MATLAB and Simulink Racing Lounge: Tire Modeling with Monash Motorsport

Introduction

• Collaboration with Monash Motorsport
• Focus on tire modeling
• Importance of extracting key data to improve car performance

Guest Introduction

• Marc Russouw
• Role: Started in aerodynamics, moved to suspension, now a mentor and vehicle dynamics role in the team
• Experience: 6 years with Monash Motorsport

Session Content Overview

• Available tire modeling techniques
• Benefits and challenges of tire modeling
• Choosing the right tire model
• Discussion on a non-dimensional tire model using MATLAB

Motivation Behind Tire Modeling

• Predicting tire response to parameter changes (camber angle, pressure, normal load)
• Basis for vehicle and kinematics design models
• Evaluating impact of different setups
• Simulation and tire modeling are resource-efficient compared to real-world tests

Types of Tire Models

• Physical representations (e.g., finite element models)
• Theoretical models (e.g., models from first principles)
• Empirical fits (most common in Formula SAE, e.g., Pacejka model)

Practical Use of Tire Models in Formula SAE

• Semi-empirical models are most applicable and simplest to implement
• Constraints in resources and time favor simpler models
• Continuity and handover of knowledge across team years

Tire Testing Consortium (TTC)

• Calspan: Company that performs tire testing
• Volunteer engineers collect data for Formula SAE teams
• Test variables: 5 inclination angles, 4 normal loads, 3-4 different pressures

Accessing TTC Data

• Costs $250-$500 (one-time fee)
• Access to historical and future tire testing data
• Valuable resource due to the high cost of commercial data

Testing Procedure

• Data acquisition is from various conditions (e.g., different loads, slip angles)
• Data needs to be processed and simplified for use in models
• Magic Formula for fitting curves to test data

Example of Practical MATLAB Use

• Script for processing raw data
• Non-dimensionalization of data
• Fitting curves using MATLAB’s nonlinear least squares method
• Representative graph output to depict tire performance

Benefits and Challenges of Tire Modeling

• Less resource-intensive than real-world tests
• Importance of simplicity for handover and understanding
• Computational efficiency for iterative processes
• Importance of context: knowing what models can and cannot do

Specific Considerations

• Friction coefficients may be higher due to test surface conditions
• Steady-state models don't account for transient effects
• Impact of variables like vertical load, camber, and pressure

Validation and Real-World Application

• Comparison of model predictions with real-world data (e.g., slip angles)
• Dynamic testing considerations (more complex, first principles needed)

Strengths and Weaknesses of Current Approaches

• Strengths: Easy setup, accurate measurement, resource-efficient
• Weaknesses: High friction coefficients, steady-state assumptions, deformation effects

Final Remarks

• Importance of clear goals in model choice
• Use of MATLAB for its flexibility, ease of use, and extensive functionality
• Acknowledgment of Calspan and their contribution to the data

Key Takeaways

1. Goals determine model choice
2. How to use data to predict tire behavior
3. Advantages of using MATLAB (modularity, ease of use, integration)
4. Continuous improvement and knowledge transfer within the team

Additional Resources

• MATLAB and Simulink Racing Lounge webpage
• Formula Student webpage
• Contact: [email protected]
• Encouragement to use MathWorks logos on cars and reports if using their software

Conclusion

• Thanks to Marc Russouw for sharing insights
• End of the session