Publications#
The following publications showcase research carried out using PyDPEET. Where available, links to the publication, source code, and example notebooks are provided. If you have used PyDPEET in your own work, feel free to get in touch—we would be happy to include your publication on this page.
Abstract
This work presents an automated method for reconstructing full-cell open-circuit voltage (OCV) curves from highly dynamic battery field data. Unlike conventional laboratory-based OCV analysis, the approach identifies OCV points during short rest periods and combines them with a database-driven half-cell fitting procedure to reconstruct the complete OCV curve. This enables overvoltage-independent capacity estimation, identification of degradation mechanisms such as loss of lithium inventory (LLI) and loss of active material (LAM), and battery diagnostics without dedicated laboratory tests. The method is applicable even to incomplete charge and discharge cycles.
Resources
Cite
Schlösser, A., Otto, M., & Kowal, J. (2026, April 14). Automated OCV Extraction and Reconstruction from Highly Dynamic Field Data. Advanced Battery Power 2026, Münster. https://doi.org/10.13140/RG.2.2.35966.75843
Abstract
PyDPEET (“Data Processing for Electrical Energy Storage Technologies”) is an open-source Python package developed to facilitate battery data analysis. It targets a common problem: lab and field tests produce large, mixed datasets in different file formats. Manual preprocessing is slow, error-prone, and hard to reproduce. PyDPEET offers a transparent workflow that standardises raw files, processes them with consistent rules, and provides evaluation functionality within a highly integrated code base. The package also offers full user flexibility via custom parameter configuration.
Resources
Cite
Otto, M., Schlösser, A., Schröder, D., Simone, C. D., Hinrichsen, A., Kalisch, J., & Kowal, J. (2026, April 14). PyDPEET: A Python Package for Fast and Easy Battery Data Unification, Processing, and Analysis. Highly Dynamic Field Data. Advanced Battery Power 2026, Münster. https://doi.org/10.13140/RG.2.2.35127.89768.