KR20170058412A - Scalable vehicle models for indoor tire testing - Google Patents

Abstract

A method for reducing vehicle deflection when testing a tire for use in a sales segment of a vehicle by creating a scaled vehicle model by vehicle weight is disclosed. Wherein at least one of the vehicle model parameters is collected from at least one vehicle in the sales segment, and at least one vehicle model parameter is determined from the at least one vehicle model parameter, Characterized in that the scalable vehicle model parameters are applied to a multibody vehicle dynamics simulation and wherein at least one operation is applied to the scalable vehicle model and wherein the at least one manipulation is applied to the multi- A tire load history is provided to the tire tester to obtain tire wear data indicative of the vehicle in the sales segment.

Description

[0001] SCALABLE VEHICLE MODELS FOR INDOOR TIRE TESTING [0002]

Cross reference of related application

Tire manufacturers often perform wear tests on tires. Tire tread wear is affected by a number of variables other than the tire structure and tread compound, such as environmental factors (such as temperature and rainfall), driver severity (such as driving style and path configuration), road surface characteristics, The position of the center of gravity, the load transfer during maneuvers, steering kinematics, etc.) and the dynamic characteristics of the vehicle. In order to accurately measure tire tread wear and to make comparisons between the various tire models, the test is designed in such a way as to keep the effects from the environment, driver severity, road surface and vehicle constant so as not to bias the tread wear results . Vehicle characteristics can have a significant impact on the wear rate of the tire and can cause irregular wear trends. As long as all tires in the test are evaluated for the same vehicle model, the deflection introduced by the vehicle will be the same for all test tire models.

Some tires, such as original equipment manufacturer ("OEM") tires, are specifically developed for specific vehicles. In this case, the tire test should be done for a specific OEM vehicle, or the vehicle should be simulated precisely when tested in an indoor tire tester. However, many tires are designed as replacements for worn or damaged OEM tires, which are referred to as "trade tires ". Trade tires are not specifically developed for a particular vehicle but rather may have been developed for a whole market segment of vehicles, including a fairly wide variety of tire sizes and their respective load capacities. Different sizes and different tire load requirements typically require testing for different vehicles, which may have different ballast conditions. If this is the case, the inter-vehicle deflection and the abrasion performance of the test tire are inseparable. For indoor tire testing, it is desirable to "represent" a vehicle of a given segment (e.g., a front-wheel drive sedan or a pick-up truck) and to create a vehicle model that is continuously scalable for different loads.

For example, when testing a trade tire for a tire quality grade (Uniform Tire Quality Grading, "UTQG ") standard of the National Highway Traffic Safety Administration for relative evaluation of tires against tread wear The manufacturer's current practice is to test each tire for a certain number of specific vehicles. For example, a test may be conducted on a real vehicle that travels a 640 km UTQG road course in Texas. Testing may also be performed in an indoor tire tester configured to apply a predetermined force to the test tire at a predetermined tilt angle to simulate the force and tilt angle of the tire to be visible on the actual UTQG road course. This latter method typically takes less time and money than the former method. For example, outdoor UTQG testing can take more than two weeks to plan, prepare, and run. In contrast, an indoor UTQG wear test can take less than a week to plan, prepare and execute. Moreover, the outdoor UTQG test requires a person in the scheduling team who is specialized in the test, but the indoor UTQG wear test can be performed by a single person in an automatic tire wear tester. Regardless of which test method is chosen, the practice of the day was to test the tire in a particular vehicle and obtain the tread wear rating of that tire for that vehicle. The goal of this method is to develop a tire wear test that produces the most accurate results possible for a particular vehicle. Tire manufacturers then use this wear rating for their tires to make them available for a number of different vehicles within the sales segment. However, due to vehicle bias, there can be a significant difference between the tread wear rating of the tire and the actual tread life experienced when the tire is mounted on a vehicle that has not been tested. This discrepancy may lead to consumer disappointment and complaints to the tire or tire manufacturer, since the observed actual tire wear distance may be much shorter than the tire wear mileage indicated by the tire's UTQG wear rating.

What is needed is a low cost test method for tires that allows a more accurate tire wear rating for a wide variety of vehicles, which leads to high consumer confidence and satisfaction. Indoor simulation testing of a wide range of tires for a scalable vehicle model ("SVM") that mitigates the need to test tires for multiple vehicles and permits measurement of tire wear and performance without inter-vehicle deflection Lt; RTI ID = 0.0 > and / or < / RTI >

In one embodiment, a method of generating an SVM for tire design and testing is provided, the method comprising: defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire; The vehicle's wheel base, the wheel track of the vehicle, the center of gravity of the vehicle, the suspension compliance of the vehicle, the suspension kinematics of the vehicle, the steering kinematics of the vehicle, the weight distribution of the vehicle, Ballasting, vehicle front-to-rear braking ratio, vehicle auxiliary roll stiffness, vehicle unsprung mass, tire stiffness, tire longitudinal force, tire lateral force, tire alignment moment determining at least one vehicle model parameter of the at least one vehicle in the vehicle segment, the at least one vehicle model parameter including at least one of an aligning moment and a camber thrust of the tire; And determining a parameter regression function for at least one vehicle model parameter, wherein the parameter regression function provides an average value of at least one vehicle model parameter for a range of weights of the vehicles comprising the specified vehicle segment , parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) there is characterized as a function of the total weight of the SVM by a ³ , Where W is the total weight of the SVM, P (W) is at least one vehicle model parameter, C _n (W) is the regression coefficient as a function of W, A is the jounce ) And the steering angle of the vehicle. In one embodiment, C _n (W) is _{a n0 + a n1 W + a} n2 W 2 + a n3 W 3. In another embodiment, the method may further comprise generating an SVM as a function of W. In another embodiment, the method may further comprise generating at least one equation comprising a regression curve fit of the tire load as a function of W. In another embodiment, the method comprises the steps of developing a coefficient model, wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an alignment torque coefficient, wherein the coefficient model comprises a vertical load applied to the tire and a W - function; Determining a total weight dependency of the coefficient model through a coefficient regression function; the coefficient regression function is a function of W; And developing a scalable vehicle model of at least one of a tire lateral force, a tire longitudinal force, and a tire aligning moment, wherein the scalable vehicle model is a function of the normal force and the slip angle applied to the tire can do. In one embodiment, the coefficient regression function may be a bi-linear function. In one embodiment, the scalable vehicle model may be modeled as a cubic spline function.

