Analysis and Prediction of Tire Cornering Properties for Different Inflation Pressures Based on Deflection Control

2020 ◽  
Author(s):  
Lihong Sun ◽  
Dang Lu ◽  
Bing Li ◽  
Yanru Suo ◽  
Danhua Xia
Keyword(s):  
Mechanical Characteristics ◽  
Prediction Method ◽  
Lateral Force ◽  
Inflation Pressure ◽  
Vehicle Performance ◽  
Magic Formula ◽  
Testing Data ◽  
Error Index ◽  
Tire Models ◽  
Be The Change

ABSTRACT The variation of inflation pressure has an important effect on the mechanical characteristics of tires that can affect vehicle performance, so the influence of inflation pressure should be taken into account in high-precision tire models. Much research has focused on tire mechanical characteristics under different inflation pressures and modeling methods, such as the Improved Magic Formula/Swift Tire model developed by Schmeitz et al. that takes inflation pressure into account. Based on a large number of tire testing data under different inflation pressures, an empirical model can be obtained, but numerous tire tests are expensive and cannot reveal the mechanism. In previous studies, most tire tests were based on load control, wherein the load is constant while the footprint, carcass, and belt stiffness can change with the variation of inflation pressure, making the cornering characteristics much more complicated. Herein, a method of deflection control is used and the contact patch is well maintained. The effect of tire inflation pressure is simplified to be the change of structure stiffness and averaged contact pressure. The effect of inflation pressure on cornering properties under deflection control is systematically observed under different tire deflection values. In addition, a prediction model extended from the Magic Formula model is proposed. The model of Schmeitz et al. [1,2] needs test data from at least three inflation pressures; the model proposed herein requires tests at only two inflation pressures. The validation shows that this prediction method has good accuracy, almost the same as that of the model of Schmeitz et al. The error index of the lateral force is at most 2%, and the error index of the self-aligning torque is 4% at the maximum.

scholarly journals Neural Network Identification of a Racing Car Tire Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jianfeng Wang ◽  
Yiqun Liu ◽  
Liang Ding ◽  
Jun Li ◽  
Haibo Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Degrees Of Freedom ◽  
Lateral Force ◽  
Dynamics Simulation ◽  
Force Model ◽  
Tire Model ◽  
Feed Forward Neural Network ◽  
Magic Formula ◽  
Tire Models

In order to meet the demands of small race car dynamics simulation, a new method of parameter identification in the Magic Formula tire model is presented in this work, based on an analysis of the Magic Formula tire model structure. A high-precision tire model used for vehicle dynamics simulation is established via this method. It is difficult for students to build a high-precision tire model because of the complexity of widely used tire models such as Magic Formula and UniTire. At a pure side slip condition, building a lateral force model is an example, which illustrate the utilization of a multilayer feed-forward neural network to build an intelligent tire model conveniently. In order to fully understand the difference between the two models, a two-degrees-of-freedom (2 DOF) vehicle model is established. The advantages, disadvantages, and applicable scope of the two tire models are discussed after comparing the simulation results of the 2 DOF model with the Magic Formula and intelligent tire model.

A Modified Dugoff Tire Model for Combined-slip Forces

2010 ◽  
Vol 38 (3) ◽  
pp. 228-244 ◽  
Author(s):  
Nenggen Ding ◽  
Saied Taheri
Keyword(s):  
Vehicle Dynamics ◽  
Tire Model ◽  
Magic Formula ◽  
Model Based ◽  
Proposed Model ◽  
Road Friction ◽  
On Line ◽  
Double Lane Change ◽  
Tire Forces ◽  
Tire Models

Abstract Easy-to-use tire models for vehicle dynamics have been persistently studied for such applications as control design and model-based on-line estimation. This paper proposes a modified combined-slip tire model based on Dugoff tire. The proposed model takes emphasis on less time consumption for calculation and uses a minimum set of parameters to express tire forces. Modification of Dugoff tire model is made on two aspects: one is taking different tire/road friction coefficients for different magnitudes of slip and the other is employing the concept of friction ellipse. The proposed model is evaluated by comparison with the LuGre tire model. Although there are some discrepancies between the two models, the proposed combined-slip model is generally acceptable due to its simplicity and easiness to use. Extracting parameters from the coefficients of a Magic Formula tire model based on measured tire data, the proposed model is further evaluated by conducting a double lane change maneuver, and simulation results show that the trajectory using the proposed tire model is closer to that using the Magic Formula tire model than Dugoff tire model.

Normalization of Tire Force and Moment Data

1993 ◽  
Vol 21 (2) ◽  
pp. 91-119 ◽  
Author(s):  
H. S. Radt ◽  
D. A. Glemming
Keyword(s):  
Surface Water ◽  
Lateral Force ◽  
Slip Angle ◽  
Inflation Pressure ◽  
Slip Ratio ◽  
Tire Force ◽  
Camber Angle ◽  
Potential Benefits ◽  
Overturning Moment ◽  
Semi Empirical

Abstract Semi-empirical theories of tire mechanics are employed to determine appropriate means to normalize forces, moments, angles, and slip ratios. Force and moment measurements on a P195/70R 14 tire were normalized to show that data at different loads could then be superimposed, yielding close to one normalized curve. Included are lateral force, self-aligning torque, and overturning moment as a function of slip angle, inclination angle, slip ratio, and combinations. It is shown that, by proper normalization of the data, one need only determine one normalized force function that applies to combinations of slip angle, camber angle, and load or slip angle, slip ratio, and load. Normalized curves are compared for the effects of inflation pressure and surface water thickness. Potential benefits as well as limitations and deficiencies of the approach are presented.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

scholarly journals A Novel Dynamic Data Driven Tire Model

2021 ◽  
Author(s):  
Junning Zhang ◽  
Shaopu YANG ◽  
Yongjie LU
Keyword(s):  
Experimental Data ◽  
Knowledge Base ◽  
Vehicle Dynamics ◽  
Mechanical Characteristics ◽  
Slip Rate ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Dynamics Simulation ◽  
Slip Angle ◽  
Tire Model

