PyBaMM (Python Battery Mathematical Modelling) solves physics-based electrochemical DAE models by using state-of-the-art automatic differentiation and numerical solvers. The Doyle-Fuller-Newman model can be solved in under 0.1 seconds, while the reduced-order Single Particle Model and Single Particle Model with electrolyte can be solved in just a few milliseconds. Additional physics can easily be included such as thermal effects, fast particle diffusion, 3D effects, and more. All models are implemented in a flexible manner, and a wide range of models and parameter sets (NCA, NMC, LiCoO2, ...) are available. There is also functionality to simulate any set of experimental instructions, such as CCCV or GITT, or specify drive cycles.

How do I use PyBaMM?

The easiest way to use PyBaMM is to run a 1C constant-current discharge with a model of your choice with all the default settings:

import pybamm
model = pybamm.lithium_ion.DFN()  # Doyle-Fuller-Newman model
sim = pybamm.Simulation(model)
sim.solve([0, 3600])  # solve for 1 hour

or simulate an experiment such as CCCV:

import pybamm
experiment = pybamm.Experiment(
        ("Discharge at C/10 for 10 hours or until 3.3 V",
        "Rest for 1 hour",
        "Charge at 1 A until 4.1 V",
        "Hold at 4.1 V until 50 mA",
        "Rest for 1 hour")
    * 3,
model = pybamm.lithium_ion.DFN()
sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver())

However, much greater customisation is available. It is possible to change the physics, parameter values, geometry, submesh type, number of submesh points, methods for spatial discretisation and solver for integration (see DFN script or notebook).

For new users we recommend the Getting Started guides. These are intended to be very simple step-by-step guides to show the basic functionality of PyBaMM, and can either be downloaded and used locally, or used online through Google Colab.

Further details can be found in a number of detailed examples, hosted here on
github. In addition, there is a full API documentation,
hosted on Read The Docs.
Additional supporting material can be found

For further examples, see the list of repositories that use PyBaMM here

How can I install PyBaMM?

PyBaMM is available on GNU/Linux, MacOS and Windows.
We strongly recommend to install PyBaMM within a python virtual environment, in order not to alter any distribution python files.
For instructions on how to create a virtual environment for PyBaMM, see the documentation.

Using pip

pip install pybamm

Using conda

PyBaMM is available as a conda package through the conda-forge channel.

conda install -c conda-forge pybamm

Optional solvers

On GNU/Linux and MacOS, an optional scikits.odes-based solver is available, see the documentation.