Thesis: Data-Driven Analysis and Prediction of Thermal Runaway Behavior of Lithium-Ion Cells

Conducting a systematic literature review on data-driven and statistical approaches for the description and prediction of thermal runaway behavior of lithium-ion cells, analysis, structuring and preparation of existing experimental measurement data from thermal runaway tests, integration of existing simulation data to support data evaluation, feature engineering and model evaluation, identification and definition of characteristic target variables, e.g. onset times, maximum temperatures, maximum pressures, venting-relevant parameters or other suitable evaluation parameters, identification of relevant cell and test parameters and investigation of their relevance for thermal runaway behavior, selection, conception, implementation and training of a suitable data-driven modeling approach for the prediction of characteristic parameters, validation of the developed model using experimental and simulation data as well as evaluation of prediction quality, identification of trends, correlations and dominant influencing variables within the considered parameter space, critical discussion of the transferability, significance and limits of the developed approach and derivation of further development potential.

Degree programs: Computer Science, Data Science, Mechanical Engineering, Vehicle Engineering, Electrical Engineering or comparable degree program. Study focuses: Machine Learning / Artificial Intelligence, Data Analysis and Data Preparation, Statistics, Modeling and Simulation. Technical knowledge: Basic knowledge in the field of Machine Learning, Statistics or data-driven modeling, interest in the analysis of technical measurement data and the identification of physical relationships, basic understanding of experimental data and technical systems, ideally initial knowledge in the field of lithium-ion batteries or battery safety. IT skills: Proficient use of MS Office, good Python skills desirable, ideally initial experience with data analysis and ML libraries, e.g. scikit-learn, PyTorch or TensorFlow. Language skills: German, English. Other / Soft Skills: High self-initiative, pronounced analytical skills, structured way of working, ability to work in a team.