Current vacancies
Laboratory for Smart Energy Systems

This thesis aims to improve an existing data-driven model for predicting the remaining useful life of battery systems using statistical methods and machine learning. Ideal for students with a strong interest in data science and energy systems.

We are looking for a student assistant to develop a machine learning model predicting battery aging based solely on real-world operational data provided by an industry partner.

Battery aging varies with cell chemistry. This thesis focuses on transferring existing aging models to different battery types using transfer learning and hybrid modeling approaches.

This combined thesis and student job project extends an existing ML-based battery aging model with new input features. Ideal for students experienced in ML and interested in applied energy system modeling.