Student Projects

During your project, you will develop a mathematical model of the perfusion machine, which will be used to test new control algorithms in simulation. In a first phase, you will focus on the fluid dynamic component of the machine, modeling flows and pressures along the pipes. To gather the necessary data for the parameter identification, you will run your own experiments on the perfusion machine testbench and get the opportunity to collaborate with an entire team of dedicated and experienced researchers from USZ and the IDSC group. The second phase of the project is the development of sophisticated model-based controllers. Specifically, your goal is to develop a MIMO controller for the pressure and flow at the organ’s connections.

Derive a mathematical model and a corresponding MIMO controller for the perfusion machine

Marc-Philippe Neumann, mneumann@idsc.mavt.ethz.ch Dr. David Machacek, davidm@ethz.ch

Several Swiss cities have committed to electrifying their entire bus fleets over the next few decades. In this process, many important decisions need to be made that can have a significant impact on the efficiency and cost of electrification, such as the choice of charging infrastructure or the size and type of batteries. In the ongoing Swiss EBus Plus project, we have therefore developed and published a Python-based open source toolbox for the simultaneous optimization of charging station placement, battery size, and charging strategy. 1 The next phase of this research aims to enhance the toolbox for broader applications. A close collaboration with PostAuto is particularly valuable, given their diverse operational profiles in urban, rural, and alpine regions. In a first step, the compatibility with the available data must be ensured and tested across all Swiss regions. To allow broad application of the toolbox, additional charging strategies (induction, trolley, etc.) and energy sources (battery-assisted solar, fuel cells, etc.) will have to be implemented. Furthermore, the toolbox will be extended to incorporate further constraints, including regional limitations identified through accompanying research at other institutions. Lastly, computational efficiency needs to be improved to speed up the optimization process. The project spans 12 months, divided into a 6-month master’s thesis followed by a 6-month full-time scientific assistantship. To maintain continuity, both parts are ideally completed by the same person.

1. Literature Review & Data Compatibility – Review existing research on electric bus fleet optimization and ensure compatibility of the toolbox with available Swiss regional data.
2. Expansion of Charging Strategies & Energy Sources – Implement additional charging methods (e.g., induction, trolley) and integrate alternative energy sources (e.g., battery-assisted solar, fuel cells).
3. Optimization & Constraint Integration – Extend the toolbox to incorporate regional constraints and operational limitations identified through external research collaborations.
4. Computational Efficiency Improvement – Enhance the toolbox’s performance to accelerate optimization processes and enable real-time feasibility analysis.
5. Documentation & Validation of Results – Systematically document enhancements, validate results through case studies, and ensure usability for future applications.

Mohammad Moradi, ML H 42.1, moradim@ethz.ch

This master thesis focuses on advancing energy management systems for hybrid ships through the application of Dynamic Programming (DP) and Model Predictive Control (MPC). A key aspect of this research involves integrating Long Short-Term Memory (LSTM) models into DP and MPC frameworks while providing enhanced predictive capabilities for system load forecasting, energy demand estimation, and operational decision-making. As part of an interdisciplinary team, you will contribute to the development of an optimization toolbox tailored to maritime energy management.

The following tasks must be tackled during the study: 1. Literature review on the state-of-the-art in maritime energy management. 2. Development of DP- and MPC-based Algorithms for optimizing hybrid ship energy usage. 3. Cost function definition for ship energy consumption, considering the real-world constraint. 4. Integration of LSTM models within DP and MPC frameworks for enhanced prediction and control. 5. Documentation of the results.

Markus Wenig, ML H 42.1, E-Mail: mwenig@ethz.ch Mohammad Moradi, ML H 42.1, E-Mail: moradim@ethz.ch