The automotive industry is in the middle of a turbulent shift from personally owned, gas powered cars to autonomous, shared, electric mobility solutions such as robot axis. This shift changes key assumptions in automotive design: usage cost becomes much more important and all the use cases that a single owned vehicle had to support can now be covered by a fleet of shared vehicles. This change in stakeholder presents opportunities for new system architectures for vehicles. Current optimization techniques often focus on the component level, while the biggest impact is often to be found on the systems' level. The systems engineering process used in industry to derive architectures is often tedious and informal, resulting in suboptimal architectures. Formalizing and partially automating part of the concept design phase to enable fast creation and evaluation of architectures can lead to more optimal system architectures for vehicles. 

In this context, the topology of a system, namely the choice, placement and interconnection of its components, has a significant impact on its achievable performance and must be carefully studied. Therefore, the possibility to rapidly generate automatically optimized system architectures from a set of desired functionalities and a library of component technologies (platform) would ultimately greatly accelerate the deployment of electric vehicles and lead to better performance on the cost function (typically total-cost-of-ownership). The key questions relate to how to automatically extract engineering knowledge that supports the top-down design process by understanding which subsystems or components from the platform fulfil certain system-level functional requirements and how to connect them by formulating mathematical constraints as in a bottom-up design process. This research aims at devising discrete mathematical models and optimization models and methods for system-level powertrain design. Specifically, the student will first investigate state-of-the-art discrete modelling tools. Second, methods will be studied by using machine learning to translate functional requirements and engineering models to a general constrained optimization setting. The student will leverage and advance optimization methods to solve such discrete optimization problems and devise a platform-based design toolbox to perform extensive case-studies for a family of electric vehicles. The research concentrates on the transition from requirements to embodiment-in-design in the early conceptional design phase by automation that is supported by machine learning, constraint programming and model-based engineering methods.

The PhD project (part of work package 4: Electric Mobility) is performed within the framework of a larger NWO research project NEON that is funded on the support of the acceleration of the transition towards sustainable energy and mobility.

• talented, enthusiastic, with excellent analytical and communication skills and high grades 
• a MSc degree (or equivalent) in Mechanical, Electrical Engineering, Systems & Control or a related discipline,
• a strong background in system engineering, optimization and optimal control methods,
• experience and interests in transportation engineering, energy systems, modelling of powertrain systems, automotive technology, system design and control are of benefit,
• a research oriented attitude,
• ability to work in a team and <interested in collaborating with the industrial partners,
• fluent in spoken and written English.

•  A meaningful job in a highly motivated team at a dynamic and ambitious university with the possibility to present your work at international conferences. You will be part of a highly profiled multidisciplinary collaboration where expertise of a variety of disciplines comes together. The 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!
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.