Enhanced UA (University of Arizona) tire model for computationally efficient and stable multibody simulations

Abstract

Multibody simulations are widely used to study the dynamic behaviour of road vehicles, requiring the representation of the tire-road contact forces. The UA tire model, developed by Gim and Nikravesh, is a physical model that requires the selection of few parameters, which are easily obtained without the need of extensive testing. However, the analytical formulas of the UA tire model imply force discontinuities that are challenging in multibody simulations, affecting the numeric time integration of the accelerations and velocities, and consequently, the simulation stability and efficiency. This work proposes an enhanced UA tire model by smoothing such force discontinuities. For the purpose, the kinematic conditions that imply discontinuities are identified and a cubic smoothing function is applied. Simulation results highlight the discontinuities in the lateral force and self-aligning moment when using the original UA tire model. The use of the enhanced model within multibody simulations is significantly more efficient, without affecting the quality of the results or modifying the range of applications of the original tire model. The performance of the enhanced UA tire model is demonstrated with the tire model performance test (TMPT), which is used for the international benchmark test of tire models.

KEYWORDS:

• Tire models
• multibody simulations
• road vehicle dynamics
• discontinuities
• tire model performance test

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

We want to thank Professor Parviz Nikravesh for his valuable inputs and feedback. This work was supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) through the PhD grant https://doi.org/10.54499/UI/BD/151204/2021. The authors also acknowledge Fundação para a Ciência e a Tecnologia (FCT) for its financial support via the projects LAETA Base Funding (https://doi.org/10.54499/UIDB/50022/2020) and LAETA Programmatic Funding (https://doi.org/10.54499/UIDP/50022/2020).