Simulation#
- class pybamm.Simulation(model, experiment=None, geometry=None, parameter_values=None, submesh_types=None, var_pts=None, spatial_methods=None, solver=None, output_variables=None, C_rate=None, discretisation_kwargs=None)[source]#
A Simulation class for easy building and running of PyBaMM simulations.
- Parameters:
model (
pybamm.BaseModel
) – The model to be simulatedexperiment (
pybamm.Experiment
or string or list (optional)) – The experimental conditions under which to solve the model. If a string is passed, the experiment is constructed as pybamm.Experiment([experiment]). If a list is passed, the experiment is constructed as pybamm.Experiment(experiment).geometry (
pybamm.Geometry
(optional)) – The geometry upon which to solve the modelparameter_values (
pybamm.ParameterValues
(optional)) – Parameters and their corresponding numerical values.submesh_types (dict (optional)) – A dictionary of the types of submesh to use on each subdomain
var_pts (dict (optional)) – A dictionary of the number of points used by each spatial variable
spatial_methods (dict (optional)) – A dictionary of the types of spatial method to use on each domain (e.g. pybamm.FiniteVolume)
solver (
pybamm.BaseSolver
(optional)) – The solver to use to solve the model.output_variables (list (optional)) – A list of variables to plot automatically
C_rate (float (optional)) – The C-rate at which you would like to run a constant current (dis)charge.
discretisation_kwargs (dict (optional)) – Any keyword arguments to pass to the Discretisation class. See
pybamm.Discretisation
for details.
- build(initial_soc=None, inputs=None)[source]#
A method to build the model into a system of matrices and vectors suitable for performing numerical computations. If the model has already been built or solved then this function will have no effect. This method will automatically set the parameters if they have not already been set.
- build_for_experiment(initial_soc=None, inputs=None)[source]#
Similar to
Simulation.build()
, but for the case of simulating an experiment, where there may be several models and solvers to build.
- create_gif(number_of_images=80, duration=0.1, output_filename='plot.gif')[source]#
Generates x plots over a time span of t_eval and compiles them to create a GIF. For more information see
pybamm.QuickPlot.create_gif()
- plot(output_variables=None, **kwargs)[source]#
A method to quickly plot the outputs of the simulation. Creates a
pybamm.QuickPlot
object (with keyword arguments ‘kwargs’) and then callspybamm.QuickPlot.dynamic_plot()
.- Parameters:
output_variables (list, optional) – A list of the variables to plot.
**kwargs – Additional keyword arguments passed to
pybamm.QuickPlot.dynamic_plot()
. For a list of all possible keyword arguments seepybamm.QuickPlot
.
- plot_voltage_components(ax=None, show_legend=True, split_by_electrode=False, show_plot=True, **kwargs_fill)[source]#
Generate a plot showing the component overpotentials that make up the voltage
- Parameters:
ax (matplotlib Axis, optional) – The axis on which to put the plot. If None, a new figure and axis is created.
show_legend (bool, optional) – Whether to display the legend. Default is True.
split_by_electrode (bool, optional) – Whether to show the overpotentials for the negative and positive electrodes separately. Default is False.
show_plot (bool, optional) – Whether to show the plots. Default is True. Set to False if you want to only display the plot after plt.show() has been called.
kwargs_fill – Keyword arguments, passed to ax.fill_between.
- save(filename)[source]#
Save simulation using pickle module.
- Parameters:
filename (str) – The file extension can be arbitrary, but it is common to use “.pkl” or “.pickle”
- save_model(filename: str | None = None, mesh: bool = False, variables: bool = False)[source]#
Write out a discretised model to a JSON file
- Parameters:
mesh (bool) – The mesh used to discretise the model. If false, plotting tools will not be available when the model is read back in and solved.
variables (bool) – The discretised variables. Not required to solve a model, but if false tools will not be availble. Will automatically save meshes as well, required for plotting tools.
filename (str, optional) – The desired name of the JSON file. If no name is provided, one will be created based on the model name, and the current datetime.
- set_up_and_parameterise_experiment()[source]#
Create and parameterise the models for each step in the experiment.
This increases set-up time since several models to be processed, but reduces simulation time since the model formulation is efficient.
- solve(t_eval=None, solver=None, save_at_cycles=None, calc_esoh=True, starting_solution=None, initial_soc=None, callbacks=None, showprogress=False, inputs=None, **kwargs)[source]#
A method to solve the model. This method will automatically build and set the model parameters if not already done so.
- Parameters:
t_eval (numeric type, optional) –
The times (in seconds) at which to compute the solution. Can be provided as an array of times at which to return the solution, or as a list [t0, tf] where t0 is the initial time and tf is the final time. If provided as a list the solution is returned at 100 points within the interval [t0, tf].
If not using an experiment or running a drive cycle simulation (current provided as data) t_eval must be provided.
If running an experiment the values in t_eval are ignored, and the solution times are specified by the experiment.
If None and the parameter “Current function [A]” is read from data (i.e. drive cycle simulation) the model will be solved at the times provided in the data.
solver (
pybamm.BaseSolver
, optional) – The solver to use to solve the model. If None, Simulation.solver is usedsave_at_cycles (int or list of ints, optional) – Which cycles to save the full sub-solutions for. If None, all cycles are saved. If int, every multiple of save_at_cycles is saved. If list, every cycle in the list is saved. The first cycle (cycle 1) is always saved.
calc_esoh (bool, optional) – Whether to include eSOH variables in the summary variables. If False then only summary variables that do not require the eSOH calculation are calculated. Default is True.
starting_solution (
pybamm.Solution
) – The solution to start stepping from. If None (default), then self._solution is used. Must be None if not using an experiment.initial_soc (float, optional) – Initial State of Charge (SOC) for the simulation. Must be between 0 and 1. If given, overwrites the initial concentrations provided in the parameter set.
callbacks (list of callbacks, optional) – A list of callbacks to be called at each time step. Each callback must implement all the methods defined in
pybamm.callbacks.BaseCallback
.showprogress (bool, optional) – Whether to show a progress bar for cycling. If true, shows a progress bar for cycles. Has no effect when not used with an experiment. Default is False.
**kwargs – Additional key-word arguments passed to solver.solve. See
pybamm.BaseSolver.solve()
.
- step(dt, solver=None, t_eval=None, save=True, starting_solution=None, inputs=None, **kwargs)[source]#
A method to step the model forward one timestep. This method will automatically build and set the model parameters if not already done so.
- Parameters:
dt (numeric type) – The timestep over which to step the solution
solver (
pybamm.BaseSolver
) – The solver to use to solve the model.t_eval (list or numpy.ndarray, optional) – An array of times at which to return the solution during the step (Note: t_eval is the time measured from the start of the step, so should start at 0 and end at dt). By default, the solution is returned at t0 and t0 + dt.
save (bool) – Turn on to store the solution of all previous timesteps
starting_solution (
pybamm.Solution
) – The solution to start stepping from. If None (default), then self._solution is used**kwargs – Additional key-word arguments passed to solver.solve. See
pybamm.BaseSolver.step()
.