle during acceleration is developed using a standard sensor in an ordinary 4WD passenger car and a speed sensor. The algorithm is constructed for the wheel shaft torque, longitudinal tire force, vertical tire force and maximum tire road friction estimation. The estimation results of shaft torque and tire force were validated using a torque sensor and wheel force transducer. In the algorithm, the current road friction is defined as the proportion calculated between longitudinal and vertical tire force. Slip slop methods using current road friction and slip ratio are applied to estimate the road friction coefficient. Based on this study's results, the traction performance, fuel consumption and drive shaft strength performance of a 4WD vehicle are improved by applying the tire road friction estimation algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.142-147
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• 2013

Over the past few years, intelligent tire is developed very actively for more accurate measurement of real-time tire forces generated during vehicle driving situation. Information on the force of intelligent tire could be used very usefully to chassis control systems of a vehicle. Intelligent tire is based on deformation of tire's inner surface from the waveform of a SAW, or Surface Acoustic Wave. The tire vertical force is estimated by using variance analysis of sensor signals. The estimated tire vertical force is compared with the tire vertical force generated during vehicle driving situation in real-time environment. The scope of this paper is a correlation study between the measured sensor signals and the tire vertical force generated during vehicle driving situation.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.33-38
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• 2021

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11b
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• pp.148-151
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• 2005

Roughness of a road is an important parameter which not only indicates vehicle's vibration and noise, but it is also related to the contact force of the tire which is induced by tire's deformation and vibration. Since tire noise indeed comes from this deformation and vibration, the estimation of the force is the key factor fur the reduction of tire noise. Because of the difficulty of directly measuring the contact force, the indirect estimation is enforced from the vibration signature measured on the tire support. This study suggests the "inverse filtering" technique well known in modern digital signal processing, so as to reform the tire contact force from monitored vibration signals.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.130-135
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• 2014

In this paper, a new estimation method is proposed to calculate steering rack axial force using a 3 dimensional tire mesh model when a car is standing on the road. This model is established by considering changes of camber angle and contact patch between the tires and the ground according to steering angle. The steering rack bar axial force is estimated based on the static equilibrium equations of forces and moments. A tire friction force is supposed to act on the center point of the contact patch, and the proportional coefficient of friction depending on contact patch is suggested. Using the proposed estimation method, rack axial force sensitivity analysis is evaluated according to changes of suspension geometry. Then optimal motor power of Motor Driven Power Steering(MDPS) is evaluated using suggested rack forces.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.126-131
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• 2012

Tire force is one of important parameters which determine vehicle dynamics. However, it is hard to measure tire force directly through sensors. Not only the sensor is expensive but also installation of sensors on harsh environments is difficult. Therefore, estimation algorithms based on vehicle dynamic models are introduced to estimate the tire forces indirectly. In this paper, an estimation system for estimating lateral force and states is suggested. The state-space equation is constructed based on the 3-DOF bicycle model. Extended Kalman Filter, Unscented Kalman Filter and Ensemble Kalman Filter are used for estimating states on the nonlinear system. Performance of each algorithm is evaluated in terms of RMSE (Root Mean Square Error) and maximum error.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.4
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• pp.169-176
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• 2000

Longitudinal and lateral forces acting on tires are known to be closely related to the tract-ability braking characteristics handling stability and maneuverability of ground vehicles. In thie paper in order to develop tire force monitoring systems a monitoring model is proposed utilizing not only the vehicle dynamics but also the roll motion. Based on the monitoring model three monitoring systems are developed to estimate the tire force acting on each tire. Two monitoring systems are designed utilizing the conventional estimation techniques such as SMO(Sliding Mode Observer) and EKF(Extended Kalman Filter). An additional monitoring system is designed based on a new SKFMEC(Scaled Kalman Filter with Model Error Compensator) technique which is developed to improve the performance of EKF method. Tire force estimation performance of the three monitoring systems is compared in the Matlab simulations where true tire force data is generated from a 14 DOF vehicle model with the combined-slip Magic Formula tire model. The built in our Lab. simulation results show that the SKFMEC method gives the best performance when the driving and road conditions are perturbed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1871-1877
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• 2000

Longitudinal and lateral forces acting at tire are known to be closely related to the tractive ability, braking characteristics, handling stability and maneuverability of ground vehicles. However, it is not feasible in the operating vehicles to measure the tire forces directly because of high cost of sensors, limitations in sensor technology, interference with the tire rotation and harsh environment. In this paper, in order to develop tire force monitoring system, a new vehicle dynamics monitoring model is proposed including the roll motion. Based on the monitoring model, tire force monitoring system is designed to estimate the lateral tire force acting at each tire. A newly proposed SKFMEC (Scaled Kalman Filter with Model Emr Compensator) method is developed utilizing the conventional EKF (Extended Kalman Filter) method. Tire force estimation performance of the SKFMEC method is evaluated in the Matlab simulations where true tire force data is generated from a 14 DOF vehicle model with a combined-slip Magic Formula tire model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.517-523
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• 2004

Recently, wheel slip controllers with controlling the wheel slip directly has been studied using the brake-by-wire actuator. The wheel slip controller is able to control the braking force more accurately and can be adapted to various different vehicles more easily than the conventional ABS systems. The wheel slip controller requires the information about the tire braking force and road condition in order to achieve the control performance. In this paper, the tire braking forces are estimated considering the variation of the friction between brake pad and disk due to aging of the brake, moisture on the contact area or heating. In addition, the road friction coefficient is estimated without using tire models. The estimated performance of tire braking forces and the road friction coefficient is evaluated in simulations.

• The Journal of Korea Robotics Society
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• v.13 no.3
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• pp.174-181
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• 2018

For a mobile robot that travels along a terrain consisting of various geology, information on tire force and friction coefficient between ground and wheel is an important factor. In order to estimate the lateral force between ground and wheel, a lot of information about the model and the surrounding environment of the vehicle is required in conventional method. Therefore, in this paper, we are going to estimate lateral force through simple model (Minimal Argument Lateral Slip Curve, MALSC) using only minimum data with high estimation accuracy and to improve estimation reliability of the friction coefficient by using the estimated lateral force data. Simulation is carried out to analyze the correlation between the longitudinal and transverse friction coefficients and slip angles to design the simplified lateral force estimation model by analysing simulation data and to apply it to the actual field environment. In order to verify the validity of the equation, estimation results are compared with the conventional method through simulation. Also, the results of the lateral force and friction coefficient estimation are compared from both the conventional method and the proposed model through the actual robot running experiments.