In another embodiment, a method is provided for predicting at least one of an inclination angle and a tire force applied to a tire by a vehicle in a particular vehicle segment, the method comprising: displaying a plurality of individual vehicles having different weights and having at least one tire Defining a vehicle segment; The vehicle's suspension distance, the vehicle's center of gravity, the suspension's compliance, the vehicle's suspension kinematics, the steering kinematics of the vehicle, the weight distribution of the vehicle, the ballasting of the vehicle, the front- Wherein at least one of the at least one of the vehicle segments includes at least one of an auxiliary roll stiffness, a spring-down mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an alignment torque of the tire, Defining at least one vehicle model parameter of the vehicle; Determining a parameter regression function for at least one vehicle model parameter, the parameter regression function providing an average value of at least one vehicle model parameter for a range of weights of the vehicles making up the prescribed vehicle segment, is there is characterized as a function of the total weight of the SVM by the equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) a 3, wherein, W is the total weight of the SVM, P (W) is at least one vehicle model parameters, C _n (W) is a regression coefficient as the W function, a comprises at least one of a steering angle of Zaun's and vehicles of the vehicle Independent variable; And predicting at least one of an inclination angle and a tire force applied to the tire by the SVM through multi-object vehicle dynamics simulation. In one embodiment, C _n (W) is _{a n0 + a n1 W + a} n2 W 2 + a n3 W 3. In another embodiment, the method includes determining at least one of longitudinal acceleration and deceleration, lateral acceleration, steering angle, tilt angle, and tire load history for each tire of the SVM in at least one of the multi-object vehicle dynamics simulations Lt; RTI ID = 0.0 > SVM < / RTI > In another embodiment, the method may further comprise generating an SVM as a function of W. In another embodiment, the method may further comprise generating at least one equation comprising a regression curve fit of the tire load as a function of W. In another embodiment, the method comprises developing a coefficient model, wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an aligned torque coefficient, and wherein the coefficient model is a function of the vertical load and W applied to the tire -; Determining a total weight dependency of the coefficient model through a coefficient regression function; the coefficient regression function is a function of W; And developing a scalable vehicle model of at least one of a tire lateral force, a tire longitudinal force, and a tire aligning moment, wherein the scalable vehicle model is a function of the normal force and the slip angle applied to the tire can do. In one embodiment, the coefficient regression function may be a bi-linear function. In one embodiment, the scalable vehicle model may be modeled as a cubic spline function.

In another embodiment, a method is provided for determining a wear rate of a tire for use in a particular vehicle segment, the method comprising: defining a vehicle segment representing a plurality of individual vehicles having varying weights and having at least one tire; The vehicle's suspension distance, the vehicle's center of gravity, the suspension's compliance, the vehicle's suspension kinematics, the steering kinematics of the vehicle, the weight distribution of the vehicle, the ballasting of the vehicle, the front- Wherein at least one of the at least one of the vehicle segments comprises at least one of: an auxiliary roll stiffness, a spring-down mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an aligning moment of the tire, Defining at least one vehicle model parameter of the vehicle; Determining a parameter regression function for at least one vehicle model parameter, the parameter regression function providing an average value of at least one vehicle model parameter for a range of weights of the vehicles making up the prescribed vehicle segment, is there is characterized as a function of the total weight of the SVM by the equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) a 3, wherein, W is the total weight of the SVM, P (W) is at least one vehicle model parameters, C _n (W) is a regression coefficient as the W function, a comprises at least one of a steering angle of Zaun's and vehicles of the vehicle Independent variable; Predicting at least one of an inclination angle and a tire force applied to the tire by SVM through multibody dynamics simulation; And mounting a tire to the machine to determine a wear rate of the tire, the machine being configured to apply the tire against the simulated road surface with at least one of the predicted tire force and the predicted tilt angle and to rotate the tire at the desired speed , The machine is running, and tire wear is measured over time. In one embodiment, C _n (W) is _{a n0 + a n1 W + a} n2 W 2 + a n3 W 3. In another embodiment, the method includes determining at least one of longitudinal acceleration and deceleration, lateral acceleration, steering angle, tilt angle, and tire load history for each tire of the SVM in at least one of the multi-object vehicle dynamics simulations Lt; RTI ID = 0.0 > SVM < / RTI > In another embodiment, the method may further comprise generating at least one equation comprising a regression curve fit of the tire load as a function of W. In another embodiment, the method comprises developing a coefficient model, wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an aligned torque coefficient, and wherein the coefficient model is a function of the vertical load and W applied to the tire -; Determining a total weight dependency of the coefficient model through a coefficient regression function; the coefficient regression function is a function of W; And developing a scalable vehicle model of at least one of a tire lateral force, a tire longitudinal force, and a tire aligning moment, wherein the scalable vehicle model is a function of the normal force and the slip angle applied to the tire can do. In one embodiment, the coefficient regression function may be a bi-linear function. In one embodiment, the scalable vehicle model may be modeled as a cubic spline function.

The accompanying drawings, which are incorporated in and form a part of the specification, illustrate various exemplary methods, data sets, and results, and are used only to illustrate various exemplary embodiments. In the drawings, like elements have like reference numerals.
Figure 1 shows an exemplary result according to a P (W) regression analysis of a data set.
Figure 2 shows exemplary results according to P (W) regression analysis of the data set.
Figure 3 shows exemplary results according to P (W) regression analysis of a data set.
4 shows an exemplary method 400 for generating SVMs for tire design and indoor tire testing.
5 shows an exemplary method 500 for predicting at least one of the tilt angle and force applied to a tire.
Figure 6 shows an exemplary method 600 for determining the wear rate of a tire.
Figure 7 shows a set of vehicle model parameters that can be scaled by weight.
Figure 8 shows exemplary results according to P (W) regression analysis of a data set.
Figure 9 shows exemplary results according to P (W) regression analysis of the data set.
Figure 10 shows exemplary results according to P (W) regression analysis of a data set.
Figure 11 shows exemplary results according to P (W) regression analysis of the data set.
Figure 12 shows exemplary results according to P (W) regression analysis of a data set.
Figure 13 shows exemplary results according to P (W) regression analysis of a data set.
Figure 14 shows exemplary results according to P (W) regression analysis of a data set.
Figure 15 shows exemplary results according to P (W) regression analysis of a data set.
Figure 16 shows exemplary results according to P (W) regression analysis of a data set.
Figure 17 shows exemplary results according to P (W) regression analysis of the data set.
Figure 18 shows exemplary results according to P (W) regression analysis of a data set.
Figure 19 shows the tire quality grade wear course of the US Road Traffic Safety Administration.
Figure 20 shows exemplary results of lateral and longitudinal accelerations of a tire quality grade wear course.
Figure 21 shows exemplary results according to Force Severity Number regression analysis of a data set.
Figure 22 shows exemplary verification results of indoor and outdoor tire quality grade tests.

Trade tires can be configured to fit a segment of vehicles having a range of weights, rim dimensions, suspension geometry, steering geometry, and the like. Trade tires can be optimized to provide the best wear characteristics for segments of vehicles.

In accordance with current practice, testing of a trade tire against an actual vehicle causes the vehicle deflection to affect the test results. That is, when the tire is tested with respect to the vehicle A, the weight of the vehicle A, the rim size, the suspension geometry, the steering geometry, and the like may affect the abrasion performance of the tire differently from the vehicle B.

The SVM is different from any particular vehicle in that the SVM represents something that does not exist in a physical form. In other words, there may be no specific vehicle with the same parameter value as the SVM. Rather, the SVM represents the average vehicle for the entire vehicle segment. Then, in many respects, the SVM is a virtual vehicle that is gradual and continuously scalable, reflecting the general characteristics of the vehicle segment as a whole. Thus, the SVM can be used in place of any of the various vehicles in the vehicle segment. Replacing vehicle A, vehicle B, etc. with SVM serves to remove vehicle deflection from the indoor testing of the trade tires and eliminates the need for actual testing of the trade tires for each individual vehicle A, Instead, trade tires can be tested in an indoor tire wear tester that can be configured to simulate many different vehicle weights in a vehicle segment.

