#
# Simulation class
#
import pickle
import pybamm
import numpy as np
import copy
import warnings
import sys
def is_notebook():
try:
shell = get_ipython().__class__.__name__
if shell == "ZMQInteractiveShell": # pragma: no cover
# Jupyter notebook or qtconsole
cfg = get_ipython().config
nb = len(cfg["InteractiveShell"].keys()) == 0
return nb
elif shell == "TerminalInteractiveShell": # pragma: no cover
return False # Terminal running IPython
elif shell == "Shell": # pragma: no cover
return True # Google Colab notebook
else:
return False # Other type (?)
except NameError:
return False # Probably standard Python interpreter
def constant_current_constant_voltage_constant_power(variables):
I = variables["Current [A]"]
V = variables["Terminal voltage [V]"]
s_I = pybamm.InputParameter("Current switch")
s_V = pybamm.InputParameter("Voltage switch")
s_P = pybamm.InputParameter("Power switch")
n_cells = pybamm.Parameter("Number of cells connected in series to make a battery")
return (
s_I * (I - pybamm.InputParameter("Current input [A]"))
+ s_V * (V - pybamm.InputParameter("Voltage input [V]") / n_cells)
+ s_P * (V * I - pybamm.InputParameter("Power input [W]") / n_cells)
)
[docs]class Simulation:
"""A Simulation class for easy building and running of PyBaMM simulations.
Parameters
----------
model : :class:`pybamm.BaseModel`
The model to be simulated
experiment : :class:`pybamm.Experiment` (optional)
The experimental conditions under which to solve the model
geometry: :class:`pybamm.Geometry` (optional)
The geometry upon which to solve the model
parameter_values: :class:`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: :class:`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.
"""
def __init__(
self,
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,
):
self.parameter_values = parameter_values or model.default_parameter_values
if isinstance(model, pybamm.lithium_ion.BasicDFNHalfCell):
if experiment is not None:
raise NotImplementedError(
"BasicDFNHalfCell is not compatible "
"with experiment simulations yet."
)
if experiment is None:
# Check to see if the current is provided as data (i.e. drive cycle)
current = self._parameter_values.get("Current function [A]")
if isinstance(current, pybamm.Interpolant):
self.operating_mode = "drive cycle"
elif isinstance(current, tuple):
raise NotImplementedError(
"Drive cycle from data has been deprecated. "
+ "Define an Interpolant instead."
)
else:
self.operating_mode = "without experiment"
if C_rate:
self.C_rate = C_rate
self._parameter_values.update(
{
"Current function [A]": self.C_rate
* self._parameter_values["Nominal cell capacity [A.h]"]
}
)
self._unprocessed_model = model
self.model = model
else:
self.set_up_experiment(model, experiment)
self.geometry = geometry or self.model.default_geometry
self.submesh_types = submesh_types or self.model.default_submesh_types
self.var_pts = var_pts or self.model.default_var_pts
self.spatial_methods = spatial_methods or self.model.default_spatial_methods
self.solver = solver or self.model.default_solver
self.output_variables = output_variables
# Initialize empty built states
self._model_with_set_params = None
self._built_model = None
self._built_initial_soc = None
self.op_conds_to_built_models = None
self._mesh = None
self._disc = None
self._solution = None
# ignore runtime warnings in notebooks
if is_notebook(): # pragma: no cover
import warnings
warnings.filterwarnings("ignore")
[docs] def set_up_experiment(self, model, experiment):
"""
Set up a simulation to run with an experiment. This creates a dictionary of
inputs (current/voltage/power, running time, stopping condition) for each
operating condition in the experiment. The model will then be solved by
integrating the model successively with each group of inputs, one group at a
time.
This needs to be done here and not in the Experiment class because the nominal
cell capacity (from the parameters) is used to convert C-rate to current.
