Student for thesis analysis of battery cell measurement data

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Number: MER00046IN Our center is responsible for the Powertrain Prototyping & Testing in the field of drive development of Mercedes-Benz AG. In the Testing Components & Aggregates department, cells, modules and high-voltage batteries are tested and evaluated in a wide range of designs using various methods. In the Team Testing Cell & Modul Electrical and Functions Track and accompany the product development process from cell/module prototype to series pattern. Background: Modern battery cell tests generate huge amounts of time series measurement data. While machine-readable test specifications define how tests are to be carried out, the resulting measurement data often lacks an explicit representation of the underlying test structure. In order to enable future automated validation of executed tests against their specifications, recurring patterns within the measurement data must first be identified and converted into a structured representation of the actual test sequence. We are looking for a student who researches, designs, implements and evaluates a reusable software framework that is able to automatically discover such patterns from real battery test data. These challenges come to you: Objective Development of a reusable python framework that: Recurring patterns in battery cell measurement data automatically identified. Detecting test structures directly from measurement signals, even if bookmarks or notes are missing Confidential pattern recognition and multiple candidate interpretations supported Recognized patterns and their relationships through a generic and expandable data model Scaled to industrial records with millions of data points Provides a clean, reusable API for integration in future applications Research topics Potential areas of investigation include: Time series pattern recognition Matrix profiles and time series mining techniques Similarity Search and Form-based Conformity Unmonitored pattern recognition and clustering Machine learning for sequence analysis Scalable data processing architectures Relevant technologies may be: Python Polar DuckDB Dask Apache Arrow STUMPY Multi-core and GPU-accelerated processing Available data The project may include: 165,000 battery cell measurements More than 50 different test types are used. This offers a unique opportunity to develop and validate solutions with large industrial records. Expected results The final solution should include: A functional prototype for automated pattern identification A reusable Python API Confidence assessment for detected patterns Support for alternative pattern interpretations Automated tests and documentation Validation and performance assessment based on real measurement data The final topics will be discussed with the university, you and us. The activity can start from October 2026.

Studies in Computer Science, Data Science, Electrical Engineering, Mechatronics, Computational Engineering, Mathematics or Similar Technical Degrees focusing on software development or data analysis

Knowledge in: Python software development Data Science and Analytics Time series analysis Machine learning Software architecture and design Battery systems and testing

Personal competencies: Secure knowledge of German and English in word and writing

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Additional information: We are looking forward to your online application with CV, lettering, certificates, current enrollment certificate with an indication of the semester and proof of the regular study period. Please do not forget to mark your documents as ''relevant for this application' in the online form and to observe the maximum file size of 5 MB. Further information on the setting criteria can be found here. Disabled and equalized applicants are welcome! The severely disabled representative (sbv-untertuerkheim@mercedes-benz.com) is happy to support you in the application process. People Solutions will be happy to help you with questions about the application process. You can reach us by email via myhrservice@mercedes-benz.com or by phone at 0711/17-99000 (Mo-Fr 10-12am & 13-15am).

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