Master Thesis AI-based Power Electronics Control

BOSC; Renningen; Hybrid; EUR 13.000 - 20.000; Teilzeit

Benefits dieser Stelle

Hybrides Arbeitsmodell

Zusammenfassung

Die Robert Bosch GmbH in Renningen sucht eine Masterarbeit im Bereich Hochleistungselektronik und Regelung. Du entwickelst eine Closed-Loop-Regelstrategie für neuartige AC/DC/DC-DC‑Topologien und validierst sie in einer Simulation.

Du arbeitest hybrid, nutzt MATLAB/Simulink, Python bzw. PyTorch, und bewertest Transienten, Effizienz und Stabilität gegenüber Basiskonzepten. Die Abschlussarbeit umfasst Dokumentation und Präsentation.

Qualifikationen

• Masterstudium im Bereich Elektrotechnik, Regelungstechnik, Mechatronik, Kybernetik, Informatik oder vergleichbar.
• Kenntnisse in Regelungssystemen (Zustandsraummethoden, nichtlineare/prädiktive Regelung) und robustem Entwurf.
• Sicherer Umgang mit MATLAB/Simulink und Python; Kenntnisse in PyTorch von Vorteil.

Aufgaben

• Wähle das vielversprechende Konzept und entwerfe eine maßgeschneiderte Regelstrategie anhand eines bestehenden Simulationsmodells.
• Untersuche fortschrittliche Closed-Loop-Kontrollmethoden mit Fokus auf MPC, neuronale Netze und RL für Leistungselektronik.
• Implementiere den entwickelten Algorithmus in MATLAB/Simulink, Python oder PLECS.

Kenntnisse

Zustandsraummethoden

Nichtlineare Regelung

Prädiktive/ Moderne Regelung

Lösungsorientiertes Arbeiten

Ausbildung

Masterstudium in Elektrotechnik / Regelungstechnik / Mechatronik oder vergleichbar

Tools

MATLAB/Simulink

Python

PyTorch

PLECS

Jobbeschreibung

Bei Bosch gestalten wir Zukunft mit hochwertigen Technologien und Dienstleistungen, die Begeisterung wecken und das Leben der Menschen verbessern. Unser Versprechen an unsere Mitarbeiterinnen und Mitarbeiter steht dabei felsenfest: Wir wachsen gemeinsam, haben Freude an unserer Arbeit und inspirieren uns gegenseitig. Willkommen bei Bosch.

Die Robert Bosch GmbH freut sich auf eine Bewerbung!

Modern power electronic systems, specifically AC/DC and DC/DC converters, are the vital backbone of tomorrow's technological landscape:

• In E-Mobility, they enable ultra-fast, highly efficient onboard charging systems and robust power delivery.
• In Robotics, they drive the high-dynamic, precise actuators and electric drives necessary for collaborative and autonomous systems.
• In Data Centers they ensure maximum energy efficiency, power density, and uninterrupted operations.

To meet the strict efficiency and volume requirements of these applications, modern power electronics increasingly employ advanced soft-switching topologies. While these topologies significantly reduce switching losses and maximize power density, they exhibit highly non-linear dynamics. Concurrently, cost and space optimization demand smaller energy storage components (capacitors and inductors), which further intensifies system instability and complex dynamic behavior. Traditional linear control methods (like standard PI controllers) struggle to maintain optimal performance under these challenging conditions.

This Master Thesis aims to explore, compare, and implement next-generation closed-loop control algorithms to handle these non-linearities and push the boundaries of converter performance.

• During your thesis, you will select the most promising control concept and design a tailor‑made control strategy using an already existing simulation model of our next‑generation charger/converter topologies.
• You will investigate advanced closed‑loop control methodologies suitable for highly non‑linear power electronics, with a focus on Model Predictive Control (MPC), Neural Network‑based control architectures and Reinforcement Learning (RL) approaches for power converters.
• Furthermore, you will implement the developed control algorithm in a simulation environment (e.g., MATLAB/Simulink, Python or PLECS).
• Besides, you will validate and benchmark the control concept under realistic operating conditions, evaluating transient behavior, efficiency, and stability compared to state‑of‑the‑art baseline controllers.
• You will analyze and benchmark the identified control strategies regarding execution effort, dynamic response, robustness against parameter variations, and suitability for real‑time implementation.
• Last but not least, you will do the documentation and presentation.

• Education: Master studies in the field of Electrical Engineering, Control Theory, Mechatronics, Cybernetics, Computer Science or comparable
• Experience and Knowledge: strong background in control systems (state‑space, non‑linear control, or predictive/modern control methods); proficient with simulation tools such as MATLAB/Simulink and/or Python with machine learning frameworks such as PyTorch; knowledge of power electronics (AC/DC and DC/DC converters) is an advantage
• Personality and Working Practice: you are a goal‑oriented and motivated person with a solution‑oriented mindset
• Work Routine: we offer you the opportunity to work in a hybrid setup
• Enthusiasm: high motivation to work on cutting‑edge research at the intersection of power electronics and intelligent control systems (machine learning, reinforcement learning)
• Languages: business fluent in English or German

Start: according to prior agreement

Duration: 6 months

Requirement for this thesis is the enrollment at university.

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.