"""
self.operating_mode = "with experiment"
if not isinstance(experiment, pybamm.Experiment):
raise TypeError("experiment must be a pybamm `Experiment` instance")
# Save the experiment
self.experiment = experiment
# Update parameter values with experiment parameters
self._parameter_values.update(experiment.parameters)
# Create a new submodel for each set of operating conditions and update
# parameters and events accordingly
self._experiment_inputs = []
self._experiment_times = []
for op, events in zip(experiment.operating_conditions, experiment.events):
operating_inputs = {
"Current switch": 0,
"Voltage switch": 0,
"Power switch": 0,
"CCCV switch": 0,
"Current input [A]": 0,
"Voltage input [V]": 0, # doesn't matter
"Power input [W]": 0, # doesn't matter
}
op_control = op["electric"][1]
if op_control in ["A", "C"]:
capacity = self._parameter_values["Nominal cell capacity [A.h]"]
if op_control == "A":
I = op["electric"][0]
Crate = I / capacity
else:
# Scale C-rate with capacity to obtain current
Crate = op["electric"][0]
I = Crate * capacity
if len(op["electric"]) == 4:
# Update inputs for CCCV
op_control = "CCCV" # change to CCCV
V = op["electric"][2]
operating_inputs.update(
{
"CCCV switch": 1,
"Current input [A]": I,
"Voltage input [V]": V,
}
)
else:
# Update inputs for constant current
operating_inputs.update(
{"Current switch": 1, "Current input [A]": I}
)
elif op_control == "V":
# Update inputs for constant voltage
V = op["electric"][0]
operating_inputs.update({"Voltage switch": 1, "Voltage input [V]": V})
elif op_control == "W":
# Update inputs for constant power
P = op["electric"][0]
operating_inputs.update({"Power switch": 1, "Power input [W]": P})
# Update period
operating_inputs["period"] = op["period"]
# Update events
if events is None:
# make current and voltage values that won't be hit
operating_inputs.update(
{"Current cut-off [A]": -1e10, "Voltage cut-off [V]": -1e10}
)
elif events[1] in ["A", "C"]:
# update current cut-off, make voltage a value that won't be hit
if events[1] == "A":
I = events[0]
else:
# Scale C-rate with capacity to obtain current
capacity = self._parameter_values["Nominal cell capacity [A.h]"]
I = events[0] * capacity
operating_inputs.update(
{"Current cut-off [A]": I, "Voltage cut-off [V]": -1e10}
)
elif events[1] == "V":
# update voltage cut-off, make current a value that won't be hit
V = events[0]
operating_inputs.update(
{"Current cut-off [A]": -1e10, "Voltage cut-off [V]": V}
)
self._experiment_inputs.append(operating_inputs)
# Add time to the experiment times
dt = op["time"]
if dt is None:
if op_control in ["A", "C", "CCCV"]:
# Current control: max simulation time: 3 * max simulation time
# based on C-rate
dt = 3 / abs(Crate) * 3600 # seconds
if op_control == "CCCV":
dt *= 5 # 5x longer for CCCV
else:
# max simulation time: 1 day
dt = 24 * 3600 # seconds
self._experiment_times.append(dt)
# Set up model for experiment
if experiment.use_simulation_setup_type == "old":
self.set_up_model_for_experiment_old(model)
elif experiment.use_simulation_setup_type == "new":
self.set_up_model_for_experiment_new(model)
[docs] def set_up_model_for_experiment_old(self, model):
"""
Set up self.model to be able to run the experiment (old version).
In this version, a single model is created which can then be called with
different inputs for current-control, voltage-control, or power-control.
This reduces set-up time since only one model needs to be processed, but
increases simulation time since the model formulation is inefficient
"""
# Create a new model where the current density is now a variable
# To do so, we replace all instances of the current density in the
# model with a current density variable, which is obtained from the
# FunctionControl submodel
# create the FunctionControl submodel and extract variables
external_circuit_variables = pybamm.external_circuit.FunctionControl(
model.param, None
).get_fundamental_variables()
# Perform the replacement
symbol_replacement_map = {
model.variables[name]: variable
for name, variable in external_circuit_variables.items()
}
replacer = pybamm.SymbolReplacer(symbol_replacement_map)
new_model = replacer.process_model(model, inplace=False)