Abstract In the study of vehicle dynamics, the accurate description of tire mechanical characteristics is the basis and key of vehicle dynamics simulation. An innovative tire model is proposed based on fuzzy algorithm and a sinusoidal membership function is used to design fuzzy rules. In order to ensure the accuracy of tire behavior calculation, this model is driven by a small amount of experimental data of tire mechanical characteristics. This tire model consists of four layers of fuzzy systems, each of which has a knowledge base. The data in knowledge base I is obtained by experiments, and the data of knowledge base II is computed by the upper system, and so is the later system. Then, the input signal, the change rate of side slip angle and slip rate, is considered to improve the calculation accuracy of the model. The proposed fuzzy tire model can accurately predict the longitudinal force, lateral force and self-aligning torque of the tire under unknown conditions. Finally, by comparing the fuzzy tire model with the experimental data, it is found that the maximum RRMSE (Relative Root Mean Square Error) value is not more than 0.14. It is proved that the model can accurately describe the tire
mechanical characteristics under combined conditions.

Tire Lateral Vibration Considerations in Vehicle-Based Tire Testing

2019 ◽  
Vol 47 (3) ◽  
pp. 211-231
Author(s):  
Anton Albinsson ◽  
Fredrik Bruzelius ◽  
P. Schalk Els ◽  
Bengt Jacobson ◽  
Egbert Bakker
Keyword(s):  
Lateral Force ◽  
Testing Procedure ◽  
Operating Conditions ◽  
Tire Model ◽  
Root Cause ◽  
Tire Force ◽  
Model Parameterization ◽  
Tire Testing ◽  
Suspension Model ◽  
Tire Models

ABSTRACT Vehicle-based tire testing can potentially make it easier to reparametrize tire models for different road surfaces. A passenger car equipped with external sensors was used to measure all input and output signals of the standard tire interface during a ramp steer maneuver at constant velocity. In these measurements, large lateral force vibrations are observed for slip angles above the lateral peak force with clear peaks in the frequency spectrum of the signal at 50 Hz and at multiples of this frequency. These vibrations can lower the average lateral force generated by the tires, and it is therefore important to understand which external factors influence these vibrations. Hence, when using tire models that do not capture these effects, the operating conditions during the testing are important for the accuracy of the tire model in a given maneuver. An Ftire model parameterization of tires used in vehicle-based tire testing is used to investigate these vibrations. A simple suspension model is used together with the tire model to conceptually model the effects of the suspension on the vibrations. The sensitivity of these vibrations to different operating conditions is also investigated together with the influence of the testing procedure and testing equipment (i.e., vehicle and sensors) on the lateral tire force vibrations. Note that the study does not attempt to explain the root cause of these vibrations. The simulation results show that these vibrations can lower the average lateral force generated by the tire for the same operating conditions. The results imply that it is important to consider the lateral tire force vibrations when parameterizing tire models, which does not model these vibrations. Furthermore, the vehicle suspension and operating conditions will change the amplitude of these vibrations and must therefore also be considered in maneuvers in which these vibrations occur.

Estimation of Tire–Road Contact Features Using Strain-Based Intelligent Tire

2018 ◽  
Vol 46 (4) ◽  
pp. 276-293
Author(s):  
Hojong Lee ◽  
Min Tae Kim ◽  
Saied Taheri
Keyword(s):  
Control Systems ◽  
Lateral Force ◽  
Stability Control ◽  
Vehicle Control ◽  
Control Algorithms ◽  
Estimated Parameters ◽  
Vehicle Stability Control ◽  
Crucial Information ◽  
Sensor Measurement ◽  
Tire Models

ABSTRACT Sensors attached inside the tire near the contact area can provide crucial information on contact characteristics, e.g., slip, forces, and deformation of tires. Vehicle control systems such as antilock braking systems (ABS) and vehicle-stability control (VSC) can be enhanced by leveraging this information, since control algorithms can be updated based on directly measured parameters from intelligent tires rather than estimated parameters based on complex vehicle dynamics and on-board sensor measurement. Moreover, tire characteristics can be investigated by scrutinizing the sensor measurements on the basis of well-defined physical theories on tire mechanics. In this article, estimations of tire contact features have been studied with the circumferential strains measured inside the tire. These parameters were imported to physical tire models to finally predict lateral force, which plays the crucial role in algorithms of vehicle control systems, as well as in analysis of tire performance.

Tire Traction Assessed by Shear Force and Vehicle Performance

1973 ◽  
Vol 1 (4) ◽  
pp. 363-381 ◽  
Author(s):  
P. S. Fancher ◽  
L. Segel
Keyword(s):  
Shear Force ◽  
Correlation Method ◽  
Lateral Force ◽  
Surface Velocity ◽  
Test Surface ◽  
Vehicle Performance ◽  
Different Types ◽  
Rank Difference ◽  
Braking Force ◽  
Force Data

Abstract Tire shear force data for ten different types of passenger car tires tested on wet surfaces are studied to examine the influence of test surface, velocity, and load on the maximum lateral force, maximum braking force, maximum resultant force, and locked wheel braking force. Tire traction rankings based on these four measures are compared with each other and with rankings obtained from J-turn and diagonal braking tests on a vehicle equipped with the same types of tires using the rank difference correlation method. The findings show that rankings based on a small number of maximum lateral force tests correlate well with rankings based on J-turn tests.

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