Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods

Code and data for the paper "Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods" by Ferran Brosa Planella, Muhammad Sheikh and W. Dhammika Widanage (2021).

What is in this repository?

In this repository you can find the scripts used to generate the code for the paper.

• The folder data/ contains the experimental data used for validation in a .csv format. The data is organised in subfolders for different temperatures (0degC, 10degC and 25degC). The methods in scripts/process_experimental_data.py can be used to import and process these files into your Python script.
• Running scripts/compare_TSPMe_TDFN.py generates the figures to compare the TSPMe against the TDFN model, reproducing Figures 5-7 in the paper. It also produces errors_models.txt which records the error between models shown in Table 2 of the paper. Note that new data is appended to this file every time the script is run.
• Running scripts/compare_TSPMe_data.py generates the figures to compare the TSPMe against experimental data, reproducing Figure 8-10 in the paper. It also produces errors_experiments.txt which records the error between TSPMe and experimental data shown in Table 5 of the paper. Note that new data is appended to this file every time the script is run.
• Running scripts/time_TSPMe_TDFN.py calculates the solving time for each model, reproducing the results in Table 3 of the paper.
• The Jupyter notebooks in notebooks reproduce the scripts but in a more interactive format.
• The scripts process_experimental_data.py and set_parameters.py contain auxiliary methods.

How to cite the code or data?

If you found the code or the data useful please cite our paper

F. Brosa Planella, M. Sheikh, and W. D. Widanage, Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods, Electrochimica Acta 388 (2021) 138524.

If you also find the code useful, apart from citing the paper above, please use the PyBaMM command

pybamm.print_citations()

at the end of your script to print in the terminal the bibtex of all the references that have contributed to your code (model, parameters, solvers...).

How to use the code?

In order to run the code you need to install this package. We strongly recommend to install it in a Python virtual environment, in order not to alter any distribution Python files. Assuming you work on Linux-based system you need to run:

1. Clone the repository
2. Go into the TEC-reduced-model folder: cd TEC-reduced-model
3. Create the virtual environment: virtualenv env
4. Activate the virtual environment: source env/bin/activate
5. Install the package: pip install .

Then you can run the scripts and notebooks. If you encounter any bugs or errors please let us know either via email or raising a GitHub issue.