# Update the algebraic equation and initial conditions for FunctionControl
# This creates an algebraic equation for the current to allow current, voltage,
# or power control, together with the appropriate guess for the
# initial condition.
# External circuit submodels are always equations on the current
# The external circuit function should fix either the current, or the voltage,
# or a combination (e.g. I*V for power control)
i_cell = new_model.variables["Total current density"]
new_model.initial_conditions[i_cell] = new_model.param.current_with_time
new_model.algebraic[i_cell] = constant_current_constant_voltage_constant_power(
new_model.variables
)
# Remove upper and lower voltage cut-offs that are *not* part of the experiment
new_model.events = [
event
for event in model.events
if event.name not in ["Minimum voltage", "Maximum voltage"]
]
# add current and voltage events to the model
# current events both negative and positive to catch specification
new_model.events.extend(
[
pybamm.Event(
"Current cut-off (positive) [A] [experiment]",
new_model.variables["Current [A]"]
- abs(pybamm.InputParameter("Current cut-off [A]")),
),
pybamm.Event(
"Current cut-off (negative) [A] [experiment]",
new_model.variables["Current [A]"]
+ abs(pybamm.InputParameter("Current cut-off [A]")),
),
pybamm.Event(
"Voltage cut-off [V] [experiment]",
new_model.variables["Terminal voltage [V]"]
- pybamm.InputParameter("Voltage cut-off [V]")
/ model.param.n_cells,
),
]
)
self.model = new_model
operating_conditions = set(
x["electric"] + (x["time"],) + (x["period"],)
for x in self.experiment.operating_conditions
)
self.op_conds_to_model_and_param = {
op_cond[:2]: (new_model, self.parameter_values)
for op_cond in operating_conditions
}
[docs] def set_up_model_for_experiment_new(self, model):
"""
Set up self.model to be able to run the experiment (new version).
In this version, a new model is created for each step.
This increases set-up time since several models to be processed, but
reduces simulation time since the model formulation is efficient.
"""
self.op_conds_to_model_and_param = {}
for op_cond, op_inputs in zip(
self.experiment.operating_conditions, self._experiment_inputs
):
# Create model for this operating condition if it has not already been seen
# before
if op_cond["electric"] not in self.op_conds_to_model_and_param:
if op_inputs["Current switch"] == 1:
# Current control
# Make a new copy of the model (we will update events later))
new_model = model.new_copy()
else:
# Voltage or power control
# Create a new model where the current density is now a variable
# To do so, we replace all instances of the current density in the
# model with a current density variable, which is obtained from the
# FunctionControl submodel
# check which kind of external circuit model we need (differential
# or algebraic)
if op_inputs["CCCV switch"] == 1:
control = "differential"
else:
control = "algebraic"
# create the FunctionControl submodel and extract variables
external_circuit_variables = (
pybamm.external_circuit.FunctionControl(
model.param, None, control=control
).get_fundamental_variables()
)
# Perform the replacement
symbol_replacement_map = {
model.variables[name]: variable
for name, variable in external_circuit_variables.items()
}
replacer = pybamm.SymbolReplacer(symbol_replacement_map)
new_model = replacer.process_model(model, inplace=False)
# Update the rhs or algebraic equation and initial conditions for
# FunctionControl
# This creates a differential or algebraic equation for the current
# to allow current, voltage, or power control, together with the
# appropriate guess for the initial condition.
# External circuit submodels are always equations on the current
# The external circuit function should fix either the current, or
# the voltage, or a combination (e.g. I*V for power control)
i_cell = new_model.variables["Current density variable"]
new_model.initial_conditions[
i_cell
] = new_model.param.current_with_time
# add current events to the model
# current events both negative and positive to catch specification
new_model.events.extend(
[
pybamm.Event(
"Current cut-off (positive) [A] [experiment]",
new_model.variables["Current [A]"]
- abs(pybamm.InputParameter("Current cut-off [A]")),
),
pybamm.Event(
"Current cut-off (negative) [A] [experiment]",
new_model.variables["Current [A]"]
+ abs(pybamm.InputParameter("Current cut-off [A]")),
),
]
)
if op_inputs["Voltage switch"] == 1:
new_model.algebraic[
i_cell
] = pybamm.external_circuit.VoltageFunctionControl(
new_model.param
).constant_voltage(
new_model.variables
)
elif op_inputs["Power switch"] == 1:
new_model.algebraic[
i_cell
] = pybamm.external_circuit.PowerFunctionControl(
new_model.param
).constant_power(
new_model.variables
)
elif op_inputs["CCCV switch"] == 1:
new_model.algebraic[
i_cell
] = pybamm.external_circuit.CCCVFunctionControl(
new_model.param
).cccv(
new_model.variables
)
# add voltage events to the model
if op_inputs["Power switch"] == 1 or op_inputs["Current switch"] == 1:
new_model.events.append(
pybamm.Event(
"Voltage cut-off [V] [experiment]",
new_model.variables["Terminal voltage [V]"]
- pybamm.InputParameter("Voltage cut-off [V]")
/ model.param.n_cells,
)
)
# Keep the min and max voltages as safeguards but add some tolerances
# so that they are not triggered before the voltage limits in the
# experiment
for event in new_model.events:
if event.name == "Minimum voltage":
event._expression += 1
elif event.name == "Maximum voltage":
event._expression -= 1
# Update parameter values
new_parameter_values = self.parameter_values.copy()
if op_inputs["Current switch"] == 1:
new_parameter_values.update(
{"Current function [A]": op_inputs["Current input [A]"]}
)
elif op_inputs["Voltage switch"] == 1:
new_parameter_values.update(
{"Voltage function [V]": op_inputs["Voltage input [V]"]},
check_already_exists=False,
)
elif op_inputs["Power switch"] == 1:
new_parameter_values.update(
{"Power function [W]": op_inputs["Power input [W]"]},
check_already_exists=False,
)
elif op_inputs["CCCV switch"] == 1:
new_parameter_values.update(
{
"Current function [A]": op_inputs["Current input [A]"],
"Voltage function [V]": op_inputs["Voltage input [V]"],
},
check_already_exists=False,
)
self.op_conds_to_model_and_param[op_cond["electric"]] = (
new_model,
new_parameter_values,
)
self.model = model
[docs] def set_parameters(self):
"""
A method to set the parameters in the model and the associated geometry.
