Upcoming Presentations
Monday, 31. August 2026
The presentation will take place at 1:00 p.m. in seminar room FD.00.01.
Information about the speaker can be found here:
Development of a Database-Based System for the Analysis, Visualization, and Evaluation of Battery Measurement Data
This master's thesis investigates the development of a database-based system for the analysis, visualization, and rule-based assessment of heterogeneous battery measurement data. The motivation for this work is the considerable manual effort required to process measurements provided in the form of MDF/MF4 files, Scienlab report packages, and multi-channel ESD campaigns. Differences in file structures, channel naming conventions, and the documentation of test conditions complicate consistent and reproducible data evaluation.
The developed prototype catalogs measurement sources in a local SQLite database without unnecessarily duplicating large raw data files. It maps differently named raw channels to a unified signal model and supports the traceable identification of both the test type and the battery context. Test-specific evaluation workflows determine characteristic parameters for capacity, open-circuit voltage (OCV), electrochemical impedance spectroscopy (EIS), hybrid pulse power characterization (HPPC), alternating current internal resistance (ACIR), and additional resistance measurements. Furthermore, the system processes module measurements and multi-channel test campaigns. The resulting analyses are supplemented with transparent rule-based assessments, data quality indicators, and documented limitations, and are presented through technical visualizations and a comprehensive PDF report.
The proposed approach was evaluated using representative cell, module, and campaign datasets, as well as automated software tests. Four case studies demonstrate the successful import and analysis of a capacity measurement, a binary Scienlab cell report, a module measurement with multiple voltage taps, and an ESD campaign with incomplete channel coverage. The results show that the explicit distinction between threshold violations, missing evidence, and non-comparable measurement conditions effectively prevents unjustified positive assessments of battery condition.