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200837 https://hdl.handle.net/2286/R.2.N.200837 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2025-05 35 pages Wilson, Drake Chen, Yan Ren, Yi Barrett, The Honors College Mechanical and Aerospace Engineering Program Tire behavior is a critical factor in motorsports, but especially the performance of Formula Student vehicles, where tight autocross circuits demand high acceleration, strong cornering grip, and predictable handling. However, accurately modeling tire forces remains a major challenge due to their nonlinear nature with respect to load. This thesis presents a structured methodology for developing and validating lateral tire models using empirical data provided by the Formula SAE Tire Test Consortium (TTC). Several modeling approaches were evaluated, with the Magic Formula selected for its balance of accuracy, flexibility, and implementation ease. A full data processing script was developed to isolate steady-state conditions and group test cases by operation parameters. Nonlinear regression was used to fit Magic Formula parameters, and a vertical load scaling factor was introduced to reduce computational load. A weighted comparison framework was developed to evaluate six different tire models based on several performance and driveability metrics, leading to a data-driven tire selection process for Arizona State University’s Formula SAE team. Finally, an experimental surface correction factor must be measured to correct for idealisms in testing data. The resulting process offers an accessible and effective approach to tire modeling and selection in resource-constrained motorsport environments. FSAE Motorsports Tires A DYNAMIC ANALYSIS OF EMPIRICAL TIRE MODELS