Thesis Work: Machine Learning based Identification of Electric Motors Parameters for Control

OmrådeVästerås
Publicerad2025-10-14
Ansök senastÖppet tills vidare

Om jobbet

På ABB hjälper vi industrier att bli snabbare, mer resurseffektiva och hållbara. Här är framsteg en självklarhet - för dig, ditt team och hela världen. Som global marknadsledare ger vi dig rätt förutsättningar för att lyckas med det. Det kommer inte alltid att vara enkelt - utveckling kräver mod och styrka. På ABB är du aldrig ensam. Run what runs the world.

Denna position rapporterar till:
R&D Department Lead




You will be part of Powertrain & Digitalization team at ABB Corporate Research in Västerås, Sweden.

At Corporate Research we lead the innovation within ABB and our task is to ensure ABB's technology competitiveness now and in the future. We work in close collaboration with other research centers, our business areas: Motion, Process Automation, Robotics & Discrete Automation and Electrification, as well academic and industrial partners.

In our creative and highly skilled team we develop, design, build and test new concepts and prototypes of physical and digital powertrains consisting of electric energy source, electrical motor, electric drive, and a selected application.

Details

  • Period: January - July 2026
  • 30 ECTS per student
  • Number of students: 1
  • Location: On site ABB Corporate Research in Västerås, Sweden or Hybrid

Your responsibilities

  • Conduct a state‑of‑the‑art review on machine‑learning methods for electric motor parameter identification
  • Develop, implement, and evaluate ML‑based parameter identification models in MATLAB/Simulink for a selected motor type
  • Design simulation studies and, where applicable, structured experiments to generate identification data under representative operating conditions
  • Compare ML approaches with classical/system‑identification baselines and quantify accuracy, robustness, and computational footprint
  • Analyze the effect of identified parameters on torque‑control performance, including steady‑state accuracy and dynamic response
  • Explore generalization strategies across operating points and model mismatch, including noise robustness and domain shift
  • Prepare clean, version‑controlled code, reproducible experiments, and clear documentation to enable handover
  • Summarize findings in a thesis report and presentation; contribution to a scientific publication is encouraged when results warrant it

Your background

  • Currently pursuing a Master's degree in Electrical Engineering or a closely related field
  • Good working knowledge of MATLAB and Simulink
  • Interest in control theory and power electronics; familiarity with electric machines is beneficial
  • Familiarity with data‑driven methods and fundamentals of machine learning or system identification is an advantage
  • Theoretical grounding in signals and systems, statistics, and optimization will be beneficial for model design and evaluation
  • Strong interest in research and clear technical communication in English

More about us

ABB is a global technology leader in electrification and automation. We see our purpose as being to enable a more sustainable and resource-efficient future. By connecting our engineering and digitalization expertise, we help industries run at high performance, while becoming more efficient, productive and sustainable so they outperform. We call this: 'Engineered to Outrun.'

You are welcome to apply the latest by November 21. Please note that selection will be done on an ongoing basis and the position may be filled before last day of application. We look forward to receiving your application (preferably in English).

Join us. Be part of the team where progress happens, industries transform, and your work shapes the world. Run What Runs the World.

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