Project Overview#

Introduction#

ampworks is a Python package designed to streamline the ingestion, processing, and interpretation of battery experiment data. It focuses on quick data visualizations and converting raw laboratory measurements into physically meaningful parameters—such as internal resistances, open-circuit potentials, and diffusion-related metrics—that support modeling, analysis, and decision-making.

The package emphasizes reproducible workflows, consistent data structures, and clear interfaces that make it easier to compare results across experiments, chemistries, and testing protocols.

Key Features#

  • Unified data loading and preprocessing: Support for importing common battery test formats and transforming them into standardized, analysis-ready representations.

  • Parameter extraction workflows: Tools to compute resistance components, OCP curves, diffusivity, voltage relaxation behavior, and other model-relevant quantities.

  • Degradation and life-model analysis: Functions for interpreting HPPC data, differential capacity (dQ/dV) results, capacity fade trends, and metrics related to loss of active material or lithium inventory.

  • Modular and extensible architecture: Designed so new analyses, file formats, and models can be added without disrupting existing workflows.

  • Reproducible and transparent: Emphasis on well-structured API design, clear documentation, and traceable data transformations.

Use Cases#

  • Generating input parameters for battery models (e.g., ECM, SPM, P2D).

  • Quantifying resistance growth, OCP shifts, and diffusion limitations over life.

  • Processing HPPC or relaxation tests to analyze rate- or SOC-dependent behavior.

  • Extracting indicators of degradation modes such as LAM, LLI, or impedance rise.

  • Building standardized pipelines for ingesting raw cycler data and producing comparable outputs across test campaigns.

  • Supporting research, diagnostics, and algorithm development in battery performance analysis.

Target Audience#

  • Battery engineers and researchers working with experimental test data.

  • Model developers needing clean and consistent parameter extraction tools.

  • Data scientists analyzing degradation patterns or building predictive life models.

  • Organizations running large experimental test programs that require reproducible pipelines.

  • Students and newcomers seeking accessible methods for interpreting battery measurements.

Technology Stack#

  • Language: Python

  • Compatibility: Runs on any hardware that supports Python. Multiple versions are supported.

Project Origins#

ampworks was developed out of a need for a unified, practical toolkit that bridges the gap between raw laboratory test data and the parameters and insights demanded by modern battery modeling and diagnostics.

Existing tools were either too domain-specific, too rigid, or lacked transparency. ampworks grew from repeated internal workflows and has evolved into a general-purpose package for the broader battery research community.

Roadmap and Future Directions#

While the package continues to evolve, future directions include:

  • Expanding support for additional cycler file formats and metadata standards.

  • Adding more physics-based and data-driven parameter extraction methods.

  • Improving visualization utilities for diagnostics and degradation tracking.

  • Enhancing validation tools and benchmark datasets for repeatable, comparable analyses.

ampworks is intended to grow as experimental methods, modeling practices, and research needs evolve, while maintaining a stable, intuitive API and a clear focus on extracting meaningful information from real battery data.

Contributions#

The ampworks project is hosted and actively maintained on GitHub. Developers interested in contributing are encouraged to review the Code structure and Workflow sections for detailed information on the branching strategy, code review process, and how to get involved. All contributions are welcome.