"""
if self.model_with_set_params:
return None
if self._parameter_values._dict_items == {}:
# Don't process if parameter values is empty
self._model_with_set_params = self._unprocessed_model
else:
self._model_with_set_params = self._parameter_values.process_model(
self._unprocessed_model, inplace=False
)
self._parameter_values.process_geometry(self._geometry)
self.model = self._model_with_set_params
[docs] def build(self, check_model=True):
"""
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.
Parameters
----------
check_model : bool, optional
If True, model checks are performed after discretisation (see
:meth:`pybamm.Discretisation.process_model`). Default is True.
"""
if self.built_model:
return None
elif self.model.is_discretised:
self._model_with_set_params = self.model
self._built_model = self.model
else:
self.set_parameters()
self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts)
self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods)
self._built_model = self._disc.process_model(
self._model_with_set_params, inplace=False, check_model=check_model
)
[docs] def build_for_experiment(self, check_model=True):
"""
Similar to :meth:`Simulation.build`, but for the case of simulating an
experiment, where there may be several models to build
"""
if self.op_conds_to_built_models:
return None
else:
# Can process geometry with default parameter values (only electrical
# parameters change between parameter values)
self._parameter_values.process_geometry(self._geometry)
# Only needs to set up mesh and discretisation once
self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts)
self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods)
# Process all the different models
self.op_conds_to_built_models = {}
processed_models = {}
for op_cond, (
unbuilt_model,
parameter_values,
) in self.op_conds_to_model_and_param.items():
if unbuilt_model in processed_models:
built_model = processed_models[unbuilt_model]
else:
# It's ok to modify the models in-place as they are not accessible
# from outside the simulation
model_with_set_params = parameter_values.process_model(
unbuilt_model, inplace=True
)
built_model = self._disc.process_model(
model_with_set_params, inplace=True, check_model=check_model
)
processed_models[unbuilt_model] = built_model
self.op_conds_to_built_models[op_cond] = built_model
[docs] def solve(
self,
t_eval=None,
solver=None,
check_model=True,
save_at_cycles=None,
calc_esoh=True,
starting_solution=None,
initial_soc=None,
**kwargs,
):
"""
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 : :class:`pybamm.BaseSolver`, optional
The solver to use to solve the model. If None, Simulation.solver is used
check_model : bool, optional
If True, model checks are performed after discretisation (see
:meth:`pybamm.Discretisation.process_model`). Default is True.
save_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 : :class:`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.
**kwargs
Additional key-word arguments passed to `solver.solve`.
See :meth:`pybamm.BaseSolver.solve`.
