The DLR Institute of Technical Thermodynamics, with research facilities in Stuttgart, Ulm, Cologne-Porz, Oldenburg, and Hamburg, employs over 270 people conducting research in the field of efficient and resource-saving energy storage and energy conversion technologies.
What to expect
In the department of Computational Electrochemistry, mathematical models of the chemical and physical processes in batteries are developed for an in-depth analysis by means of numerical simulations. The aim is to gain detailed insights into the complex multiscale processes that allow optimization of the battery design with respect to performance and aging.
This master thesis focuses on lithium-sulfur cells with a high energy density, which are developed for aerospace applications. One challenge of this cell type is that simple estimates of the current state of charge based on the cell voltage fail. This is due to the highly non-linear characteristics of the cell type and requires advanced, data-driven methods to ensure safe operation.
Motivated by this, the suitability of Kalman filters should be evaluated in terms of sequential data assimilation. In particular, the embedding of efficient surrogate models of lithium-sulfur cells seems to be crucial for this and also opens up possibilities for machine learning approaches. The work offers the prospect of making an important contribution to the safe establishment of lithium-sulfur cells in commercial systems.
Your tasks
- literature study on sequential data assimilation using Kalman filters
- selection, implementation and benchmarking of an established approach
- variation of embedded surrogate models (also ML based)
- identification of critical aspects for the design of replacement models
- documentation of the work
Your profile
- data-driven methods & ML
- control theory
- Python Programming
We look forward to getting to know you!
If you have any questions about this position (Vacancy-ID 2513) please contact:
Timo Danner
Tel.: +49 711 6862 8218
