What is ampworks?#

ampworks is a Python package for processing and analyzing experimental battery data, with an emphasis on extracting quantities that are useful for battery modeling and diagnostics. The package is designed to bridge the gap between raw laboratory data and the structured parameters needed for electrochemical models, degradation/life models, machine learning models, and other generally useful feature summaries (e.g., capacity, internal resistance, coulombic efficiency, etc.). The package is built on top of the pandas library and provides a consistent interface for working with battery data, regardless of the source or format of the data. The package also includes a set of tools for visualizing and analyzing battery data, as well as a set of utilities for working with battery data in a variety of formats.

In addition to feature extraction and analysis, ampworks also provides a set of tools for data cleaning, preprocessing, and visualization. You can generate interactive plots powered by plotly to rapidly explore your data prior to or after processing. The interactive plots allow you to include hover tips so you can quickly identify cycle and step numbers for slicing out segments for further analysis.

While ampworks is built mostly for scriptable Python workflows, some analysis modules also include optional graphical user interfaces (GUIs), making the package accessible to users trying to automate pipelines and those performing interactive data exploration. The package is especially useful in research environments, where reproducibility and transparency are important.

Use Cases#

The ampworks package is designed to be flexible and can be used in a variety of contexts. Some common use cases include:

  • Providing a consistent interface for working with battery data, regardless of the source or original format of the data.

  • Automating data processing pipelines for large datasets of battery data.

  • Cleaning and preprocessing battery data to prepare it for analysis.

  • Visualizing battery data to gain insights into the behavior of the battery.

  • Extracting features from raw battery data for use in machine learning models or other downstream analyses.

Acknowledgements#

This work was authored by the National Laboratory of the Rockies (NLR), operated by Alliance for Energy Innovation, LLC, for the U.S. Department of Energy (DOE). The views expressed in the package and its documentation do not necessarily represent the views of the DOE or the U.S. Government.