Degree: Bachelor
Contact Person:Thomas Eisenbarth

The popularity of electric vehicles has seen a large increase in recent years, and the EU plans to ban the sale of combustion engine cars by 2035. In order to keep electric vehicles moving, drivers have the option to use public charging stations to recharge their car batteries. In order to provide services such as user authentication, charging load management and billing, the charging stations share detailed information on charging sessions with multiple parties in the ecosystem. In addition, there are other public data sources collected in movement studies or other evaluations.

The goal of this thesis topic is to explore the impact that sharing EV charging session data and other data has on the privacy of the user. For example, can charging session history or location information be used to infer sensitive information about the driver, such as their social status, income or medical state? In addition, could personal data be used as a form of geo-tracking by predicting a driver’s location?

The student is expected to train and evaluate appropriate machine learning models to answer the above questions, using publicly available charging session or other datasets. Required skills are a good background in machine learning and data science, with hands-on experience with Python/Jupyter notebooks or similar tools.

The thesis is open to be carried out in industry.