Thesis - Edge-Deployable Virtual Sensing for Vehicle Dynamics

We are Geely Technology Europe. A unified European R&D centre within Geely Auto Group, where world-class engineers, developers and

innovators push the boundaries of intelligent mobility. Our mission is to shape the next generation of vehicle architectures,

digital technologies and intelligent systems for global markets. We integrate European customer and regulatory requirements early

in the development process and support multiple brands within the Geely portfolio, including Zeekr, Lynk & Co and Geely. With

nearly two decades of engineering experience in Europe, we continue to build smart, sustainable and user‑centric mobility

solutions.

Accurate knowledge of vehicle dynamic states is essential for advanced motion control, driver assistance, energy-efficient

driving, and automated driving functions. However, many relevant states are difficult, costly, or impractical to measure directly

in production vehicles. Virtual sensing offers a way to estimate such quantities using available vehicle signals, models, and

data-driven methods. Traditional physics-based approaches provide interpretability and engineering insight, while machine learning

can capture complex patterns from data.

This thesis builds upon a previous thesis conducted within the team, Physics-Informed Neural Networks for Vehicle Lateral Dynamics

Modeling. Students are encouraged to review the findings and methodology presented in that work as a starting point for

understanding the problem domain and existing approaches. The present thesis aims to advance the state of the art by investigating

scientific machine learning methods, including physics-informed and physics-augmented machine learning approaches, to improve

robustness, generalization, interpretability, and suitability for deployment in real-world automotive systems.

The scope of the thesis is to investigate machine learning-based virtual sensing methods for estimating selected vehicle dynamic

states from available sensors and vehicle network signals. The work should remain open with respect to the final model

architecture and focus on how domain knowledge, physical principles, and data-driven learning can be combined in a scientifically

grounded way. The study may cover lateral and/or longitudinal vehicle dynamics, depending on data availability and project focus,

and should consider robustness across operating conditions, generalization to unseen scenarios, and feasibility for real-time

automotive applications.

• Develop and evaluate machine learning-based methods for estimating selected vehicle dynamic states from available vehicle

signals.

• Investigate physics-informed or physics-augmented machine learning approaches as part of the broader machine learning

discipline, combining physical insight with data-driven modelling.

• An evaluation of physics-informed or physics-augmented machine learning methods, positioned within scientific machine learning,

for incorporating physical knowledge into data-driven models.

• A validation and benchmarking study comparing selected methods against relevant baseline models using representative vehicle

data.

We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of

global culture, and a belief in a smarter more sustainable future. At Geely Tech Eu we fast-track innovation and transform ideas

into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a

major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills

and creativity to push the boundaries of what´s possible.

What happens when you apply

If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover

letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations.

Supervisor: Karthik Prasad, Expert Motion Systems, karthik.prasad@zeekrtech.eu, please contact for more information about the

project

Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or

other channels.