Theses & Projects

Below, you see a snapshot of some open thesis and project topics for bachelor's and master's students (if you are looking for PhD positions, please check the TUHH recruitment portal and the news page on our website). Typically, we have some more topics; please talk, best after alecture or an exercise, directly to the team to find out more. Most of our topics are based on our courses (Distributed Systems, Internet of Things, TinyML, etc.). We expect a bachelor student to have taken at least one of our elective courses and a master's student to have also taken one (preferably multiple) courses with us. For each thesis, we list required background, including the course(s) taken with us and often also courses given by other institutes. Also, for master's students, it is a nice bonus to conduct a project with us before starting a thesis with us. Connecting both will ease your learning curve on the background, methods, and tools. 

For each topic, we list a contact person. If you have taken courses with us and have the required background, please don't hesitate to contact them directly when a topic sounds interesting. Furthermore, you are welcome to contact Olaf or Marcus to have a chat with them about the different topics and how they match your interests and background, i.e., how they fit the courses you have taken. When you contact us, please name the course(s) you have taken with us, and please also include a list of courses you have taken (from TUNE) at TUHH and wherever else you studied

Work atmosphere, workshops, presentations: We offer you a great work atmosphere, motivated advisors, and a coffee machine (or tea if you prefer). Next to frequent meetings and discussions with your supervisor(s), each term we also organize a series of workshops to guide you throughout the different phases of your thesis and project work, such as how to work scientifically, evaluation, writing, and presentation. Students doing their thesis and project with us are expected to join these workshops to gain the proper knowledge to conduct their work and shall also regularly join the kickoff and final presentations of other students at our institute to learn how to present their work and their results. Commonly, the workshops and presentations are once a month on Wednesday afternoon, commonly at 2pm. 

Conducting a thesis with a company: This is an option, but not the default one. As a result, additional requirements apply (next to the ones stated above). We expect the topic to (a) have a strong research component like any other thesis, (b) be close to our teaching and research and the course(s) you have taken with us, and (c) at the company to be supervised by members of the R&D department. They shall have research and supervision experience, for example, from PhD studies. Please check this before contacting us, and when you contact us about doing a thesis with a company, please also let us know a bit about the topic and the company (next to listing your courses taken with us and including a list of courses you have taken from TUNE at TUHH and wherever else you studied, see above). 

(Last update 30.07.2026)

 

Open Topics

TinyML for Second Life Battery State Prediction
Master Thesis

Topic Description

The end-of-life (EOL) of an EV battery is usually reached when its capacity drops to 70-80% of the original capacity. But by clustering multiple EOL EV batteries we can give them a "second life" in a stationary setting. This requires an embedded management system make load balancing decisions based on the State of Charge (SoC), State of Helath (SoH), and Remainin Useful Life (RUL) of the batteries. This, in turn, requires the predition of SoC, SoH, and RUL on an embedded device.

In this thesis students can work on challenges related to TinyML models for SoC and SoH prediction in a second life usage context. The baseline work should be the implementation of such a model based on open source datasets and open platforms. In recent literature from Giazitzis et al. (2024) a TinyML model for SoC prediction was developed, that a student may reproduce for that purpose.

From there the student can choose from several research directions, including, e.g., developing an Neural Network architrecture, that works across battery chemistries; quantifying the prediction uncertainty with Bayesian methods; or quickly assessing the state of a used battery cell of unknown history.

Requirements

  • TinyML module passed with very good grades
  • done at least one additional ML course

Nice-To-Have

  • knowledge of Kalman Filtering
  • knowledge of electrochemistry

Literature

  • Gregory Plett, "Battery Management Systems", Volume I: Battery Modeling , Artech, 2015.
  • S. Giazitzis, M. Sakwa, E. Ogliari, S. Badha and F. Rosetti, "Tiny Machine Learning for Li-ion Battery State of Health Estimation", 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON), Porto, Portugal, 2024, pp. 1019-1024, doi: 10.1109/MELECON56669.2024.10608784.

Contact: Bernhard Germann