galpynostatic 0.2.1

A physics-based heuristic model to predict the optimal electrode particle size for a fast-charging of lithium-ion batteries.

galpynostatic

galpynostatic is a Python package with a physics-based heuristic model to predict the optimal electrode particle size for a fast-charging of lithium-ion batteries.

Requirements

You need Python 3.9+ to run galpynostatic. All other dependencies, which are the usual ones of the scientific computing stack (matplotlib, NumPy, pandas, scikit-learn and SciPy), are installed automatically.

Installation

You can install the most recent stable release of galpynostatic with pip

python -m pip install --upgrade pip
python -m pip install --upgrade galpynostatic

Usage

To learn how to use galpynostatic you can start by following the tutorials and then read the API.

Also, you can read the Jupyter Notebook pipelines in the papers folder to reproduce the results of the published articles.

License

galpynostatic is under MIT License.

Citation

If you use galpynostatic in a scientific publication, we would appreciate it if you could cite the following article

F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin, Y. Ein-Eli and E. P. M. Leiva. "Towards a fast-charging of LIBs electrode materials: a heuristic model based on galvanostatic simulations." Electrochimica Acta 464 (2023): 142951.

BibTeX entry:

@article{fernandez2023towards,
  title={Towards a fast-charging of LIBs electrode materials: a heuristic model based on galvanostatic simulations},
  author={Fernandez, F and Gavil{\'a}n-Arriazu, EM and Barraco, DE and Visintin, A and Ein-Eli, Y and Leiva, EPM},
  journal={Electrochimica Acta},
  volume={464},
  pages={142951},
  year={2023},
  publisher={Elsevier}
}

Other related citations can be found in the CITATION.bib file.

Contact

You can contact me if you have any questions at [e-mail]