Thesis Worker - Machine Learning-Based Tyre Model Optimization

Thesis Worker at Volvo Cars

Welcome to explore the world of Volvo Cars by writing your thesis with us! As a thesis worker in our organization you are supported by a supervisor who follows you during your project. All thesis projects are arranged in business critical areas and therefore you will be able to contribute to our company purpose – providing freedom to move in a safe, sustainable and personal way – from day one!

Background

Handling and steering performances characterizes the driving DNA of a vehicle. It comprises of a complex vehicle performance matrix that describes the agility, predictability, controllability, steerability and stability. Tyres play a critical role in handling and steering as they are the sole interface between vehicle and road. They have direct impact on vehicle performance characteristics, ride quality and handling behaviour.

In concept phase of vehicle design, computer aided engineering (CAE) is widely used to predict and adjust vehicle performance. Tyre models are one critical input to CAE. The models used in this phase are empirical models in the form of Magic Formula calibrated from force-moment data measured from the flat-trac procedure. However, measurement methods and calibration methods can bring great data discrepancy in the model even for the same physical tyre. This makes vehicle tuning difficult.

This Master's thesis project aims to leverage Machine Learning techniques to optimize parameter identification within the Magic Formula, minimizing data discrepancies across tyre models. The developed algorithm will create correlation factors among different models of the same physical tyre, enabling improved accuracy and consistency. The thesis will involve comparing and ranking tyre models, with and without these correlation factors, through both offline simulations and subjective evaluations in a driving simulator.

If you are interested in applying machine learning to tackle real-world automotive challenges and enhancing vehicle performance, this thesis presents a unique opportunity to contribute to cutting-edge research in vehicle dynamics.

Scope

The thesis will include:

• Literature survey of tyre MF application in vehicle dynamic simulation of handing and steering
• Compare method on optimization and machine learning tools for the purpose of the study
• Generate correlation factors for the given sets of tyre model data
• Compute and rank tyre models in offline simulation and subjective evaluation in Simulator for selected driving manoeuvres
• Write method recommendation, script for continuous implementation
• Write report

Prerequisites

The successful candidates are expected to be master students from mathematics, computer science, physics, mechatronics and mechanical engineering. You should be self-motivated and explorative. Good at programming and have some good knowledge in optimization and machine learning. Knowledge in either Python or Matlab is required.

Number of students: 2, please apply with CV and cover letter

Starting date: January 2025. The duration of the thesis work is 20 weeks

For more information please contact: Industrial supervisor: Xin Li, xin.li.11@volvocars.com, Per Blomberg, per.blomberg@volvocars.com

Who are we?

Everything we do starts with people. Our purpose is to provide freedom to move, in a personal, sustainable and safe way. We are committed to simplifying our customers’ lives by offering better technology solutions that improve their impact on the world and bringing the most advanced mobility innovations to protect them, their loved ones and the people around them.

Volvo Cars’ continued success is the result of a collaborative, diverse, and inclusive working environment. The people of Volvo Cars are committed to making a difference in our world. Today, we are one of the most well-known and respected car brands, with over 40,000 employees across the globe. We believe in bringing out the best in each other and harnessing the true power of people. At Volvo Cars your career is designed around your talents and aspirations so you can reach your full potential. Join us on a journey of a lifetime as we create safety, autonomous driving and electrification technologies of tomorrow