galpynostatic is a Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries.
If you have any questions, you can contact me at ffernandev@gmail.com
You need Python 3.12+ 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.
You can install the latest stable release of galpynostatic with pip
python -m pip install --upgrade pip
python -m pip install --upgrade galpynostatic
To learn how to use galpynostatic you can start by following the tutorials and then read the API.
galpynostatic is licensed under the MIT License.
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 DOIgalpynostatic.datasets: https://doi.org/10.1002/cphc.202200665
BibTeX entries can be found in the CITATIONS.bib file.