https://github.com/fernandezfran/galpynostatic
:zap::battery: A Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries
https://github.com/fernandezfran/galpynostatic
battery data-driven fast-charging heuristic-algorithm metrics physics-based predictions regression-models
Last synced: 5 months ago
JSON representation
:zap::battery: A Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries
- Host: GitHub
- URL: https://github.com/fernandezfran/galpynostatic
- Owner: fernandezfran
- License: mit
- Created: 2022-12-06T11:37:08.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T14:17:03.000Z (6 months ago)
- Last Synced: 2024-11-01T22:06:17.489Z (6 months ago)
- Topics: battery, data-driven, fast-charging, heuristic-algorithm, metrics, physics-based, predictions, regression-models
- Language: Python
- Homepage: https://galpynostatic.readthedocs.io/
- Size: 5.16 MB
- Stars: 8
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Citation: CITATIONS.bib
Awesome Lists containing this project
- open-sustainable-technology - galpynostatic - A Python/C++ package with physics-based models to predict optimal conditions for fast-charging lithium-ion batteries. (Energy Storage / Battery)
README
# galpynostatic
[](https://github.com/fernandezfran/galpynostatic/actions/workflows/CI.yml)
[](https://galpynostatic.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/galpynostatic/)
[](https://www.python.org/)
[](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE)
[](https://doi.org/10.1016/j.electacta.2023.142951)
**galpynostatic** is a Python/C++ package with physics-based and data-driven
models to predict optimal conditions for fast-charging lithium-ion batteries.
## Contact
If you have any questions, you can contact me at
## Requirements
You need Python 3.12+ 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 latest 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).
## License
galpynostatic is licensed under the
[MIT License](https://github.com/fernandezfran/galpynostatic/blob/main/LICENSE).
## Citations
If you use galpynostatic in a scientific publication, we would appreciate it if
you could cite the main article of the package:
> 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. DOI: https://doi.org/10.1016/j.electacta.2023.142951
For certain modules of the code, please refer to other works:
- `galpynostatic.metric`: TODO DOI
- `galpynostatic.datasets`: https://doi.org/10.1002/cphc.202200665
BibTeX entries can be found in the
[CITATIONS.bib](https://github.com/fernandezfran/galpynostatic/blob/main/CITATIONS.bib)
file.