Master Thesis Robust Identification of Compositional Electrical Drive Models - Identifiability, Sensitivity & Excitation Analysis

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Responsibilities

During your thesis, you delve into physics-based models of electric drives and our Python component library while simultaneously reviewing current literature on robust system identification and the design of experiments.; Furthermore, you develop innovative robustness diagnostics for our existing identification pipeline - considering parameter identifiability, sensitivity, and convergence behavior - and implement as well as quantify appropriate countermeasures.; In Addition, you analyze the excitation content of given datasets, evaluate relevant criteria, and link these insights to the achievable identification quality.; You comprehensively evaluate the approach you have developed using a practical benchmark use case.; Moreover, you document as well as present your research findings clearly and understandably.; Lastly, you extend the approach to address partially observable effects resulting from states that cannot be measured directly, such as temperatures.

Requirements

Education: Master studies in the field of in Cybernetics, Engineering, Mathematics, Computer Science, or comparable; Experience and Knowledge: basic knowledge of dynamic systems and differential equations, proficiency in Python programming, understanding of the physics of electrical machines, optional: experience with MATLAB/Simulink for interacting with legacy simulation models, optional: knowledge of machine learning and automatic differentiation frameworks (JAX or PyTorch); Personality and Working Practice: you work independently and systematically on complex issues and possess strong analytical skills, enabling you to accurately grasp and solve problems; Work Routine: office attendance required; Languages: very good in English