"""
# Setup
if solver is None:
solver = self.solver
if initial_soc is not None:
if self._built_initial_soc != initial_soc:
# reset
self._model_with_set_params = None
self._built_model = None
self.op_conds_to_built_models = None
c_n_init = self.parameter_values[
"Initial concentration in negative electrode [mol.m-3]"
]
c_p_init = self.parameter_values[
"Initial concentration in positive electrode [mol.m-3]"
]
param = pybamm.LithiumIonParameters()
c_n_max = self.parameter_values.evaluate(param.c_n_max)
c_p_max = self.parameter_values.evaluate(param.c_p_max)
x, y = pybamm.lithium_ion.get_initial_stoichiometries(
initial_soc, self.parameter_values
)
self.parameter_values.update(
{
"Initial concentration in negative electrode [mol.m-3]": x
* c_n_max,
"Initial concentration in positive electrode [mol.m-3]": y
* c_p_max,
}
)
# For experiments also update the following
if hasattr(self, "op_conds_to_model_and_param"):
for key, (model, param) in self.op_conds_to_model_and_param.items():
param.update(
{
"Initial concentration in negative electrode [mol.m-3]": x
* c_n_max,
"Initial concentration in positive electrode [mol.m-3]": y
* c_p_max,
}
)
# Save solved initial SOC in case we need to re-build the model
self._built_initial_soc = initial_soc
if self.operating_mode in ["without experiment", "drive cycle"]:
self.build(check_model=check_model)
if save_at_cycles is not None:
raise ValueError(
"'save_at_cycles' option can only be used if simulating an "
"Experiment "
)
if starting_solution is not None:
raise ValueError(
"starting_solution can only be provided if simulating an Experiment"
)
if self.operating_mode == "without experiment":
if t_eval is None:
raise pybamm.SolverError(
"'t_eval' must be provided if not using an experiment or "
"simulating a drive cycle. 't_eval' 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. "
"For a constant current (dis)charge the suggested 't_eval' "
"is [0, 3700/C] where C is the C-rate. "
"For example, run\n\n"
"\tsim.solve([0, 3700])\n\n"
"for a 1C discharge."
)
elif self.operating_mode == "drive cycle":
# For drive cycles (current provided as data) we perform additional
# tests on t_eval (if provided) to ensure the returned solution
# captures the input.
time_data = self._parameter_values["Current function [A]"].x[0]
# If no t_eval is provided, we use the times provided in the data.
if t_eval is None:
pybamm.logger.info("Setting t_eval as specified by the data")
t_eval = time_data
# If t_eval is provided we first check if it contains all of the
# times in the data to within 10-12. If it doesn't, we then check
# that the largest gap in t_eval is smaller than the smallest gap in
# the time data (to ensure the resolution of t_eval is fine enough).
# We only raise a warning here as users may genuinely only want
# the solution returned at some specified points.
elif (
set(np.round(time_data, 12)).issubset(set(np.round(t_eval, 12)))
) is False:
warnings.warn(
"""
t_eval does not contain all of the time points in the data
set. Note: passing t_eval = None automatically sets t_eval
to be the points in the data.
""",
pybamm.SolverWarning,
)
dt_data_min = np.min(np.diff(time_data))
dt_eval_max = np.max(np.diff(t_eval))
if dt_eval_max > dt_data_min + sys.float_info.epsilon:
warnings.warn(
"""
The largest timestep in t_eval ({}) is larger than
the smallest timestep in the data ({}). The returned
solution may not have the correct resolution to accurately
capture the input. Try refining t_eval. Alternatively,
passing t_eval = None automatically sets t_eval to be the
points in the data.
""".format(
dt_eval_max, dt_data_min
),
pybamm.SolverWarning,
)
self._solution = solver.solve(self.built_model, t_eval, **kwargs)
elif self.operating_mode == "with experiment":
self.build_for_experiment(check_model=check_model)
if t_eval is not None:
pybamm.logger.warning(
"Ignoring t_eval as solution times are specified by the experiment"
)
# Re-initialize solution, e.g. for solving multiple times with different
# inputs without having to build the simulation again
self._solution = starting_solution
# Step through all experimental conditions
inputs = kwargs.get("inputs", {})
pybamm.logger.info("Start running experiment")
timer = pybamm.Timer()
