PhD on AI based control methods for High Efficiency Electrical Drive Systems

Are you looking for an exciting opportunity in one of Europe’s high-tech regions? Do you have passion to contribute towards a zero waste society by working towards data-driven modeling of next-gen electrical drive systems. Then we have a job for you!

Irène Curie Fellowship

No

Department(s)

Electrical Engineering

Reference number

V36.6299

Big Data and data-driven techniques have fundamentally changed our world and continue to do so. They are the key enabler, together with connectivity, for smart interconnected systems that add value in terms of cost, efficiency and performance. The design of these systems, both in
e-mobility systems as well as in industrial applications, needs to consider various aspects.
As examples, the investigation of the effect of the material choice to ensure a low carbon footprint, the optimization of the tradeoff between efficiency and cost, or the estimating maintenance requirements based on a life cycle analysis are all complex questions which require multi-physical, cross-domain system representations of a system and its components. These representations are nowadays possible through the use of data-driven techniques and allow for the evaluation of fundamental research questions.

This PhD trajectory will focus on data-driven modeling and control for electrical drives (electrical machine and controller) to achieve an increased system reliability and efficiency. Next generation electrical machines in automotive tend to be more complex and highly nonlinear in their magnetic design to ensure high efficiency and power densities in the lack of hard magnetic materials (to reduce their carbon footprint). Modern control methods provide an opportunity both to handle these nonlinear systems efficiently as well as provide an integration basis towards digitization of the complete drive.  

One of the main goals of this research is to determine the interfaces to be used in computer simulations to develop complex cyber-physical systems. The required investigations involve: how Big Data and data-driven modeling efforts can be leveraged to produce multi-domain models of a modern drive system, that encompass – among others - dynamic behavior, thermal aspects, and efficiency maps. These models will allow for diverse characterizations of the motor and controller separately, as well as of their interaction in the electrical drive. Another goal is to investigate the merging of data-driven techniques with other classical modern control approaches for developing high-performance controllers for electrical drives featuring new machine designs targeting the automotive industry.

Your tasks will comprise creating and understanding the interactions in the multi-physical high fidelity models of electrical drives. With the help of data-driven models both nonlinear machine behavior and existing controller behavior will be dynamically represented with Functional Mockup Interfaces (FMIs). This interface standard is used in computer simulations to develop cyber-physical systems (e.g. FMU (Functional Mockup Unit) and Digital Twin of the traction electrical drive). By collecting and creating big/field data, different characteristic features (e.g. fault selection, aging, derating, etc.) of the drive will be defined which effect reliability and efficiency performance. You will also perform in-depth analysis on which techniques to use on AI for the real-time control (online) and for the (offline) characterization of the nonlinear machine and control parameters. The findings will contribute to transition from model-based to data-driven approaches which integrate big data for future electrical drive designs.

Your findings will also contribute to the active projects within the EPE group and TUe research institutes (CPSe and EAISI -Responsible Mobility as possible input for the use-cases relevant with Digital Twin development of the relevant systems in high-tech and mobility. Besides research you will also contribute to education within the department.

About the EPE group:

Eindhoven University of Technology (TU/e) is a world-leading research university specializing in engineering science & technology. Within Department of Electrical Engineering, Electromechanics and Power Electronics (EPE) group has one of the most advanced electromechanics and power electronics laboratories in the Netherlands. It contains state-of-the-art facilities for testing and validation of electromechanical and power electronics systems.

The EPE group consists of 2 full professors, 10 associate and assistant professors, several postdocs, about 30 PhD candidates and support staff. The EPE group is world-renowned for 6-DoF planar motors and levitated stages, wireless energy transfer concepts, tubular actuation concepts, and high-precision power electronics.

The main focus areas of the EPE group are applied and fundamental research in the clusters of high-precision technology, automotive, medical, renewable energy and grids. In recent years, the research focus has strongly grown for innovative and energy-saving conversion systems in line with industrial demands. All research areas are greatly supported by the national and international programs.

We are looking for candidates matching the following profile:

• MSc degree in Electrical Engineering, Control Engineering, Aerospace Engineering, Applied Mathematics/Artificial Intelligence or Mechatronics
• Affinity with
  • AI and Machine learning techniques and implementation on research projects
  • Nonlinear system optimization
  • Different electrical machine design and control techniques, as well as their experimental validation
  • Model Predictive Control and experimental validation
  • FPGA programming
• Solid practical skills in programming (Matlab, Simulink, Phyton, CUDA, PLecs, etc),
• Knowledge of and experience in these fields is a big plus:
  • Mobility systems and/or industrial servo applications
  • Parameter identification of electrical drive systems
  • Classical and modern control theory and related optimization techniques
  • Functional Mock-up Units (FMU), Functional Mock-up Interfaces (FMI)
  • Electrical machine FEM simulation programs (Ansys, Altaire, Comsol, JMAG, etc.)
• A very strong analytical background, with sound mathematical skills and excellent problem solving abilities for multi-physical systems
• Ability to independently organize your own work, to solve problems, to achieve desirable goals and to cooperate
• Project management skills are a plus
• Ability to participate in the teaching process in BSc and MSc programs (both taught in English)
• Good scientific writing and documentation skills
• Strong command of the English language (knowledge of Dutch/German language is a plus)
• Excellent scientific writing skills, preferably proven with peer-reviewed publications
• Ability and willingness to participate in the educational program offered by the group in the BSc and MSc programs (both taught in English)

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities.
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

About us

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. 

Information

Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager dr. Esin Ilhan Caarls (e.ilhan[at]tue.nl).

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.Flux[at]tue.nl.

Are you inspired and would like to know more about working at TU/e? Please visit our career page .

Application

We invite you to submit a complete application by using the 'apply now'-button on this page. Applications should include the following files in pdf format separately:

• letter of motivation including;
  • explaining your motivation and suitability for the position
  • 2-page plan on how your research is going to contribute on the specific project
  • portfolio with relevant work
• detailed Curriculum Vitae inc. months (not just years)  with:
  • education and work experience
  •  a list of your publications,  
  • key achievements in research projects;
  • contact information of two references (please do not include any letters);
• brief (1-page description) of your MSc thesis, transcript, course names & grades,
• results of IELTS or TOEFL test

A selection of applications will be invited for job interviews. During the interviews, the applicant(s) will be asked to present their portfolio, and defend her/his research plans.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.

Keywords:

AI, Data driven models, Electrical Drives, Control, Digital Twin, FMU (Functional Mock-up Unit), FMI (Functional Mock-up Interface), Co-simulation, Automated Mobility applications, Cyber-Physical Systems.