MegaRide Webinar Series

Begin your journey in the field of vehicle dynamics! This webinar introduces one of the most complex systems to model: tires! The course starts with an introduction to the basics of tire forces and moments. We will also dive into how forces and moments are generated, how quantities such as slip angle, split ratio, camber, forces and moments are measured, how tire data is visualized and which metrics are used to quantify the forces and moments of the tire. The webinar concludes with an exercise to connect how the tire metrics affect the vehicle behaviour.

If you have ever done any vehicle simulation you know that the most sensitive sub-system is the tire. However, tires remain one of the most difficult systems to model, relying greatly on laboratory and on-track testing. Physically testing all of the possible configurations becomes impossible both in terms of time and budget. Thus, it becomes important to know within which conditions you should test your tire and how to correlate laboratory with on-track testing. Learn the requirements, advantages and disadvantages between laboratory and on-track testing.

Since the MF tire model is highly data-driven, proper parameterization is essential to accurately reproduce tire behavior across the full operating range. Tire models are built using force and moment data from indoor or outdoor tests, which must be carefully processed before fitting. This seminar covers tire model fitting, how to improve models by tuning constraints, population size, iterations, and stop criteria, and how to identify good versus poor models. Common fitting pitfalls that degrade vehicle simulation accuracy and strategies to overcome them will also be discussed.

One of the most common vehicle tests is on-track testing to acquire tire data and achieve model correlation. The vehicle is equipped with sensors to measure slip angle, slip ratio, camber, speed, and other variables, and is driven using predefined steering and throttle inputs. The collected data is used to generate tire models and correlate them with laboratory results. Data quality strongly depends on test procedures, analysis methods, sensors, and driving maneuvers. Vehicle models help estimate states such as loads and forces, improving data quality and test success.