galpynostatic 0.1.0

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 3.8+ 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 -U pip
python -m pip install -U 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 pipeline in the paper folder to reproduce the results of the published article.

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, E. P. M. Leiva. "Towards a fast-charging of LIBs electrode materials: a heuristic model based on galvanostatic simulations" (2023). Electrochimica Acta

BibTeX entry:

@article{fernandez2023towards,
    title = {Towards a fast-charging of LIBs electrode materials: a heuristic model based on galvanostatic simulations},
    journal = {Electrochimica Acta},
    pages = {142951},
    year = {2023},
    issn = {0013-4686},
    doi = {https://doi.org/10.1016/j.electacta.2023.142951},
    url = {https://www.sciencedirect.com/science/article/pii/S001346862301126X},
    author = {F. Fernandez and E.M. Gavilán-Arriazu and D.E. Barraco and A. Visintin and Y. Ein-Eli and E.P.M. Leiva},
    keywords = {Fast-charging, Lithium-Ion Battery, Heuristic Model, Galvanostatic charge},
    abstract = {Fast charging is one of the most important features to be accomplished for the improvement of electric vehicles. In the search for optimal use of active materials for this aim, we present a recipe to find the conditions for fast charging, fifteen minutes for 80 % of the State-of-Charge, of lithium-ion battery's single particle electrodes, thus taking advantage of the maximum possible capacity. A guide based on a general model that considers diffusion and charge transfer limitations under constant current is proposed. This guide was constructed on the basis of our previous theoretical development. A Python free and user-friendly package is provided to handle all experimental data processing and estimations.}
}

Contact

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