Recent years have witnessed an exponential increase in battery research, driven by the need to develop efficient and sustainable energy storage systems. One of the main tools in battery research is battery cycling experiments, providing insights into the performance, lifetime and quality of the battery. Due to the large variety of battery testing equipment and the resulting multitude of different and often proprietary data formats, combined with the large number of parameters involved, managing and processing battery testing data has often been a difficult and tedious task.
The Python library cellpy assists in solving these problems by
1. providing tools to read different data formats,
2. converting those into one common data format that also includes relevant battery-specific metadata, and
3. providing a data structure equipped with a set of methods that helps the user to easily perform simple and in-depth analyses of both single data sets and collections of data sets.