Vehicle segments can be defined in many ways. For example, a vehicle segment may be selected from an existing vehicle segment, such as a consumer market-driven segment, or may be associated with an individual or person who defines a vehicle segment, such as a performance-based or design- Lt; / RTI > can be created in any manner that groups vehicles into segments with some similarity. Possible vehicle segments include, for example, a rear-wheel drive ("RWD") pickup truck, a front-wheel drive ("FWD") sedan, and a sport utility vehicle ). In one embodiment, certain ballasting conditions such as those used by the UTQG wear test can constitute a distinct segment. The UTQG test requirements may vary across vehicle segments. For example, RWD pickup trucks may require 50% / 50% pre / post ballasting. As another example, the FWD sedan may require curb plus driver ballasting. In one embodiment, any of the various vehicle segments may be generated and analyzed. In another embodiment, the vehicle segments may be generated based on the intended vehicle on which any of the various trade tires may be applied. In one embodiment, the various vehicles of the vehicle segment may have various weights.

The at least one vehicle model parameter may be defined according to the definition of a specific vehicle segment representing a plurality of vehicles having different weights and having at least one tire. The vehicle model parameters correspond to the unique characteristics of the vehicle, but not all such characteristics are scalable by weight. Figure 7 shows the vehicle parameters (i.e., the vehicle's inherent characteristics) that can be scaled by weight. Defining the vehicle model parameters may include selecting at least one vehicle model parameter and collecting corresponding physical data from at least one vehicle in the defined vehicle segment. Therefore, the vehicle model parameters can be defined, and accordingly the vehicle-to-vehicle distance, the vehicle's wheel distance, the vehicle's center of gravity, the suspension compliance of the vehicle, the vehicle's suspension kinematics, The back-to-rear braking ratio of the vehicle, the sub-roll stiffness of the vehicle, the spring-down mass of the vehicle, the stiffness of the tire, the longitudinal force of the tire, the lateral force of the tire, the aligning moment of the tire, In one embodiment, at least the following vehicle model parameters are defined: the distance between the axes of the vehicle, the distance of the vehicle's wheel, the distance of the vehicle Center of gravity, suspension stiffness of vehicle, suspension kinematics of vehicle, steering kinematics of vehicle, front-to-rear weight distribution of vehicle, forward of vehicle A rearward braking split of the vehicle, an auxiliary roll stiffness of the vehicle, a spring-down mass of the vehicle, a stiffness of the tire in the vehicle, a longitudinal force of the tire in the vehicle, a lateral force of the tire in the vehicle, And the camber thrust of the tire in the vehicle.

In one embodiment, the various at least one vehicle model parameters are fixed among the vehicles when developing the SVM. These model parameters may include vehicle weight distribution, front-to-rear braking split, and suspension static alignment.

In one embodiment, the various at least one vehicle model parameters are scalable between vehicles when developing SVMs. The model parameters may include inter-axis distance, wheel distance, center of gravity, suspension stiffness, roll stiffness, suspension kinematics and tire stiffness.

In one embodiment, each vehicle of the selected vehicle segment is analyzed for at least one vehicle model parameter relative to the total weight of the vehicle. Data on the vehicle parameters to be analyzed can be collected from vehicles in a particular sales segment in a variety of ways. For example, testing of the FWD sedan may be performed using a plurality of different vehicles having various characteristics. The measurements taken for each vehicle may include without limitation the distance between axes and wheel distances, load distribution, steering kinematics, and suspension compliance and kinematics. Moreover, each vehicle can be driven through a force platform mounted on the floor along a predetermined path at various levels of deceleration and acceleration. The front-to-rear braking distribution and load transfer coefficient can be determined using these force platform measurements. Since the center of gravity height is not directly measured, the center of gravity of the vehicle can be estimated using the load transfer coefficient. Since vehicle inertia and shock absorbers have relatively little effect on the vehicle load transfer behavior for the type of simulated operation, the calculated values of vehicle moment and inertia and shock absorber may not be measured and scaled for the SVM.

Once the data relating to the vehicle parameters have been collected, the data may be entered into a computer configured to operate the regression analysis software. In one embodiment, regression analysis software is available from Microsoft Corporation of Redmond, Wash., Under the trade name Excel®. In one embodiment, regression analysis software is available from NC eseueseu, El elssi (NCSS, LLC) in Utah Bill Case material under the trade name NC eseueseu ⁹ (NCSS ⁹⁾ ®. In one embodiment, regression analysis software stores data points in at least two dimensions to represent data points as a single equation that relates dependent variables (e.g., vehicle parameters) to independent variables (e.g., vehicle weight) And may be any software configured to operate on a computer that is capable of plotting these data points in a chart or graph and fitting curves to data points. In one embodiment, the regression analysis software may determine a regression function that computes the mean value of the dependent variable for a range of independent variables.

Figure 1 shows exemplary results from a regression analysis of a data set. The data set represents the front suspension stiffness versus total vehicle weight. Each point indicated in the exemplary data set represents the vehicle of the vehicle segment and its total vehicle weight. For example, FIG. 1 refers to a vehicle that includes a total vehicle weight of approximately 12,000 N (2,698 lbf) with a front suspension stiffness of approximately 28.0 N / mm (160 lbf / in). In another example, FIG. 1 refers to a vehicle that includes a total vehicle weight of approximately 24,500 N (5,508 lbf), with a front suspension stiffness of approximately 39.0 N / mm (223 lbf / in). The suspension stiffness of a vehicle can play an important role in the amount of force experienced in the tire of the vehicle during driving.

The front suspension stiffness data is applied to the regression analysis to generate the SVM suspension stiffness illustrated as a line representing P (W). In one embodiment, the line representing P (W) is used in the SVM to estimate the suspension stiffness of the SVM at any of various weights of 11,615 N (2,611 lbf) to 20,835 N (4,684 lbf).

Figure 2 shows an exemplary result according to a regression analysis of a data set. The data set represents the rear camber change versus the jawshaft in various vehicles in the vehicle segment. Each line shown in the exemplary data set represents the relationship of the vehicle of the vehicle segment, and of its rear camber, to its magnetic field. The rear camber of each vehicle is approximately 0.0 degrees when the vehicle's magnetic field is approximately 0.0 mm (0.0 inch). For example, FIG. 2 shows that the vehicle 6 has a rear camber of approximately -1.0 degrees when the vehicle's azimuth is approximately 50.0 mm (2.0 inches). The rear camber of the vehicle can play an important role in the inclination angle experienced by the vehicle tire during driving.

