BATMODS-lite

Summary

Battery Analysis and Training Models for Optimization and Design Studies (BATMODS) is a Python package with an API for pre-built battery models. The original purpose of the package was to quickly generate synthetic data for machine learning models to train with. However, the models are generally useful for any battery simulations or analysis. BATMODS-lite includes the following:

1. A library and API for pre-built battery models
2. Kinetic/transport properties for common battery materials

Note that the package focuses on phsics-based models like the single-particle (SPM) and pseudo-2D (P2D) model. If you enjoy the interface but are looking to run equivalent circuit models (ECMs), you should check out thevenin, which is distributed separately, but is developed and maintained by the same team. Consequently, the interface to thevenin models is intentionally similar, allowing for a smooth transition between physics-based and ECM approaches.

Installation

BATMODS-lite can be installed from PyPI using the following command:

pip install batmods-lite

If you run into issues with installation due to the scikit-sundae dependency, please submit an issue here. We also manage this solver package, but distribute it separately since it is not developed in pure Python.

For those interested in setting up a developer and/or editable version of this software, please see the directions available in the "Development" section of our documentation.

Get Started

The API is organized around three main classes that allow you to construct simulations, define experiments, and interact with solutions. Two basic examples are given below. These demonstrate a 2C discharge for both the single particle model (SPM) and pseudo-2D (P2D) model. Note that the experiment class interfaces with all simulations. The simulations and their respective solutions, however, will depend on the model subpackage they are loaded from. For more detailed examples, check out the documentation on Read the Docs. Note that while the full name of the package is batmods-lite, the installed module is imported using bmlite.

# Single particle model example
import bmlite as bm

sim = bm.SPM.Simulation()

expr = bm.Experiment()
expr.add_step('current_C', 2., (1350., 10.))

soln = sim.run(expr)
soln.simple_plot('time_s', 'voltage_V')

# Pseudo-2D model example
import bmlite as bm

sim = bm.P2D.Simulation()

expr = bm.Experiment()
expr.add_step('current_C', 2., (1350., 10.))

soln = sim.run(expr)
soln.simple_plot('time_s', 'voltage_V')

Notes:

• If you are new to Python, check out Spyder IDE. Spyder is a powerful interactive development environment (IDE) that can make programming in Python more approachable to new users.
• Another friendly option for getting started in Python is to use Jupyter Notebooks. We write our examples in Jupyter Notebooks since they support both markdown blocks for explanations and executable code blocks.
• Python, Spyder, and Jupyter Notebooks can be setup using Anaconda. Anaconda provides a convenient way for new users to get started with Python due to its friendly graphical installer and environment manager.

Citing this Work

This work was authored by researchers at the National Laboratory of the Rockies (NLR). If you use this package in your work, please include the following citation:

Randall, Corey R. "BATMODS-lite: Packaged battery models and material properties [SWR-25-108]." Computer software, Jun. 2025. url: github.com/NatLabRockies/batmods-lite. doi: 10.11578/dc.20260114.1.

For convenience, we also provide the following for your BibTex:

@misc{randall2025bmlite,
  author = {Randall, Corey R.},
  title = {{BATMODS-lite: Packaged battery models and material properties [SWR-25-108]}},
  url = {github.com/NatLabRockies/batmods-lite},
  month = {Jun.},
  year = {2025},
  doi = {10.11578/dc.20260114.1},
}

Contributing

If you'd like to contribute to this package, please look through the existing issues. If the bug you've caught or the feature you'd like to add isn't already being worked on, please submit a new issue before getting started.

Disclaimer

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 repository do not necessarily represent the views of the DOE or the U.S. Government.