2 PhD's on Optimal Congestion-aware Control and Design of Charging Energy Hubs

2 PhD's on Optimal Congestion-aware Control and Design of Charging Energy Hubs

Published Deadline Location
4 Oct 18 Feb Eindhoven

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We are seeking two highly motivated PhD students who are interested in pursuing research at the interface of control systems and optimization for smart grid technologies.

Job description

Driven by regulation and an expected lower total cost of ownership, the number of electric trucks and vans will increase substantially in the coming years, resulting in an expected additional electricity demand of almost 15% compared to the current total consumption in the Netherlands by 2050. The current electricity grid and regulations are not prepared to accommodate the huge increase in electricity demand for electric vehicles and the increase in renewable energy generation, leading to grid balancing and capacity problems on the electric grid that may ultimately impede the electrification of mobility and logistics. This project is part of a multidisciplinary consortium aimed at accelerating the electrification of the logistics sector by developing technological innovations to enable the integration of the charging infrastructure, battery storage systems, and distributed renewable generation within the existing energy grid. In this context, the design and the coordination of these components is of paramount importance to achieve a robust and sustainable energy grid operation.

PhD 1: Within this project, we aim at developing (distributed) optimal energy management control algorithms to coordinate energy consumers (charging stations, energy storage systems, etc.) and producers (generators, energy storage systems, etc.), with the main goal of optimizing infrastructure utilization. Specifically, we foresee three major subtasks: 1) To develop a demand prediction model to forecast system state (charging demand, power generation, etc.), possibly while maintaining user privacy; 2) to develop an implementable online control framework for optimal energy distribution and storage, e.g., leveraging optimal control methods and model predictive control (MPC) algorithms, possibly in a distributed fashion; 3) to benchmark the developed control framework by implementing it into a digital twin and compare its performance with existing rule-based approaches and (offline) strategies.

PhD 2: Within this project, we aim at developing a digital-twin for scenario-based analyses and optimal system design of energy charging hubs considering, e.g., logistical planning activities, charging profiles, local storage systems, flexible assets (electrolysers, battery systems), and grid capacity. Specifically, we foresee four major subtasks: 1) Specification of an agent-based scenario toolbox and its interfacing with the open system design toolbox; 2) Development of an agent-based modelling environment for scenario-based analysis; 3) Validation of the models against real-world user-design case studies; 4) Optimization algorithms for combined charging infrastructure design and fleet operation, given other (stochastic) exogenous inputs.

These positions are part of the joint interdisciplinary Nationaal Groeifonds project Charging Energy Hubs between, among others, the Control Systems Technology section in the Department of Mechanical Engineering at TU/e, Firan, Maxem, Netherlands Organization for Scientific Research (TNO), and Scholt Energy. During the project, the candidates will have opportunities to mentor students at many levels and take part in international scientific events.


Eindhoven University of Technology (TU/e)


Talented, enthusiastic, and open-minded candidates with excellent analytical and communication skills are encouraged to apply.
A MSc degree in Mechanical Engineering, Electrical Engineering, Computer Science, Cybernetics, or a related discipline is required, as well as a strong background in control engineering, programming, and system modelling and identification. Experience and interest in power grid systems, model predictive control, and/or numerical optimization are of advantage.

Conditions of employment

We offer a competitive position for four years starting from January 2024 in a highly motivated and welcoming team at a dynamic and ambitious university. You will be part of a highly profiled multidisciplinary collaboration where expertise of a variety of disciplines come together. TU/e is in one of the smartest regions of the world and part of the European technology hotspot 'Brainport Eindhoven', well-known because of many high-tech industries and start-ups. A place to be for talented scientists! 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, scale P (min. €2,770 max. €3,539).
  • 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.


  • PhD
  • Engineering
  • max. 38 hours per week
  • University graduate
  • V35.6979


Eindhoven University of Technology (TU/e)

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De Rondom 70, 5612 AP, Eindhoven

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