In one embodiment, the at least one vehicle model parameter is characterized by regression analysis as a function of the total weight ("W") of the SVM. In one embodiment, at least one of the vehicle model parameter regression using the equation _{P (W) = C 0 (} W) + C 1 (W) A + C 2 (W) A 2 + C 3 (W) A 3 Analysis. P (W) may be at least one vehicle model parameter. C _n (W) is the number one coefficient as a function of W and W + a _n1 a + _n0 a _{n2 n3} W ² + W ³ a. A may be an independent variable that includes at least one of the vehicle's azimuth and the vehicle's steering angle.

Rear camber change versus span data can be applied to regression analysis to produce the SVM rear camber variation illustrated as a series of lines representing P (W). Each line representing P (W) is associated with a particular vehicle weight. In one embodiment, the line representing P (W) for a particular vehicle weight is used to estimate the relationship between the rear camber change in the span of the SVM of that weight.

In one embodiment, each of the at least one vehicle model parameter is characterized by regression analysis in the same manner as the front suspension stiffness data shown in Fig. 1 or the rear camber change versus geomance data shown in Fig.

Figure 3 shows an exemplary result according to a regression analysis of the data set shown in Figure 2. Figure 3 shows the regression lines for SVM weights of 16,680 N (3,750 lbf) to 17,790 N (4,000 lbf) weighed by pre-regression analysis rear camber variation versus pre-regression for various vehicles in the vehicle segment. The regression lines represent P (W) and allow scalable linear predictability to determine the backward camber versus the sphere in the SVM.

In one embodiment, the front axle load of the vehicle has been found to be approximately 60% of the total vehicle weight over the entire range of 52 vehicles. In this case, C ₁ (W), C ₂ (W), and C ₃ (W) were zero and C ₀ (W) decreased to a ₀₀ = 0.60. Other examples include wheel distances and inter-axis distances as shown in both Figures 8 and 9, respectively. The vehicle wheel distance and inter-axis distance model can only be treated as a linear function of the total vehicle weight. Thus, the _{P (W) = C 0 (} W) = a 00 + a 01 W. A ₀₀ = 1240 and a ₀₁ = 0.088 in the case of the wheel distance model, and a ₀₀ = 1255 and a ₀₁ = 0.218 in the case of the inter-axis distance model.

Depending on the characteristics of the at least one vehicle model parameter as a function of W, a computer configured to operate the multi-object vehicle dynamic simulation software package may be used to predict at least one of the tilt angle and the tire force of the at least one tire. The computer configured in this manner may convert the input data comprising the characterization of at least one vehicle model parameter as a function of W into output data comprising a tire load history configured without limitation to tire force and tilt angle data. In one embodiment, a multi-object vehicle dynamics simulation software package ("MBVDSS") is available from Mechanical Simulation Corporation of Ann Arbor, Michigan under the trade designation CarSim. In another embodiment, MBVDSS is available from MSC Adams < (R) >, available from MSC Software Corporation, Newport Beach, Calif .; And any commercially available or proprietary multipurpose object including, without limitation, SimCreator®, available from Realtime Technologies, Inc. of the Royal Oak, Mich., USA Vehicle dynamics simulation software. In one embodiment, the MBVDSS converts input data, which is computer-operated and includes characterization of at least one vehicle model parameter as a function of W, into output data comprising a tire load history configured without limitation to tire force and tilt angle data Lt; / RTI >

In one embodiment, inputting at least one vehicle model parameter as a function of W into MBVDSS can be used to develop an individual SVM with scalable vehicle attributes in a representative set of weights. In another embodiment, inputting at least one vehicle model parameter as a function of W into the MBVDSS can be used to develop an individual SVM with scalable vehicle attributes in a representative set of corner loads.

Vehicle models generated using the SVM approach may be intended for indoor wear and durability applications, as opposed to handling, traveling, heavy braking, crash, and noise applications. Vehicle handling associated with wear and durability applications can be virtually quasi-static with limited acceleration and deceleration levels generally not exceeding about 0.5 g. Thus, the vehicle inertia and shock absorber may have relatively little impact on the vehicle load transfer behavior and suspension spring constant, and other compliance may be linearized. In addition, it is assumed that the Kassim® MBVDSS internal tire model combined type slip prediction from pure cornering and pure braking forces and moments is sufficiently accurate. The vehicle can be assumed to have a completely independent suspension.

In one embodiment, the SVM is presented in MBVDSS, and the SVM is simulated with a set of standard operations to provide results for indoor UTQG wear modeling on the wear test drums. In another embodiment, the SVM is applied to at least one operation in MBVDSS to determine at least one of longitudinal acceleration and deceleration, lateral acceleration, steering angle, tilt angle, and tire load history. The tire load history for each tire of the SVM may be generated based on the application of the SVM to at least one operation in the MBVDSS. The tire load history may include at least one of the radial, lateral, and longitudinal forces of the tire, and the camber. The tire load history may be provided at the input of a laboratory tire tester or tire model. The laboratory tester may include an accelerated indoor wear tester that is used to predict tire wear performance when used by a consumer. The tire model may include a finite element analysis ("FEA") model. Depending on the application of the SVM to at least one operation in the MBVDSS, at least one expression for the tire force and the tilt angle according to the tire position on the SVM can be generated.

In one embodiment, at least one equation can be used to drive an indoor tire testing machine. Indoor tire testing machines can test tires for at least one of durability and wear. In another embodiment, at least one expression may be used to provide information about the FEA.

In one embodiment, the SVM is characterized by measuring the tilt angle and the three directional forces (F _x , F _y , F _z ) experienced by each of the tires during at least one simulated operation. The force F _x is the longitudinal force applied to the tire in the contact patch of the tire parallel to the direction of rotation. The force F _y is the lateral force applied to the tire in the contact patch of the tire perpendicular to the direction of rotation. The force (F _z ) is the normal force applied to the tire in the contact patch of the tire.

In one embodiment, the SVM is characterized by measuring the acceleration (A _x , A _y ) and velocity (V _x ) of the vehicle when three directional forces and tilt angles are measured. The acceleration (A _x ) is the longitudinal acceleration of the vehicle. The acceleration ( _Ay ) is the lateral acceleration of the vehicle. The speed (V _x ) is the longitudinal speed of the vehicle.

In one embodiment, equations that relate the vehicle acceleration (A _x , A _y ) and velocity (V _x ) to the tilt angle experienced by each of the tires and the three directional forces (F _x , F _y , F _z ) . In one embodiment, the equation is F _x = f ₁ (A _x , A _y , V _x ); F _y = f ₂ (A _x , A _y , V _x ); F _z = f ₃ (A _x , A _y , V _x ); And a _{IA = f 4 (A x,} A y, V x).

In one embodiment, the longitudinal acceleration (A _x ) and the lateral acceleration (A _y ) experienced by the SVM in at least one simulated operation are measured. In another embodiment, the longitudinal velocity (V _x ) of the SVM in at least one simulated operation is measured.

In one embodiment, when the SVM is simulated or driven through an actual additional operation, the tilt angle and force data representing the tilt angle and force to be experienced by the SVM are predicted. In one embodiment, the longitudinal acceleration (A _x ), lateral acceleration (A _y ), and longitudinal velocity (V _x ) of the SVM in at least one simulated operation are _calculated using the formula F _x = f ₁ (A _x , A _y , V _x ); F _y = f ₂ (A _x , A _y , V _x ); _{Fz = f 3 (A x,} A y, V x); And the vehicle acceleration (A _x , A _y ) and velocity (V _x ) at IA = f ₄ (A _x , A _y , V _x ).

