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PhD - Hybrid Modelling in EMC
Aktualität: 26.04.2024


26.04.2024, Bosch-Gruppe
PhD - Hybrid Modelling in EMC
Electrified mobility has become a necessity. The Robert Bosch GmbH plays a leading role in developing electrical drive components. From e-bikes to heavy goods vehicles, motors, batteries, control units, etc. are interconnected with cable harnesses. The number of electric components that have a significant impact on the electromagnetic fields inside and outside the vehicles grows. And their arrangement can vary significantly in terms of their geometric design and their configurations. The field of electromagnetic compatibility (EMC) tries to keep the fields in check. But computational predictions and analysis of such complex electric drive systems suffers from this uncertainty and variability. Computational models are required in order to predict the radiated emissions of a configuration, to find more suitable designs related to EMC, cost and weight requirements as well as to search for the root causes of resonances seen in the emission spectrum that exceed the limits of radiated emission. Those models should ideally be quick to compute, consider the uncertainties of material properties, geometry etc. and match measurements precisely. You will take part in developing hybrid models, a combination of models from first principles and data-based modeling, that will act as digital twins to be used in the analysis, optimization and monitoring of an electromechanical system, with the focus on EMC. As a starting point, you will use simulation models of a single component and validate the results against measurements. You familiarize yourself with the electromechanical systems to be modeled. You will learn about the challenges and state of the art solutions in the creation of hybrid models for the use in EMC. You design the digital twin and tackle the challenges of the modelling exercise. Last but not least, you use the digital twin in an optimization or monitoring application.
Education: completed studies in the field of Electrical Engineering, Physics, Mathematics, Information Technology, Computer Science or similar Experience and Knowledge: familiar with data-driven modelling techniques and coding, ideally with a background in electromagnetic compatibility Personality and Working Practice: proactive, independent and creative in your work