To accelerate the introduction of smart and green heavy-duty transport, we are investigating ultra-efficient engines that run on renewable fuels. These advanced combustion concepts are characterized by controlled auto-ignition. As a result, they lack direct control of combustion phasing and of rate of heat release and are sensitive for varying operating conditions. Therefore, closed-loop combustion control is essential to realize stable and safe operation.


 

In this PhD project, we aim to develop innovative control strategies that optimize the overall system efficiency by on-line shaping of the heat release process while meeting safety and emission constraints. Main objective is auto-tuning of the engine's control settings for every combustion cycle using real-time estimation of engine performance quantities. This self-learning, cycle-to-cycle control strategy shows close analogy with reinforcement learning concepts. Focus is on model-based development of observers and on systematic control analysis and synthesis for the studied nonlinear discrete-time system. The potential of the developed self-learning control strategies will be demonstrated on our state-of-the-art engine platform using FPGA control hardware in our Zero Emission Lab.  

The position is funded for four years.

We are looking for a talented, outstanding PhD candidate with a M.Sc. degree in Systems and Control, Electrical or Mechanical Engineering, or a related field, with background and/or interest in automotive powertrain applications, and with good command of the English language. Furthermore, the candidate is a team player, who will work together with colleague researchers.

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, scale P (min. €2,872 max. €3,670).
• 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.