The scaling-enabled characteristics of the vehicle can be divided into three broad categories, such as suspension compliance and kinematics, steering kinematics, and tire forces and moments (see FIG. 7). Suspension characteristics can affect tire forces and moments generated in simulations across road courses. Suspension kinematics can include both static alignment settings and changes in the toe and camber due to the mood and rebound. To characterize the suspension kinematics, in one example the independent variable A may be a magnetic or suspension deflection. The vehicle plotted in FIG. 10 was selected to demonstrate the total vehicle weight dependency or independence of the suspension kinematics. In one embodiment, the gross weight is dependent on the Zoness-toe relationship. Fig. 11 shows the self-in-toe measurement of the vehicle E from Fig. Measurements can be performed on a K & C machine. These diagrams show that the third order polynomial can be used to accurately represent these measurements. The resulting third-order polynomial approximation of the front suspension toe change for vehicle E is T o = -0.0099-0.0085 A ^-5 × 10 ^-5 A ² -3 × 10 ^-8 A ³ . This cubic polynomial approximation process was applied to 52 vehicles and then regression analysis was performed for each set of coefficients of the cubic polynomial approximation as a function of W. Regression analysis yielded C ₁ (W) = a ₁₀ + a ₁₁ W = - 1.322 × 10 ^-3 - 3.885 × 10 ^-7 W, which is plotted in FIG. For applications of interest, C ₁ (W), which is the slope at 0 = 0, may be the most important. The regression results demonstrate a small sensitivity of the initial slope to W, although there are significant deviations from vehicle to vehicle.

The parameters C ₀ (W), C ₂ (W), and C ₃ (W) were determined in the same manner. The end result of the characterization of the front suspension jaws-toe for this SVM is shown in Fig. These functions represent a small sensitivity to the total vehicle weight and eliminate relatively large individual vehicle deflection.

Steering characteristics can affect tire lateral forces at low speed, tight corners, especially in urban operation. The Ackerman error can be chosen to demonstrate how the regression model is applied to characterize steering kinematics. The independent variable A is the average steering angle of the front wheels. Fig. 14 shows the common error measurements on the left turn for the same subgroup of vehicles used in the self-trajectory example. Here, the common error can be defined as the difference between the average steering angle of the front wheels and the theoretical true angle. The process of characterizing steering kinematics may be similar to that applied in the characterization of suspension kinematics. The first step of characterization may be to determine a polynomial approximation to each individual common error set of measurements as a function of the steering angle. The second step may be to return the polynomial coefficients as a function of W. However, regression analysis did not show significant dependence of steering kinematics on W.

Figure 15 shows the resulting curve of the common error at the left turn for the SVM expressed as P (W) = common error = 0.0148 A + 0.006 A ² , where A is the steering angle. Right common error can be determined in the same way. Since there is no vehicle weight dependency, a single curve can be applied to the SVM regardless of vehicle weight.

The tire force and moment properties ("F &M") may include spring constant, longitudinal force (F _x ), lateral force (F _y ), alignment moment (M _z ), and camber thrust. Of these properties, the process of characterizing F _x , F _y and M _z can be more complex. The characterization of F _y can prove the characterization process because the processes for characterizing F _x and M _z are similar. The spring constant and camber thrust can be characterized using the process described above for suspension compliance and kinematics, respectively. In one embodiment, all tires F & M can be characterized in pure cornering or pure braking conditions. In another embodiment, the Kasim® MBVDSS internal tire model may be used.

The characterization of the _Fy of a tire for an SVM application can consist of the following three steps:

One. Development of cornering coefficient (CC) model. The cornering coefficient of the tire is a function of the total vehicle weight as well as the applied vertical load.

2. Determine total vehicle weight dependency of CC through regression analysis.

3. Develop F _y model from CC using third-order spline function assuming F _y saturation at 10 ° slip angle.

Characterization of F _y requires knowledge of the CC. Figure 17 shows the measurements of CC for tires from the previous 52 vehicle models. As a first step, based on the experimental data, a bilinear CC model as a function of the applied vertical load can be assumed as shown by the dashed line in FIG. This dual linear model assumes that the tire cornering constant is constant up to a vertical load of 3,000 N (674 lbf). Then, it can be assumed that CC decreases as the vertical load increases. For each of the 52 vehicles, a separate dual linear model can be created accordingly. As a second step, the constant part of the dual linear CC model can be regressed as a function of W. Dependencies for W in the dual linear CC model are graphically shown in Fig. The final step is shown in Fig. 18 and is a tire F _y model of an SVM vehicle with a weight of 13,350 N (3,001 lbf). The lateral force generated by the tire can be a function of the applied vertical load as well as the slip angle. These curves can be generated using a cubic spline whose initial slope is determined from the dual linear CC model shown in Fig. 17 for an SVM vehicle of 13,350 N (3,001 lbf). The spline function may assume that the lateral force is maximum at a slip angle of 10 degrees. This assumption can be valid since wear and durability manipulations typically produce a slip angle of only about 4 degrees. Thus, the function of the lateral force vs. slip angle may also be dependent on the total vehicle weight.

Characterization of tire F _x and tire M _z follows the same general three-step process as described above for CC. However, the characterization of the tire F _x and the tire M _z is different in that the tire F _x characterization comprises the slip stiffness factor, while the tire M _z characterization comprises the alignment torque coefficient, rather than including the CC. The concept and calculation including CC, slip stiffness coefficient, and alignment torque coefficient are well known in the field of tire modeling and design.

In one embodiment, predicted tire force and tilt angle data may be used to drive an indoor tire wear tester. The indoor wear test of a tire may include the application of a tire to a wear test drum. The tire can be mounted on a rim, and the rim is attached to a mechanism that includes an axle. The tire may be inflated to an intended operating pressure of the tire or to any desired possible pressure. The abrasion test drum may provide a rotating cylindrical surface configured to simulate a road surface. The tire may be contacted against the abrasion test drum to simulate a tire operating on the road surface. The device may be configured to apply the tire against the wear test drum at a specific force and angle of inclination. The applied force of the tire against the abrasion test drum may indicate the load of the tire due to the weight of the vehicle, the cargo of the vehicle, the acceleration of the vehicle, the deceleration of the vehicle, the speed of the vehicle, cornering of the vehicle, The application angle of the tire against the abrasion test drum may indicate the inclination angle of the tire due to the jaws, weight of the vehicle, acceleration of the vehicle, deceleration of the vehicle, cornering of the vehicle, and the like. In one embodiment, the indoor tire wear tester may be provided by MTS Systems Corporation, Eden Prairie, Minn., Under the trade name MTS Tire Tread Wear Simulation System. In another embodiment, the indoor tire wear tester may be any machine or system configured to test the wear rate of the tire as described above.

