Master Thesis Identification of Models for Electric Machines Using Differentiable ODE Solvers

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

The identification of accurate simulation models of electric machines is a crucial step for the design of high-performing controllers, fault diagnosis and many other tasks. The goal of your Master thesis is to investigate and develop effective optimization and training methods for the identification of electric machines. The focus lies on the design of physics-enhanced data-based models and the implementation of training algorithms. Of further interest is the evaluation of various optimization techniques and their impact on model accuracy and computational efficiency. 

• During your Master Thesis your task includes the familiarization with the physical models of electric machines.
• You will be responsible for conducting literature research on existing ML-based approaches for the identification of electric machines.
• In addition, you will set up a differentiable parametric model of the electric machine.
• Furthermore, you will implement and compare different training and optimization methods, such as quadrature-based approaches, multi-step ODE solvers utilizing various integration techniques and prediction horizons, neural ODEs and methods involving the differentiation of measured currents.
• Last but not least, you will evaluate the training methods in terms of convergence speed, accuracy, robustness and computational cost.

Qualifications

• Education: Master studies in the field of Electrical Engineering, Technical Cybernetics, Engineering Sciences, Computer Science or comparable
• Experience and Knowledge: proficient programming skills in Python or Julia, as well as experience with Machine Learning and frameworks for automatic differentiation (e.g., PyTorch, JAX or Flux.jl); basic knowledge of dynamical systems (differential equations); understanding of the physics of electric machines, system identification; experience with MATLAB/Simulink to interact with legacy simulation models
• Personality and Working Practice: you are a communicative and reliable individual with a critical mindset and a proactive approach
• Languages: good in English 

Additional Information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Giulio Montecchio (Functional Department)
+49 173 590 8165

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