if starting_solution is None:
starting_solution_cycles = []
starting_solution_summary_variables = []
else:
starting_solution_cycles = starting_solution.cycles.copy()
starting_solution_summary_variables = (
starting_solution.all_summary_variables.copy()
)
cycle_offset = len(starting_solution_cycles)
all_cycle_solutions = starting_solution_cycles
all_summary_variables = starting_solution_summary_variables
current_solution = starting_solution
# Set up eSOH model (for summary variables)
if calc_esoh is True:
esoh_model = pybamm.lithium_ion.ElectrodeSOH()
esoh_sim = pybamm.Simulation(
esoh_model, parameter_values=self.parameter_values
)
else:
esoh_sim = None
idx = 0
num_cycles = len(self.experiment.cycle_lengths)
feasible = True # simulation will stop if experiment is infeasible
for cycle_num, cycle_length in enumerate(
self.experiment.cycle_lengths, start=1
):
pybamm.logger.notice(
f"Cycle {cycle_num+cycle_offset}/{num_cycles+cycle_offset} "
f"({timer.time()} elapsed) " + "-" * 20
)
steps = []
cycle_solution = None
# Decide whether we should save this cycle
save_this_cycle = (
# always save cycle 1
cycle_num == 1
# None: save all cycles
or save_at_cycles is None
# list: save all cycles in the list
or (
isinstance(save_at_cycles, list)
and cycle_num + cycle_offset in save_at_cycles
)
# int: save all multiples
or (
isinstance(save_at_cycles, int)
and (cycle_num + cycle_offset) % save_at_cycles == 0
)
)
for step_num in range(1, cycle_length + 1):
exp_inputs = self._experiment_inputs[idx]
dt = self._experiment_times[idx]
op_conds_str = self.experiment.operating_conditions_strings[idx]
op_conds_elec = self.experiment.operating_conditions[idx][
"electric"
]
model = self.op_conds_to_built_models[op_conds_elec]
# Use 1-indexing for printing cycle number as it is more
# human-intuitive
pybamm.logger.notice(
f"Cycle {cycle_num+cycle_offset}/{num_cycles+cycle_offset}, "
f"step {step_num}/{cycle_length}: {op_conds_str}"
)
inputs.update(exp_inputs)
kwargs["inputs"] = inputs
# Make sure we take at least 2 timesteps
npts = max(int(round(dt / exp_inputs["period"])) + 1, 2)
step_solution = solver.step(
current_solution,
model,
dt,
npts=npts,
save=False,
**kwargs,
)
steps.append(step_solution)
current_solution = step_solution
cycle_solution = cycle_solution + step_solution
# Only allow events specified by experiment
if not (
step_solution is None
or step_solution.termination == "final time"
or "[experiment]" in step_solution.termination
):
feasible = False
break
# Increment index for next iteration
idx += 1
# Break if the experiment is infeasible
if feasible is False:
pybamm.logger.warning(
"\n\n\tExperiment is infeasible: '{}' ".format(
step_solution.termination
)
+ "was triggered during '{}'. ".format(
self.experiment.operating_conditions_strings[idx]
)
+ "The returned solution only contains the first "
"{} cycles. ".format(cycle_num - 1 + cycle_offset)
+ "Try reducing the current, shortening the time interval, "
"or reducing the period.\n\n"
)
break
if save_this_cycle:
self._solution = self._solution + cycle_solution
# At the final step of the inner loop we save the cycle
cycle_solution, cycle_summary_variables = pybamm.make_cycle_solution(
steps,
esoh_sim,
save_this_cycle=save_this_cycle,
)
all_cycle_solutions.append(cycle_solution)
all_summary_variables.append(cycle_summary_variables)
# Calculate capacity_start using the first cycle
if cycle_num == 1:
if "capacity" in self.experiment.termination:
# Note capacity_start could be defined as
# self.parameter_values["Nominal cell capacity [A.h]"] instead
capacity_start = all_summary_variables[0]["Capacity [A.h]"]
value, typ = self.experiment.termination["capacity"]
if typ == "Ah":
capacity_stop = value
elif typ == "%":
capacity_stop = value / 100 * capacity_start
else:
capacity_stop = None
if capacity_stop is not None:
capacity_now = cycle_summary_variables["Capacity [A.h]"]
if np.isnan(capacity_now) or capacity_now > capacity_stop:
pybamm.logger.notice(
f"Capacity is now {capacity_now:.3f} Ah "
f"(originally {capacity_start:.3f} Ah, "
f"will stop at {capacity_stop:.3f} Ah)"
)
else:
pybamm.logger.notice(
"Stopping experiment since capacity "
f"({capacity_now:.3f} Ah) "
f"is below stopping capacity ({capacity_stop:.3f} Ah)."
