Scalable Vehicle Models for Indoor Tire Testing

摘要

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.

主权项

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 Cn(W) is a regression coefficient as a function of W, andwherein A is an independent variable, including at least one of: a vehicle's jounce and a vehicle's steering angle.