In another embodiment, predicted tire force and tilt angle data is used to drive an indoor tire testing machine. The indoor tire testing machine can be configured to test the durability of the tire. In one embodiment, the indoor tire testing machine is configured to test tire wear. In another embodiment, predicted tire force and tilt angle data are used to input information to the FEA.

4 shows an exemplary method 400 for generating an SVM for tire design and testing. The method includes defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire (step 402). The method includes the steps of: determining a distance between a vehicle and a vehicle, a distance of the vehicle, a center of gravity of the vehicle, suspension compliance of the vehicle, suspension kinematics of the vehicle, steering kinematics of the vehicle, weight distribution of the vehicle, At least one of at least one of the following: at least one of the following: at least one of: a vehicle's sub-roll stiffness, a vehicle's sprung mass, the rigidity of the tire, the longitudinal force of the tire, the lateral force of the tire, And defining a vehicle model parameter (step 404). The method includes determining a parameter regression function of at least one vehicle model parameter (step 406), wherein the regression function is adapted to determine an average value of at least one vehicle model parameter for a range of weights of the vehicles making up the specified vehicle segment service and parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) function of the total weight of the SVM by a ³ a Where W is the total weight of the SVM, P (W) is at least one vehicle model parameter, C _n (W) is the regression coefficient as a function of W, A is the vehicle's azimuth, And an independent variable including at least one of the steering angles.

5 shows an exemplary method 500 for predicting at least one of the tilt angle and force applied to a tire by a vehicle in a particular vehicle segment. The method includes defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire (step 502). The method includes the steps of: determining a distance between a vehicle and a vehicle, a distance of the vehicle, a center of gravity of the vehicle, suspension compliance of the vehicle, suspension kinematics of the vehicle, steering kinematics of the vehicle, weight distribution of the vehicle, , At least one of an auxiliary roll stiffness of the vehicle, a sprung mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an aligning moment of the tire, and a camber thrust of the tire And defining at least one vehicle model parameter of the at least one vehicle (step 504). The method includes determining a parameter regression function of at least one vehicle model parameter (step 506), wherein the regression function comprises calculating a mean value of at least one vehicle model parameter for a range of weights of the vehicles making up the specified vehicle segment service and parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) function of the total weight of the SVM by a ³ a Where W is the total weight of the SVM, P (W) is at least one vehicle model parameter, C _n (W) is the regression coefficient as a function of W, A is the vehicle's azimuth, And an independent variable including at least one of the steering angles. The method may include estimating at least one of the tilt angle and the tire force applied to the tire by the SVM through multi-object vehicle dynamics simulations (step 508).

6 shows an exemplary method 600 for determining the wear rate of a tire for use in a particular vehicle segment. The method includes defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire (step 602). The method includes the steps of: determining a distance between a vehicle and a vehicle, a distance of the vehicle, a center of gravity of the vehicle, suspension compliance of the vehicle, suspension kinematics of the vehicle, steering kinematics of the vehicle, weight distribution of the vehicle, , At least one of an auxiliary roll stiffness of the vehicle, a sprung mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an aligning moment of the tire, and a camber thrust of the tire And defining at least one vehicle model parameter of the at least one vehicle (step 604). The method includes determining a parameter regression function of at least one vehicle model parameter (step 606), the regression function determining an average value of at least one vehicle model parameter for a range of weights of the vehicles making up the defined vehicle segment service and parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) a + C 2 (W) a 2 + C 3 (W) function of the total weight of the SVM by a ³ a Where W is the total weight of the SVM, P (W) is at least one vehicle model parameter, C _n (W) is the regression coefficient as a function of W, A is the vehicle's azimuth, And an independent variable including at least one of the steering angles. The method may include estimating at least one of the tilt angle and the tire force applied to the tire by the scalable vehicle model through multi-object vehicle dynamics simulation (step 608). The method includes applying a tire to a machine to determine a wear rate of the tire (step 610). The machine applies the tire against the simulated road surface with at least one of the predicted tire force and the predicted tilt angle, The tire is configured to rotate at a desired speed and the machine is actuated to measure tire wear over time.

One application of the SVM for indoor tire wear testing may be the UTQG test for determining the relative rating of tires for tread wear. It is desirable to quickly and accurately evaluate a number of different prototype tire designs as well as different sizes in an indoor tire wear tester to predict the UTQG tread wear rating during a tire development process for a new line or model of trade tires. To this end, there is a need for an SVM that represents a vehicle segment, such as a pick-up truck with the same front and rear ballasting in nominal alignment. The tires to be subjected to the UTQG test may be placed in an indoor tire testing apparatus including an abrasion test drum. The wear test drums provide a rotating surface that abuts the tire to simulate the road surface. The test apparatus provides a mechanism for varying the force between the tire and the rotating surface. The speed of the rotating surface and the inclination angle of the tire can also be varied. The indoor UTQG abrasion test can be performed in a fraction of the time it takes to perform the outdoor UTQG test. Moreover, the indoor UTQG wear test can provide more accurate and consistent data, which eliminates many of the outdoor UTQG test related parameters, including without limitation driver, road surface, weather and ambient conditions, and vehicle deflection Because. The more accurate the test data, the more accurate the UTQG estimate for each tire. The more accurate the UTQG rating for the tire, the greater the consumer's confidence in the tire manufacturer, thereby increasing the consumer's brand satisfaction and loyalty.

Model identification can be performed by comparing the tire load history generated by the SVM to that from an individual vehicle used in the SVM development. For this comparison, the Kassim® MBVDSS model was developed using SVM characteristics for nine different vehicle weights of 11,100 N (2,495 lbf) to 22,250 N (5,002 lbf). These tire load histories were then compared to individual load histories from the 52 vehicles that were originally used to develop the FWD SVM. To make the comparison as realistic as possible, the tire load history was generated from each of the vehicle models for the actual wear path. For these examples, the acceleration and GPS measurements were collected at 1 meter intervals over a 640 km UTQG wear path (see FIG. 19) of the U. S. Department of Transportation near San Angel, Texas, USA. The color contour diagrams of the lateral and longitudinal accelerations are shown in Fig. These road courses consist of left and right cornering less than 0.3 gram, acceleration less than 0.3 gram, and deceleration less than 0.4 gram.

For a quick assessment of the tire force history at a distance of 640 km, we introduced a metric called force severity ("FSN") which evaluates the load history severity for tread wear.

Where: F _y = lateral force;

F _x = longitudinal force;

F _z = radial or normal force; And

n = the total number of measurements.

The summation is based on the distance per meter and is normalized to the sum. The physical phenomenon behind this simple formula is that the degree of severity can be related to the rate of going through the tire. The uniformity is proportional to the product of the slip and force integrated over the traveled distance. The product of slip angle and lateral force is approximated by the product of (F _y / F _z ) and lateral force F _y . Similar inference applies to longitudinal slip and longitudinal forces. The camber angle is only introduced in relation to the lateral forces generated by the camber thrust stiffness of the tire.

