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 sim.plot()
or simulate an experiment such as a constant-current discharge followed by a constant-current-constant-voltage charge:
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()) sim.solve() sim.plot()
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 on GitHub. In addition, full details of classes and methods can be found in the API documentation. Additional supporting material can be found here.