galpynostatic 0.5.13

A Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries.

galpynostatic

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 [e-mail]

Requirements

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.

Installation

You can install the latest 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.

License

galpynostatic is licensed under the MIT 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 file.