Metadata-Version: 2.1
Name: galpynostatic
Version: 0.2.2
Summary: A physics-based heuristic model to predict the optimal electrode particle size for a fast-charging of lithium-ion batteries.
Author-email: Francisco Fernandez <ffernandev@gmail.com>
License: MIT License
        
        Copyright (c) 2022-2023 Francisco Fernandez
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/fernandezfran/galpynostatic
Keywords: battery,physics-based,data-driven,heuristic-algorithm,regression-models
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: importlib_metadata

# galpynostatic

[![galpynostatics CI](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml/badge.svg)](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml)
[![documentation status](https://readthedocs.org/projects/galpynostatic/badge/?version=latest)](https://galpynostatic.readthedocs.io/en/latest/?badge=latest)
[![pypi version](https://img.shields.io/pypi/v/galpynostatic)](https://pypi.org/project/galpynostatic/)
[![python version](https://img.shields.io/badge/python-3.9%2B-4584b6)](https://www.python.org/)
[![mit license](https://img.shields.io/badge/License-MIT-ffde57)](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE)
[![doi](https://img.shields.io/badge/doi-10.1016/j.electacta.2023.142951-36abe8)](https://doi.org/10.1016/j.electacta.2023.142951)

**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](https://matplotlib.org/), [NumPy](https://numpy.org/), 
[pandas](https://pandas.pydata.org/), [scikit-learn](https://scikit-learn.org/) 
and [SciPy](https://scipy.org/)), are installed automatically.


## Installation

You can install the most recent stable release of galpynostatic with 
[pip](https://pip.pypa.io/en/latest/)

```
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](https://galpynostatic.readthedocs.io/en/latest/tutorials/index.html)
and then read the 
[API](https://galpynostatic.readthedocs.io/en/latest/api/index.html).

Also, you can read the Jupyter Notebook pipelines in the
[papers folder](https://github.com/fernandezfran/galpynostatic/tree/main/papers) 
to reproduce the results of the published articles.


## License

galpynostatic is under 
[MIT License](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE).


## Citation

If you use galpynostatic in a scientific publication, we would appreciate it if 
you could cite the following 
[article](https://doi.org/10.1016/j.electacta.2023.142951)

> 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:

```bibtex
@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](https://github.com/fernandezfran/galpynostatic/blob/main/CITATION.bib)
file.


## Contact

You can contact me if you have any questions at <ffernandev@gmail.com>
