Bachelor- or Masterthesis - easily detachable battery cell connections

The Fraunhofer-Gesellschaft (www.fraunhofer.com) currently operates 76 institutes and research units throughout Germany and is a leading applied research organization. Around 32 000 employees work with an annual research budget of 3.4 billion euros.

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Bachelor- or Masterthesis for easily detachable battery cell connections (all genders)

Darmstadt

Here you create change

With the increasing use of electric vehicles, the need for sustainable and efficient recycling processes for traction batteries is also growing. A key step is the automated, safe, and non-destructive separation of individual cells from used battery modules – particularly to reuse them either as second-life cells or to recover high-quality materials.

As part of an ongoing research project, Fraunhofer LBF is developing an automated disassembly system. This system will now be expanded to include a module for integrating real-time battery cell health diagnostics. The goal is to apply electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability.

A pre-trained machine learning model for assessing cell condition based on EIS data is available and will be integrated into the system.

Your Tasks

Literature research on the condition diagnosis of lithium-ion cells with a focus on EIS
Design and simulate a probe system (potentially integrated with a robotic head) for cell contact and impedance measurement
Analysis and preprocessing of EIS data
Integration and deployment of an existing ML model (e.g., as a Python module, API, or embedded system)
Evaluation of real-time capability and measurement quality
Validation of the solution in a prototype test environment
Documentation and presentation of the results

This is your contribution

Electrical Engineering / Mechatronics / Automation Engineering / Computer Science or related fields
Interest in battery technologies and electromobility
Basic knowledge of electrochemistry or willingness to learn
Experience in working with Python, MATLAB or comparable tools for signal analysis
Knowledge of machine learning (especially model deployment) is an advantage
Structured, independent, and solution-oriented working style
Good communication skills (German & English)

What we have in store for you

An individually tailored task with plenty of creative freedom
A highly topical and practically relevant research topic with direct relevance to the circular economy
The opportunity to actively participate in an innovative and interdisciplinary project
Access to state-of-the-art laboratory equipment and professional support
Prospects for a further scientific or industrial career
Insight into current developments in battery cell disassembly and diagnostics

Home office option by arrangement (not 100%).

Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled people are given preference if they are equally suitable.

Our tasks are varied and adaptable - we work together with applicants with disabilities to find solutions that best promote their abilities. The same applies if they do not meet all the profile requirements due to a disability.

Do you have questions about the position? Our colleague Savan Dihora is there for you: Phone +49 6151 705-573

Fraunhofer Institute for Structural Durability and System Reliability LBF

www.lbf.fraunhofer.de

Requisition Number: 80490 Application Deadline: 09/01/2025

Job Segment: Computer Science, Electrical Engineering, Electrical, Laboratory, Engineer, Engineering, Technology, Science

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