Compare models in PyBaMM

This repository shows an example of how to compare models for speed/accuracy in PyBaMM. It contains two parameter sets from the literature:

1. Parameters for an LG M50 cell taken from the paper

   Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the Electrochemical Society 167 (2020): 080534

   with additional thermal parameters from

   Ferran Brosa Planella, Muhammad Sheikh, and W. Dhammika Widanage, "Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods." Electrochimica Acta Volume 388, 2021, 138524

   The parameter set is stored in Chen2020_params.py and provides functional forms for parameters that depend on state (e.g., open circuit potential as a function of concentration) and scalar values for the remaining parameters. The associated measured data for the functions are also provided as .csv files in the data folder with the same name as the function.

2. Parameters for a Kokam SLPB 75106100 cell, from the papers

   Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery I. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.

   Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.

   The thermal material properties are for a 5 Ah power pouch cell by Kokam. The data are extracted from

   Zhao, Y., et al. "Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance."" Journal of The Electrochemical Society, 165.13 (2018): A3169-A3178.

   The parameter set is stored in Ecker2015_params.py and provides functional forms for parameters that depend on state (e.g., open circuit potential as a function of concentration) and scalar values for the remaining parameters.

🚀 Installation

In order to run the models and load in any data you will need to install pybamm. The notebooks have been tested on PyBaMM Version 22.2. To install the required python packages on Linux/Mac OS use the following terminal commands:

1. Clone the repository

https://github.com/rtimms/compare-pybamm-models

2. Change into the compare-pybamm-models directory

cd compare-pybamm-models

3. Create a virtual environment (optional)

virtualenv env

4. Activate the virtual environment (optional)

source env/bin/activate

5. Install the required packages

pip install -r requirements.txt

PyBaMM is available on GNU/Linux, MacOS and Windows. For more detailed instructions on how to install PyBaMM, see the PyBaMM documentation.