PyDPEET (“Python 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 many 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 comes with a set of standard parameters to allow users to quickly explore the actual evaluation process, while also preserving full user flexibility via custom parameter configuration.

The package converts inputs from different battery cycling equipment into a single, machine-readable table (Pandas DataFrame) that contains harmonised meta data, step identifiers, voltage, current, temperature, test time, absolute time, and — if provided — impedance data (frequency, real part, imaginary part, and DC offset current). On this basis, PyDPEET automatically detects protocol segments (e.g., constant-current, constant-voltage, rest, or ramp phases) and splits experiments into meaningful sequences, such as iOCV, HPPC, and pulse tests. User-defined segmentation rules are supported through a clean and simple configuration syntax. Built-in routines compute key indicators, including state of charge, state of health, capacity, power, energy, internal resistance, coulombic efficiency, and more. The design prioritises scalability and reproducibility: multi-campaign datasets can be processed and analysed in a few lines of user code, and identical pipelines yield identical results across chemistries, formats, and test devices. This enables consistent review and comparison of large datasets. The codebase is modular and uses an open-source approach, which lowers the barrier to extension and review. New importers, metrics, and analysis modules can be added easily. The open-source model also enables community contributions and supports transparent verification of methodology and results.

In summary, PyDPEET offers a robust and extensible foundation for fast and consistent battery data analysis. Planned work includes tooling for real-world operating data, advanced model-based functions such as parameterisation of equivalent-circuit models and mechanistic models, and a graphical user interface to further simplify the usage while keeping workflows transparent and scriptable.