)
break
if self.solution is not None and len(all_cycle_solutions) > 0:
self.solution.cycles = all_cycle_solutions
self.solution.set_summary_variables(all_summary_variables)
pybamm.logger.notice(
"Finish experiment simulation, took {}".format(timer.time())
)
# reset parameter values
if initial_soc is not None:
self.parameter_values.update(
{
"Initial concentration in negative electrode [mol.m-3]": c_n_init,
"Initial concentration in positive electrode [mol.m-3]": c_p_init,
}
)
return self.solution
[docs] def step(
self, dt, solver=None, npts=2, save=True, starting_solution=None, **kwargs
):
"""
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 : :class:`pybamm.BaseSolver`
The solver to use to solve the model.
npts : int, optional
The number of points at which the solution will be returned during
the step dt. Default is 2 (returns the solution at t0 and t0 + dt).
save : bool
Turn on to store the solution of all previous timesteps
starting_solution : :class:`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 :meth:`pybamm.BaseSolver.step`.
"""
if self.operating_mode in ["without experiment", "drive cycle"]:
self.build()
if solver is None:
solver = self.solver
if starting_solution is None:
starting_solution = self._solution
self._solution = solver.step(
starting_solution, self.built_model, dt, npts=npts, save=save, **kwargs
)
return self.solution
[docs] def plot(self, output_variables=None, quick_plot_vars=None, **kwargs):
"""
A method to quickly plot the outputs of the simulation. Creates a
:class:`pybamm.QuickPlot` object (with keyword arguments 'kwargs') and
then calls :meth:`pybamm.QuickPlot.dynamic_plot`.
Parameters
----------
output_variables: list, optional
A list of the variables to plot.
quick_plot_vars: list, optional
A list of the variables to plot. Deprecated, use output_variables instead.
**kwargs
Additional keyword arguments passed to
:meth:`pybamm.QuickPlot.dynamic_plot`.
For a list of all possible keyword arguments see :class:`pybamm.QuickPlot`.
"""
if quick_plot_vars is not None:
raise NotImplementedError(
"'quick_plot_vars' has been deprecated. Use 'output_variables' instead."
)
if self._solution is None:
raise ValueError(
"Model has not been solved, please solve the model before plotting."
)
if output_variables is None:
output_variables = self.output_variables
self.quick_plot = pybamm.dynamic_plot(
self._solution, output_variables=output_variables, **kwargs
)
return self.quick_plot
@property
def model(self):
return self._model
@model.setter
def model(self, model):
self._model = copy.copy(model)
self._model_class = model.__class__
@property
def model_with_set_params(self):
return self._model_with_set_params
@property
def built_model(self):
return self._built_model
@property
def geometry(self):
return self._geometry
@geometry.setter
def geometry(self, geometry):
self._geometry = geometry.copy()
@property
def parameter_values(self):
return self._parameter_values
@parameter_values.setter
def parameter_values(self, parameter_values):
self._parameter_values = parameter_values.copy()
@property
def submesh_types(self):
return self._submesh_types
@submesh_types.setter
def submesh_types(self, submesh_types):
self._submesh_types = submesh_types.copy()
@property
def mesh(self):
return self._mesh
@property
def var_pts(self):
return self._var_pts
@var_pts.setter
def var_pts(self, var_pts):
self._var_pts = var_pts.copy()
@property
def spatial_methods(self):
return self._spatial_methods
@spatial_methods.setter
def spatial_methods(self, spatial_methods):
self._spatial_methods = spatial_methods.copy()
@property
def solver(self):
return self._solver
@solver.setter
def solver(self, solver):
self._solver = solver.copy()
@property
def output_variables(self):
return self._output_variables
@output_variables.setter
def output_variables(self, output_variables):
self._output_variables = copy.copy(output_variables)
@property
def solution(self):
return self._solution
[docs] def specs(
self,
geometry=None,
parameter_values=None,
submesh_types=None,
var_pts=None,
spatial_methods=None,
solver=None,
output_variables=None,
C_rate=None,
):
"Deprecated method for setting specs"
raise NotImplementedError(
"The 'specs' method has been deprecated. "
"Create a new simulation for each different case instead."
)
[docs] def save(self, filename):
"""Save simulation using pickle"""
if self.model.convert_to_format == "python":
# We currently cannot save models in the 'python' format
raise NotImplementedError(
"""
Cannot save simulation if model format is python.
Set model.convert_to_format = 'casadi' instead.
"""
)
# Clear solver problem (not pickle-able, will automatically be recomputed)
if (
isinstance(self._solver, pybamm.CasadiSolver)
and self._solver.integrator_specs != {}
):
self._solver.integrator_specs = {}
if self.solution is not None:
self.solution.clear_casadi_attributes()
with open(filename, "wb") as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
def load_sim(filename):
"""Load a saved simulation"""
return pybamm.load(filename)