Figure 21 shows the FSN values for each of the individual vehicle models and the nine SVM weights. All four tire positions were averaged to simulate tire rotation. Static alignment was set to nominal. The SVM model is not limited to only the nine vehicle weights used. This can be used for any vehicle load. These nine conditions appeared essentially linear with respect to the FSN metric. This linearity was not enforced by any of the assumptions made during the derivation of the SVM vehicle characteristics. 52 individual vehicles were clustered with SVM FSN values. Some of the vehicles were roughly 50% stricter than the SVM equivalent value, at least based on this FSN metric, but in most cases the vehicles are within ± 20% of their SVM equivalent value.

The wear rating is assigned to each test tire and can be normalized by the wear rate of the P195 / 75R14 Course Monitoring Tire ("CMT"). The test requires a load of 4,580 N (1,030 lbf) for these tire sizes in all four positions of the vehicle. To achieve this ballasting requirement, pick-up trucks are used most often. Nevertheless, there is a limited range of ballasting capabilities for trucks of each size. When the sizes of the test tires are different, they often have significantly different load requirements than the CMT and accordingly require trucks of different sizes because there is a limited range of physically possible ballasting. This may make it impossible to rotate the CMT and test tires between vehicles. Thus, inter-vehicle deflection is introduced.

A second SVM with a different coefficient can be developed for a pickup truck having the same ballasting at all four tire positions. This second model can be developed in the same manner as discussed above, using small size to 3/4 tone sizes and a wide range of trucks from various manufacturers. This second SVM can be used to generate a load history for tires of different sizes (e.g., tires of five different sizes), in addition to the CMT size for the 640 km UTQG path. This load history can then be used to program the tire wear tester. Since this test is a rotated position test, four different load histories can be used for each interior tested tire, one for each position of the vehicle. The load history can be rotated for each tire using the same rotation schedule used for outdoor testing. Furthermore, the outdoor UTQG wear test was conducted on six test vehicles (for example, five vehicles with test tires and one with CMT), with varying loads from 3,830 N (861 lbf) to 6,490 N (1,459 lbf) Vehicle). ≪ / RTI > The UTQG wear rating for both outdoor and indoor wear tests is shown in FIG. In outdoor testing, the range of wear ratings for each of the four tires in the five test vehicles is shown as an individual data point. In the indoor wear test, two tires of each tire design were tested. The indoor wear rating of the P185 / 65R16 and P215 / 45R17 tires was lower by 100 grade points in the outdoor test, although it was found that there was generally a match between the indoor wear test results and the outdoor wear test results.

The load history for the indoor wear test can be generated from the SVM with the required load for each tire size and the outdoor tire load history can also be specific for each of the selected individual vehicles based on the static load requirements of the tires. The FSN value can be calculated for each load history to estimate if the discrepancy between the indoor wear rating and the outdoor wear rating was a result of the difference in the SVM load history versus the specific vehicle load history. The CARCIM® MBVDSS vehicle model can be generated for each of the outdoor vehicles under the same suspension alignment and appropriate ballast conditions used during the test. Each ratio of FSN to equivalent SVM FSN of these test vehicles can then be used to calculate the vehicle deviation. If the ratio is greater than 1, the vehicle will be expected to have more severe tire wear than the outdoor test tire of the average vehicle. Conversely, if the ratio is less than one, then the particular vehicle would be expected to be less severe to tire wear.

Each vehicle deflection can be calculated including the vehicle used in the CMT tires. The outdoor wear rate can then be adjusted by dividing by this value to eliminate inter-vehicle effects. The wear rate can be recalculated using these adjusted wear rates. This adjusted wear rating is also shown as a solid circle in FIG. The two tire sizes that were not previously consistent with the indoor wear results by more than a 100 grade point can be adjusted to new values that almost exactly match the indoor wear results. The 235 / 65R17 size can also be moved closer to the 1: 1 line, and the remaining two sizes can be adjusted or not adjusted at all, because their vehicle deflection ratio is very close to unity.

This adjustment process can significantly improve the correlation between indoor wear results and outdoor wear results. In this particular example, two of the five tires were deflected by 27 to 29% for the harsh side, one for the harsh side by 6%, and two for less than 1%. Inter-vehicle deflection may change the tire wear rate, making it difficult to evaluate the wear performance. This is especially true when evaluating tires of different sizes that require testing on different vehicles. Unfortunately, the high correlation level of the adjusted result shown in FIG. 22 can not be consistently obtained. In this particular case, both the outdoor and indoor wear tests were conducted on tires from the same factory, including CMT, and all five sizes were tested together both indoors and outdoors. In addition, it is difficult to obtain accurate vehicle models due to the wide variety of vehicles used for outdoor testing. In many cases, vehicles are older models with mileage up to 500,000 km. In addition, the room test was conducted at a constant ambient temperature, and no attempt was made to match the range of ambient temperatures from the outdoor test.

As the term " includes " or " including "is used in the specification or claims, when the term is interpreted when employed in a transitional word in the claims, the term" Comprising "is intended to be inclusive in a manner similar to the term " comprising ". It is also intended to mean "A or B, or both ", as long as the term" or "is employed (e.g., A or B). When the present inventors wish to express "only A or B but not both, " the term" A or B alone, not both, Accordingly, the use of the term "or" in this specification is a comprehensive use and not an exclusive use. See Bryan A. Garner, A Dictionary of Modern Legal Usage 624 (2d Ed. 1995). It is also intended that the terms "within" or "within ", as used in the specification or claims, shall mean additionally" on "or" to on. " As long as the term "substantially" is used in the specification or claims, it is contemplated to take into account the precision available in the manufacture of tires, which is +/- 6.35 mm (0.25 inches) in one embodiment. As used in the specification or claims, the term "optionally" refers to a condition of a component that enables a user of the device to activate or deactivate a feature or function of the component as needed or required for use of the device . As used in the specification or claims, the term "operably connected" is intended to mean that the components being identified are connected in such a way as to perform the specified function. As used in the specification and claims, the singular forms "a," "an," and "the" include plural forms. Finally, where the term " about "is used in conjunction with a number, it is intended to include +/- 10% of that number. In other words, "about 10" can mean 9 to 11.

While the present application has been illustrated by the description of the embodiments and the embodiments have been described in considerable detail, the inventors intend to limit the scope of the appended claims to such details, It is not limited thereto. Additional advantages and modifications that would benefit from the present disclosure will readily appear to those skilled in the art. Accordingly, the present application is not intended to be limited in its breadth of the specification, to the particulars disclosed, to the illustrated examples or to any device mentioned. Modifications may be made from such details, examples and apparatuses without departing from the spirit or scope of the overall inventive concept.

