Scalable Vehicle Models for Indoor Tire Testing

• US 20150057951A1
• Filed: 10/31/2014
• Published: 02/26/2015
• Est. Priority Date: 12/28/2012
• Status: Active Grant

• Current Assignee: Bridgestone Americas Tire Operations LLC (Bridgestone Corporation)
• Original Assignee: Bridgestone Americas Tire Operations LLC (Bridgestone Corporation)
• Inventors: Stalnaker, David O., Xie, Ke Jun, Knuth, Erik F., Turner, John L., Neugebauer, Paul M.
• Granted Patent: US 9,428,018 B2
• Time in Patent Office: Days
• US Class Current: 702/34
• CPC Class Codes: B60C 99/006 Computer aided tyre design or simulation
• CPC Class Codes: G01M 17/02 Tyres
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/23 using finite element methods [FEM] or finite difference methods [FDM]

Abstract

A method for reducing vehicle bias when testing a tire for use with a market segment of vehicles by creating a vehicle model that is scalable by vehicle weight. A market segment of vehicles is defined, at least one of a vehicle model parameter is defined, data is collected for the at least one vehicle model parameter from at least one vehicle in the market segment, the at least one vehicle model parameter is characterized through regression analysis as a function of total weight of a scalable vehicle model, the scalable vehicle model parameter is applied to a multibody vehicle dynamics simulation, at least one maneuver is applied to the scalable vehicle model, and the tire load histories generated by the multibody vehicle dynamics simulation are provided to a tire test machine to obtain tire wear data representative of the vehicles in the market segment.

Claims

1. A method for creating a scalable vehicle model for tire design and testing, comprising:

defining a vehicle segment representing a plurality of individual vehicles having various weights and at least one tire;

defining at least one vehicle model parameter of at least one vehicle in the vehicle segment, including at least one of;

the vehicle'"'"'s wheel base, the vehicle'"'"'s wheel track, the vehicle'"'"'s center of gravity, the vehicle'"'"'s suspension compliance, the vehicle'"'"'s suspension kinematics, the vehicle'"'"'s steering kinematics, the vehicle'"'"'s weight distribution, the vehicle'"'"'s ballasting, the vehicle'"'"'s front-to-rear brake proportioning, the vehicle'"'"'s auxiliary roll stiffness, the vehicle'"'"'s unsprung mass, the tire'"'"'s stiffness, the tire'"'"'s longitudinal force, the tire'"'"'s lateral force, the tire'"'"'s aligning torque, and the tire'"'"'s camber thrust; and

determining a parameter regression function for at least one vehicle model parameter,

wherein the parameter regression function provides the average value of the at least one vehicle model parameter for the range of weights of the vehicles comprising the defined vehicle segment,

wherein the parameter regression function is characterized as a function of the total weight of a scalable vehicle model by the equation P(W)=C₀(W)+C₁(W)A+C₂(W)A²+C₃(W)A³,

wherein W is the total weight of the scalable vehicle model,

wherein P(W) is the at least one vehicle model parameter,

wherein C_n(W) is a regression coefficient as a function of W, and

wherein A is an independent variable, including at least one of;

a vehicle'"'"'s jounce and a vehicle'"'"'s steering angle.

2. The method of claim 1, wherein C_n(W) is equal to a_n0+a_n1W+a_n2W²+a_n3W³.

3. The method of claim 1, further comprising creating the scalable vehicle model scalable as a function of W.

4. The method of claim 1, further comprising creating at least one formula comprising

a regression curve fit of a tire load as a function of W.

5. The method of claim 1, further comprising:

developing a coefficient model for at least one tire property,

wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an aligning torque coefficient, and

wherein the coefficient model is a function of W and a vertical load exerted on a tire;

determining a total weight dependency of the coefficient model through a coefficient regression function,

wherein the coefficient regression function is a function of W; and

developing a scalable tire model of at least one of;

a tire lateral force, a tire longitudinal force, and a tire aligning moment,

wherein the scalable tire model is a function of a slip angle and the vertical force exerted on the tire.

6. The method of claim 5, wherein the coefficient regression function is a bi-linear function, and

wherein the scalable tire model is modeled as a cubic spline function.

7. A method for predicting at least one of a force and an inclination angle exerted on a tire by a vehicle in a particular vehicle segment, comprising:

defining a vehicle segment representing a plurality of individual vehicles having various weights and at least one tire;

defining at least one vehicle model parameter of at least one vehicle in the vehicle segment, including at least one of;

the vehicle'"'"'s wheel base, the vehicle'"'"'s wheel track, the vehicle'"'"'s center of gravity, the vehicle'"'"'s suspension compliance, the vehicle'"'"'s suspension kinematics, the vehicle'"'"'s steering kinematics, the vehicle'"'"'s weight distribution, the vehicle'"'"'s ballasting, the vehicle'"'"'s front-to-rear brake proportioning, the vehicle'"'"'s auxiliary roll stiffness, the vehicle'"'"'s unsprung mass, the tire'"'"'s stiffness, the tire'"'"'s longitudinal force, the tire'"'"'s lateral force, the tire'"'"'s aligning torque, and the tire'"'"'s camber thrust;

determining a parameter regression function for at least one vehicle model parameter,

wherein the parameter regression function provides the average value of the at least one vehicle model parameter for the range of weights of the vehicles comprising the defined vehicle segment,

wherein the parameter regression function is characterized as a function of the total weight of a scalable vehicle model by the equation P(W)=C₀(W)+C₁(W)A+C₂(W)A²+C₃(W)A³,

wherein W is the total weight of the scalable vehicle model,

wherein P(W) is the at least one vehicle model parameter,

wherein C_n(W) is a regression coefficient as a function of W, and

wherein A is an independent variable, including at least one of;

a vehicle'"'"'s jounce and a vehicle'"'"'s steering angle; and

predicting at least one of a tire force and an inclination angle exerted on a tire by the scalable vehicle model through a multibody vehicle dynamics simulation.

