WO2016069149A4 - Scalable vehicle models for indoor tire testing - Google Patents

Scalable vehicle models for indoor tire testing Download PDF

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Publication number
WO2016069149A4
WO2016069149A4 PCT/US2015/052113 US2015052113W WO2016069149A4 WO 2016069149 A4 WO2016069149 A4 WO 2016069149A4 US 2015052113 W US2015052113 W US 2015052113W WO 2016069149 A4 WO2016069149 A4 WO 2016069149A4
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WIPO (PCT)
Prior art keywords
vehicle
tire
model
function
coefficient
Prior art date
Application number
PCT/US2015/052113
Other languages
French (fr)
Other versions
WO2016069149A1 (en
Inventor
David O. Stalnaker
Ke Jun XIE
Erik F. Knuth
John L. Turner
Paul M. Neugebauer
Original Assignee
Bridgestone Americas Tire Operations, Llc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US14/529,536 external-priority patent/US9428018B2/en
Application filed by Bridgestone Americas Tire Operations, Llc filed Critical Bridgestone Americas Tire Operations, Llc
Priority to JP2017523799A priority Critical patent/JP2018501466A/en
Priority to KR1020177010588A priority patent/KR20170058412A/en
Priority to EP15854380.1A priority patent/EP3213239A4/en
Publication of WO2016069149A1 publication Critical patent/WO2016069149A1/en
Publication of WO2016069149A4 publication Critical patent/WO2016069149A4/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • G01M17/02—Tyres

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Automation & Control Theory (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Tires In General (AREA)

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

AMENDED CLAIMS received by the International Bureau on 20 May 2016 (20.05.2016)
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 an average value of the at least one vehicle model parameter for a range of weights of the vehicles comprising the defined vehicle segment, wherein the parameter regression function is characterized as a function of a total weight of a scalable vehicle model by the equation P(W) = Co(W) + d(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 Cn(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 Cn(W) is equal to ano + anlW + an2W + an3W3.
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; 31
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 load exerted on the tire.
6. The method of claim 5, wherein the coefficient regression function is a bilinear 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; 32
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 comprising the defined vehicle segment,
wherein the parameter regression function is characterized as a function of a total weight of a scalable vehicle model by the equation P(W) = Co(W) + d(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 Cn(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 Cn(W) is equal to ano + anlW + an2W + an3W3.
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, 33
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 load exerted on the tire. 34
13. The method of claim 12, wherein the coefficient regression function is a bilinear function, and wherein the scalable tire model is modeled as a cubic spline function.
14. A method for determining a 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 an average value of the at least one vehicle model parameter for a range of weights of the vehicles comprising the defined vehicle segment,
wherein the parameter regression function is characterized as a function of a total weight of a scalable vehicle model by the equation P(W) = Co(W) + d(W)A + C2(W)A2 + C3(W)A3, 35
wherein W is the total weight of the scalable vehicle model, wherein P(W) is the at least one vehicle model parameter, wherein Cn(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 Cn(W) is equal to ano + aniW + a^W + an3W3.
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, 36
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 load exerted on the tire.
20. The method of claim 19, wherein the coefficient regression function is a bilinear function, and wherein the scalable tire model is modeled as a cubic spline function.
PCT/US2015/052113 2014-10-31 2015-09-25 Scalable vehicle models for indoor tire testing WO2016069149A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2017523799A JP2018501466A (en) 2014-10-31 2015-09-25 Scaleable vehicle model for indoor tire testing
KR1020177010588A KR20170058412A (en) 2014-10-31 2015-09-25 Scalable vehicle models for indoor tire testing
EP15854380.1A EP3213239A4 (en) 2014-10-31 2015-09-25 Scalable vehicle models for indoor tire testing

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US14/529,536 US9428018B2 (en) 2012-12-28 2014-10-31 Scalable vehicle models for indoor tire testing
US14/529,536 2014-10-31

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WO2016069149A1 WO2016069149A1 (en) 2016-05-06
WO2016069149A4 true WO2016069149A4 (en) 2016-07-14

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JP (2) JP2018501466A (en)
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6790875B2 (en) * 2017-01-27 2020-11-25 住友ゴム工業株式会社 Tire wear performance prediction method
KR102192859B1 (en) * 2018-12-20 2020-12-18 넥센타이어 주식회사 Automated system for dynamic analysis of vehicle and method of dynamic analysis of vehicle using the same
JP7265912B2 (en) 2019-03-29 2023-04-27 Ｔｏｙｏ Ｔｉｒｅ株式会社 Calculation model generation system, wear amount estimation system, and calculation model generation method
CN116070356B (en) * 2023-04-06 2023-08-08 山东玲珑轮胎股份有限公司 Tire model design method and system
CN117972910B (en) * 2024-03-29 2024-06-21 湖南大学 Steering system collaborative design method of multi-axis intelligent chassis

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US6532811B2 (en) * 2001-01-26 2003-03-18 Bridgestone/Firestone North American Tire, Llc Method of wear testing a tire
US7228732B2 (en) * 2001-01-26 2007-06-12 Bridgestone Firestone North American Tire, Llc Tire wear analysis method
FR2841827A1 (en) * 2002-07-04 2004-01-09 Michelin Soc Tech ESTIMATING THE WEAR OF A TIRE
US7469578B2 (en) * 2005-09-27 2008-12-30 The Yokohama Rubber Co., Ltd. Method and apparatus for evaluating a cornering stability of a wheel
US7778809B2 (en) * 2006-08-22 2010-08-17 The Yokohama Rubber Co., Ltd. Tire characteristic calculation method, tire dynamic element parameter value derivation method, vehicle traveling simulation method, and tire designing method and vehicle designing method in which consideration is given to tire friction ellipse
JP4229959B2 (en) * 2006-08-22 2009-02-25 横浜ゴム株式会社 Tire design method considering tire friction ellipse
JP4260175B2 (en) * 2006-08-22 2009-04-30 横浜ゴム株式会社 Vehicle design method considering tire friction ellipse
JP4201821B2 (en) * 2007-03-28 2008-12-24 横浜ゴム株式会社 Tire model determination method, tire transient response data calculation method, tire evaluation method, and tire design method
JP5337090B2 (en) * 2010-03-25 2013-11-06 株式会社デンソーアイティーラボラトリ Vehicle characteristic information estimation device and warning device using the same
US8555698B2 (en) * 2011-01-26 2013-10-15 Bridgestone Americas Tire Operations, Llc Engineered surfaces for laboratory tread wear testing of tires
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US20140188406A1 (en) * 2012-12-28 2014-07-03 Bridgestone Americas Tire Operations, Llc Scalable vehicle models for indoor tire testing

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Publication number Publication date
EP3213239A1 (en) 2017-09-06
EP3213239A4 (en) 2018-06-06
JP2018501466A (en) 2018-01-18
WO2016069149A1 (en) 2016-05-06
JP2020020796A (en) 2020-02-06
KR20170058412A (en) 2017-05-26

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