Tire Characteristics Estimation Method Independent of Road Surface Conditions

2018 ◽  
Vol 30 (1) ◽  
pp. 138-144 ◽  
Author(s):  
Yuuki Shiozawa ◽  
◽  
Hiroshi Mouri

To control vehicle behavior, it is essential to estimate tire force accurately at all times. However, it is currently difficult to detect tire performance degradation before the deterioration of vehicle dynamics in real time because tire force estimation is usually conducted by comparing the observed vehicle motion with the onboard vehicle-model motion baseline reference. Such conventional estimators do not perform well if there is a significant difference between the vehicle and the model behavior. The lack of technology to easily predict tire forces and road surface friction is concerning. In this paper, a new tire state estimation method based on tire force characteristics is proposed.

2018 ◽  
Vol 30 (5) ◽  
pp. 811-818
Author(s):  
Yuuki Shiozawa ◽  
◽  
Shunsuke Tsukuda ◽  
Hiroshi Mouri

For vehicle dynamics control and Autonomous Driving (AD) system, it is important to know the friction coefficient μ of the road surface accurately. It is because the lateral and the longitudinal force characteristics of the tire depend on the road surface condition largely. However, currently, it is difficult to detect tire performance degradation before the deterioration of vehicle dynamics in real time because tire force estimation is usually conducted by comparing the observed vehicle motion with the onboard reference vehicle-model motion. Such conventional estimators do not perform well if there is a significant difference between the vehicle and the model behavior. In this paper, a new tire state estimation method based on this tire longitudinal characteristic is proposed. In addition, the estimator for tire-road surface friction coefficient μ is proposed by using this geometric relationship. Using this method, the friction coefficient value for a real road can be determined from relatively simple calculations. Also, the advantage of this method is that it can be estimated in a small slip region before the tire loses its grip. In addition, this paper explain how to apply and the effect on the actual vehicle.


Author(s):  
Guanqun Liang ◽  
Yan Wang ◽  
Mario A. Garcia ◽  
Tong Zhao ◽  
Zhe Liu ◽  
...  

ABSTRACT Efforts to improve the performance and safety of vehicles include placing active sensing components (e.g., embedded microsensors) within tires result in intelligent tires. One application of intelligent tire is tire force estimation based on accelerometers. However, its development is limited due to the difficulty of relating the tire force to kinematical information by model-based theory. In this manuscript, a universal approach to tire forces estimation by the accelerometer-based intelligent tire is formulated and experimentally validated. First, a microelectromechanical system accelerometer-based intelligent tire prototype is established with the function of on-board monitoring of tire forces. Then, a theoretical rolling kinematics model is proposed for illustrating the mechanisms of acceleration fields, resulting from the coupling effect of rigid body motion and elastic deformation. An analytical model is formulated to estimate the vertical force in real time. Furthermore, the beam model is adopted to describe lateral deformations of the tire belt, directly linking lateral acceleration and lateral force. Finally, the lateral force can be estimated by lateral acceleration and vertical force already estimated. Based on a universal analytical model, the lateral force estimation method realizes high accuracy under different circumstances, even with unified coefficients, by clarifying and eliminating the influence of ply steer. A field test and two bench experiments have been conducted to fully validate the developed model. It can be concluded that the theoretical-analysis-based estimation model realizes an encouraging tire force estimation application with an intelligent tire hardware system.


Author(s):  
Valery Pylypchuk ◽  
Shih-Ken Chen ◽  
Nikolai Moshchuk

This paper provides a brief analytical overview of the existing methodologies for estimation of tire forces and proposes a new simple and effective methodology with sensor measurement such as elastic strains on different suspension parts. Although such an approach requires specific modeling and signal processing, it incorporates the wheel dynamics and avoids dealing with the rotating wheels. Validation using a 7DOF vehicle model and CarSim model showed good results comparing the estimated vertical tire forces and the actual tire forces obtained directly from the models. Initial vehicle tests point to the importance of sensor locations due to the complexity of 3D elastic strain fields near suspension mounts. Detailed finite element and experimental studies are required in order to optimize sensor positions, so any effects causing variations of such positions can be compensated by parameter adjustments based on specifically designed calibrating tests.


2001 ◽  
Vol 34 (1) ◽  
pp. 41-46 ◽  
Author(s):  
A. El Hadri ◽  
G. Beurier ◽  
J.C. Cadiou ◽  
N.K. M'Sirdi ◽  
Y. Delanne

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jun Yang ◽  
Wuwei Chen ◽  
Yan Wang

This paper demonstrates the implementation of a model-based vehicle estimator, which can be used for lateral tire force estimation without using any highly nonlinear tire-road friction models. The lateral tire force estimation scheme has been designed, and it consists of the following three steps: the yaw moment estimation based on a disturbance observer, the sum of the lateral tire force of two front tires and two rear tires estimation based on a least-square method, and individual lateral tire force estimation based on a heuristic method. The proposed estimator is evaluated under two typical driving conditions and the estimation values are compared with simulator data from CarSim and experimental data provided by GM. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.


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