8. The method of claim 7, wherein C_n(W) is equal to a_n0+a_n1W+a_n2W²+a_n3W³.

9. The method of claim 7, further comprising applying the scalable vehicle model to at least one maneuver in the multibody vehicle dynamics simulation to determine at least one of:

a longitudinal acceleration and a deceleration,

a lateral acceleration,

a steering angle,

an inclination angle, and

a tire loading history,

for each tire of the scalable vehicle model.

10. The method of claim 7, further comprising creating the scalable vehicle model scalable as a function of W.

11. The method of claim 7, further comprising creating at least one formula comprising a regression curve fit of a tire load as a function of W.

12. The method of claim 7, further comprising:

developing a coefficient model for at least one tire property,

wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an aligning torque coefficient, and

wherein the coefficient model is a function of W and a vertical load exerted on a tire;

determining a total weight dependency of the coefficient model through a coefficient regression function,

wherein the coefficient regression function is a function of W; and

developing a scalable tire model of at least one of;

a tire lateral force, a tire longitudinal force, and a tire aligning moment,

wherein the scalable tire model is a function of a slip angle and the vertical force exerted on the tire.

13. The method of claim 12, wherein the coefficient regression function is a bi-linear function, and

wherein the scalable tire model is modeled as a cubic spline function.

14. A method for determining the wear rate of a tire for use with a particular vehicle segment, comprising:

defining a vehicle segment representing a plurality of individual vehicles having various weights and at least one tire;

defining at least one vehicle model parameter of at least one vehicle in the vehicle segment, including at least one of;

the vehicle'"'"'s wheel base, the vehicle'"'"'s wheel track, the vehicle'"'"'s center of gravity, the vehicle'"'"'s suspension compliance, the vehicle'"'"'s suspension kinematics, the vehicle'"'"'s steering kinematics, the vehicle'"'"'s weight distribution, the vehicle'"'"'s ballasting, the vehicle'"'"'s front-to-rear brake proportioning, the vehicle'"'"'s auxiliary roll stiffness, the vehicle'"'"'s unsprung mass, the tire'"'"'s stiffness, the tire'"'"'s longitudinal force, the tire'"'"'s lateral force, the tire'"'"'s aligning torque, and the tire'"'"'s camber thrust;

determining a parameter regression function for at least one vehicle model parameter,

wherein the parameter regression function provides the average value of the at least one vehicle model parameter for the range of weights of the vehicles comprising the defined vehicle segment,

wherein the parameter regression function is characterized as a function of the total weight of a scalable vehicle model by the equation P(W)=C₀(W)+C₁(W)A+C₂(W)A²+C₃(W)A³,

wherein W is the total weight of the scalable vehicle model,

wherein P(W) is the at least one vehicle model parameter,

wherein C_n(W) is a regression coefficient as a function of W, and

wherein A is an independent variable, including at least one of;

a vehicle'"'"'s jounce and a vehicle'"'"'s steering angle;

predicting at least one of a tire force and an inclination angle exerted on a tire by the scalable vehicle model through a multibody vehicle dynamics simulation; and

determining the wear rate of a tire by mounting the tire on a machine,

wherein the machine is configured to rotate the tire at a desired speed and to apply the tire against a simulated road surface with at least one of;

the predicted tire force and the predicted inclination angle,

wherein the machine is placed into operation, and

wherein the wear of the tire is measured over time.

15. The method of claim 14, wherein C_n(W) is equal to a_n0+a_n1W+a_n2W²+a_n3W³.

16. The method of claim 14, further comprising applying the scalable vehicle model to at least one maneuver in the multibody vehicle dynamics simulation to determine at least one of:

a longitudinal acceleration and a deceleration,

a lateral acceleration,

a steering angle,

an inclination angle, and

a tire loading history,

for each tire of the scalable vehicle model.

17. The method of claim 14, further comprising creating the scalable vehicle model scalable as a function of W.

18. The method of claim 14, further comprising creating at least one formula comprising

a regression curve fit of a tire load as a function of W.

19. The method of claim 14, further comprising:

developing a coefficient model for at least one tire property,

wherein the coefficient model characterizes one of a cornering coefficient, a slip stiffness coefficient, and an aligning torque coefficient, and

wherein the coefficient model is a function of W and a vertical load exerted on a tire;

determining a total weight dependency of the coefficient model through a coefficient regression function,

wherein the coefficient regression function is a function of W; and

developing a scalable tire model of at least one of;

a tire lateral force, a tire longitudinal force, and a tire aligning moment,

wherein the scalable tire model is a function of a slip angle and the vertical force exerted on the tire.

20. The method of claim 19, wherein the coefficient regression function is a bi-linear function, and

wherein the scalable tire model is modeled as a cubic spline function.