Claims (20)

1. A method for generating a scalable vehicle model for tire design and testing,
Defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire;
The vehicle's wheel base, the wheel track of the vehicle, the center of gravity of the vehicle, the suspension compliance of the vehicle, the suspension kinematics of the vehicle, the steering kinematics of the vehicle, the weight distribution of the vehicle, The braking force of the vehicle in the front-to-rear direction, the auxiliary roll stiffness of the vehicle, the unsprung mass of the vehicle, the rigidity of the tire, the longitudinal force of the tire, the lateral force of the tire, aligning torque, and camber thrust of the tire; determining at least one vehicle model parameter of at least one vehicle in the vehicle segment; And
Determining a parameter regression function for at least one vehicle model parameter,
Wherein the parameter regression function provides an average value of the at least one vehicle model parameter for a range of weights of the vehicles constituting the prescribed vehicle segment,
The parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) A + C 2 (W) A 2 + C 3 (W) function of the total weight of the scalable vehicle model by A ³ , ≪ / RTI >
Where W is the total weight of the scalable vehicle model,
P (W) is the at least one vehicle model parameter,
C _n (W) is the regression coefficient as a function of W,
Wherein A is an independent variable comprising at least one of a vehicle jounce and a steering angle of the vehicle. The method of claim 1, wherein C _n (W) is a _n0 + a _n1 W + a _n2 W ² + a _n3 W ³ . The method of claim 1, further comprising generating the scalable vehicle model as a function of W. 2. The method of claim 1, further comprising generating at least one equation comprising a regression curve fit of a tire load as a function of W. The method according to claim 1,
Developing a coefficient model for at least one tire characteristic,
Wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an alignment torque coefficient,
The coefficient model being a function of the vertical load and W applied to the tire;
Determining a total weight dependency of the coefficient model through a coefficient regression function,
The coefficient regression function is a function of W; And
Developing at least one scalable tire model of tire lateral force, tire longitudinal force, and tire aligning moment,
Wherein the scalable tire model is a function of the vertical load and slip angle applied to the tire. 6. The method of claim 5, wherein the coefficient regression function is a bi-linear function and the scalable tire model is modeled as a cubic spline function. A method for predicting at least one of an inclination angle and a force applied to a tire by a vehicle in a specific vehicle segment,
Defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire;
The vehicle's suspension distance, the vehicle's center of gravity, the suspension's compliance, the vehicle's suspension kinematics, the steering kinematics of the vehicle, the weight distribution of the vehicle, the ballasting of the vehicle, the front- At least one of the at least one of the vehicle segment including at least one of an auxiliary roll stiffness, a spring-down mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an alignment torque of the tire, Defining at least one vehicle model parameter of the vehicle of the vehicle;
Determining a parameter regression function for at least one vehicle model parameter,
Wherein the parameter regression function provides an average value of the at least one vehicle model parameter for a range of weights of the vehicles constituting the prescribed vehicle segment,
The parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) A + C 2 (W) A 2 + C 3 (W) function of the total weight of the scalable vehicle model by A ³ , ≪ / RTI >
Where W is the total weight of the scalable vehicle model,
P (W) is the at least one vehicle model parameter,
C _n (W) is a regression coefficient as a function of W,
A is an independent variable comprising at least one of a vehicle's azimuth and a vehicle's steering angle; And
Predicting at least one of an inclination angle and a tire force applied to the tire by the scalable vehicle model through a multi-object vehicle dynamics simulation. 8. The method of claim 7, wherein C _n (W) is a _n0 + a _n1 W + a _n2 W ² + a _n3 W ³ . 8. The method of claim 7, wherein for each tire of the scalable vehicle model
Longitudinal acceleration and deceleration,
Lateral acceleration,
Steering angle,
Inclination angle, and
Tire load history
Further comprising applying the scalable vehicle model to at least one maneuver in the multi-object vehicle dynamics simulation to determine at least one of the multi-object vehicle dynamics simulation. 8. The method of claim 7, further comprising generating the scalable vehicle model as a function of W. 8. The method of claim 7, further comprising generating at least one equation comprising regression curve fitting of tire load as a function of W. 8. The method of claim 7,
Developing a coefficient model for at least one tire characteristic,
Wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an alignment torque coefficient,
The coefficient model being a function of the vertical load and W applied to the tire;
Determining a total weight dependency of the coefficient model through a coefficient regression function,
The coefficient regression function is a function of W; And
Developing at least one scalable tire model of tire lateral force, tire longitudinal force, and tire aligning moment,
Wherein the scalable tire model is a function of the vertical load and slip angle applied to the tire. 13. The method of claim 12, wherein the coefficient regression function is a bilinear function and the scalable tire model is modeled as a cubic spline function. CLAIMS What is claimed is: 1. A method of determining a wear rate of a tire for use in a particular vehicle segment,
Defining a vehicle segment representing a plurality of individual vehicles having different weights and having at least one tire;
The vehicle's suspension distance, the vehicle's center of gravity, the suspension's compliance, the vehicle's suspension kinematics, the steering kinematics of the vehicle, the weight distribution of the vehicle, the ballasting of the vehicle, the front- At least one of the at least one of the vehicle segment including at least one of an auxiliary roll stiffness, a spring-down mass of the vehicle, a stiffness of the tire, a longitudinal force of the tire, a lateral force of the tire, an alignment torque of the tire, Defining at least one vehicle model parameter of the vehicle of the vehicle;
Determining a parameter regression function for at least one vehicle model parameter,
Wherein the parameter regression function provides an average value of the at least one vehicle model parameter for a range of weights of the vehicles constituting the prescribed vehicle segment,
The parameter regression function equation _{P (W) = C 0 (} W) + C 1 (W) A + C 2 (W) A 2 + C 3 (W) function of the total weight of the scalable vehicle model by A ³ , ≪ / RTI >
Where W is the total weight of the scalable vehicle model,
P (W) is the at least one vehicle model parameter,
C _n (W) is a regression coefficient as a function of W,
A is an independent variable comprising at least one of a vehicle's azimuth and a vehicle's steering angle;
Predicting at least one of an inclination angle and a tire force applied to the tire by the scalable vehicle model through a multibody vehicle dynamics simulation; And
Determining a wear rate of the tire by mounting a tire on the machine,
The machine is adapted to apply the tire against the simulated road surface with at least one of the predicted tire force and the predicted tilt angle and to rotate the tire at a desired speed,
The machine is activated,
Wherein wear of the tire is measured over time. 15. The method of claim 14, wherein C _n (W) is a _n0 + a _n1 W + a _n2 W ² + a _n3 W ³ . 15. The method of claim 14, wherein for each tire of the scalable vehicle model
Longitudinal acceleration and deceleration,
Lateral acceleration,
Steering angle,
Inclination angle, and
Tire load history
Further comprising applying the scalable vehicle model to at least one operation in the multi-object vehicle dynamics simulation to determine at least one of the multi-object vehicle dynamics simulation. 15. The method of claim 14, further comprising generating the scalable vehicle model that is scalable as a function of W. 15. The method of claim 14, further comprising generating at least one equation comprising regression curve fitting of tire load as a function of W. 15. The method of claim 14,
Developing a coefficient model for at least one tire characteristic,
Wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an alignment torque coefficient,
The coefficient model being a function of the vertical load and W applied to the tire;
Determining a total weight dependency of the coefficient model through a coefficient regression function,
The coefficient regression function is a function of W; And
Developing at least one scalable tire model of tire lateral force, tire longitudinal force, and tire aligning moment,
Wherein the scalable tire model is a function of the vertical load and slip angle applied to the tire. 20. The method of claim 19, wherein the coefficient regression function is a bilinear function and the scalable tire model is modeled as a cubic spline function.

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