pybamm-team / pybamm-eis

PyBaMM EIS

PyBaMM EIS rapidly calculates the electrochemical impedance of any battery model defined using PyBaMM, see the documentation

🔋 Using PyBaMM EIS

The easiest way to use PyBaMM EIS is to compute the impedance of a model of your choice with the default parameters:

import pybammeis
import pybamm
import numpy as np

# DFN with capacitance
model = pybamm.lithium_ion.DFN(options={"surface form": "differential"})
eis_sim = pybammeis.EISSimulation(model)
# calculate impedance at log-spaced frequencies
eis_sim.solve(np.logspace(-4, 4, 30))
eis_sim.nyquist_plot()

💻 About PyBaMM

The example simulations use the package PyBaMM (Python Battery Mathematical Modelling). PyBaMM is an open-source battery simulation package written in Python. Our mission is to accelerate battery modelling research by providing open-source tools for multi-institutional, interdisciplinary collaboration. Broadly, PyBaMM consists of (i) a framework for writing and solving systems of differential equations, (ii) a library of battery models and parameters, and (iii) specialized tools for simulating battery-specific experiments and visualizing the results. Together, these enable flexible model definitions and fast battery simulations, allowing users to explore the effect of different battery designs and modeling assumptions under a variety of operating scenarios.

🚀 Installation

In order to run the notebooks in this repository you will need to install the pybamm-eis package. We recommend installing within a virtual environment in order to not alter any python distribution files on your machine.

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

Linux/Mac OS

To install the requirements on Linux/Mac OS use the following terminal commands:

1. Clone the repository

   git clone https://github.com/pybamm-team/pybamm-eis

2. Change into the pybamm-eis directory

   cd pybamm-eis

3. Create a virtual environment

   virtualenv env

4. Activate the virtual environment

   source env/bin/activate

5. Install the pybammeis package

   pip install .

Windows

To install the requirements on Windows use the following commands:

1. Clone the repository

   git clone https://github.com/pybamm-team/pybamm-eis

2. Change into the pybamm-eis directory

   cd pybamm-eis

3. Create a virtual environment

   python -m virtualenv env

4. Activate the virtual environment

   \path\to\env\Scripts\activate

   where \path\to\env is the path to the environment created in step 3 (e.g. C:\Users\'Username'\env\Scripts\activate.bat).

5. Install the pybammeis package

   pip install .

As an alternative, you can set up Windows Subsystem for Linux. This allows you to run a full Linux distribution within Windows.

Developer

To install as a developer follow the instructions above, replacing the final step with

pip install -e .

This will allow you to edit the code locally.

📖 Documentation

API documentation for the pybammeis package can be built locally using Sphinx. To build the documentation, first change into the docs directory, and then execute the following command:

make html

This will generate a number of html files in the docs/_build/html directory. To view the documentation, open the file docs/_build/html/index.html in a web browser.

🛠️ Contributing to PyBaMM

If you'd like to help us develop PyBaMM by adding new methods, writing documentation, or fixing embarrassing bugs, please have a look at these guidelines first.

🎉 Acknowledgments

This code was developed as part of the Oxford Mathematics Summer Project "Efficient Linear Algebra Methods to Determine Li-ion Battery Behaviour".

Student: Rishit Dhoot Supervisors: Prof Colin Please and Dr. Robert Timms

📫 Get in touch

For any questions, comments, suggestions or bug reports, please see the contact page.

📃 License

pybamm-eis is fully open source. For more information about its license, see LICENSE.

pybamm-eis's Issues

Interpolate results from iterative_method

iterative_method should just return impedances interpolated back onto the original frequencies passed to solve.

Make logo

Fix docs build

Semi-circle for the SPMe

Hi,

Comparing EIS data of different models in compare_models.py, it appears that the SPMe does not have a semi-circle for the same electrode double-layer capacity as the other models.

options={"surface form": "differential"} and both positive and negative electrode double-layer capacities are set to 0.2 F m-2 for the figure below

This is unexpected as the semi-circles of all models should be quite similar!

Interestingly, semi-circles for the SPMe appear when choosing the electrode double-layer capacity values unreasonably high:

100 F/m2:

compared to 0.2 F/m2:

This may be related to the bug in leading surface form conductivity (pybamm-team/PyBaMM#4139), however, using the develop branch does not solve this issue.

We use PyBAMM version 24.1.

Kind regards,

Noël

Avoiding spectral leakage in time domain (brute force) simulation

Hi,

To compute the impedance in the time domain in examples/compare_methods.py the model response to a sinusoidal current is computed.

To avoid spectral leakage when taking the FFT I recommend to simulate an integer number of periods.

This can be solved by removing the 1+ on line 38 and -1 on lines 41 to 43.

The difference between frequency domain and time domain simulations will then become smaller!

Kind regards,

Noël

Remove SymbolReplacer

pybamm.SymbolReplacer was removed in v22.10. Update set_up_model_for_eis to formulate the model differently.

Try out `pybamm-cookiecutter`

@santacodes I think this repo could be a suitable candidate for you to try https://github.com/pybamm-team/pybamm-cookiecutter when we have the basic structure ready within a few weeks.

For context, this repo was originally proposed for adding the calculation of electrochemical impedance spectroscopy within the PyBaMM framework as a GSoC project (https://pybamm.org/gsoc/2022/#electrochemical-impedance-spectroscopy-eis-modelling), and later worked on as a summer project at the University of Oxford (see https://github.com/pybamm-team/pybamm-eis#-acknowledgments).

P.S. EIS is like a fancy tool to measure the battery's internal resistances and activities at different points, it acts like an AC tester of sorts that can give out information like how easily electrons are flowing through the materials the battery is made out of. Imagine gently pushing tiny electrical charges back and forth at various frequencies, which can help you find stuff like the resistance at the electrode surfaces (where the reactions are happening). @rtimms can probably correct me if I am wrong 😅

Though this project has been set up and hosted already, if we can verify from start to end that the template is able to generate all the files here and provide a reasonable starting point for constructing the files under https://github.com/pybamm-team/pybamm-eis/tree/main/pbeis, while also providing a way to integrate the docs under docs/ and with RTD, we can say that the template is ready to handle reasonably sized real-world research projects and we can then work on any shortcomings we find

Add docs

Hard coded reaction rate and electrode charge transfer coefficients

The reaction rate and electrode charge transfer coefficient parameters are hard coded in the exchange current density function (through the Butler-Volmer kinetics). I believe these are parameters that vary from battery to battery.

Wouldn't it make more sense to also consider these parameters in the parameter set? This would help for model parametrisation from impedance data as the reaction rate parameter could then represent the charge transfer resistance in a model where parameters are grouped.

Add tests

Host readthedocs

Get docs on readthedocs (and make them prettier)

Update infra

testing, use pyproject.toml, etc.