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Scalable Vehicle Models for Tire Testing

Journal Article
ISSN: 1946-3995, e-ISSN: 1946-4002
Published April 14, 2015 by SAE International in United States
Scalable Vehicle Models for Tire Testing
Citation: Stalnaker, D., Xie, K., and Wei, T., "Scalable Vehicle Models for Tire Testing," SAE Int. J. Passeng. Cars - Mech. Syst. 8(2):659-668, 2015,
Language: English


Tire manufacturers need to perform various types of testing to determine tire performance under representative vehicle load conditions. However, test results are influenced by a number of external variables other than tire construction. Vehicle load distribution and suspension properties are some of those external variables which can have a significant effect on tire wear rate and durability. Therefore, in order to measure real world tire performance in a controlled and repeatable manner, a representative vehicle and associated tire load conditions are needed.
Laboratory or indoor tire testing offers many advantages over vehicle fleet testing. It provides a well-defined test environment and repeatable results without influence from external factors. Indoor testing has been largely developed around the process of simulating tire wear performance on a specific reference vehicle, including its specific weight distribution, suspension characteristics, and alignment. However this approach is not desirable in some cases, such as Department of Transportation's Uniform Tire Quality Grading (UTQG) wear testing, aftermarket tire development for a vehicle category, and laboratory durability testing. These tests should mimic representative tire loading conditions for a particular tire size and category based on an ‘average’ or generic vehicle. This vehicle must, however, reflect the general loading and suspension characteristics of the vehicle segments of interest. This approach applies not only to laboratory testing but also to defining generalized but representative loading conditions for tire modeling.
This study reviews a methodology for developing a gradually and continuously scalable generic vehicle model, which yields a consistent tire load history change across tire sizes. This new concept yields laboratory test results free of specific vehicle bias, thus providing a more representative indication of tire performance across tire sizes within a product and vehicle category.