Make basic models compatible with experiments

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 - Added `plot_thermal_components` to plot the contributions to the total heat generation in a battery ([#4021](https://github.com/pybamm-team/PyBaMM/pull/4021))
 - Added functions for normal probability density function (`pybamm.normal_pdf`) and cumulative distribution function (`pybamm.normal_cdf`) ([#3999](https://github.com/pybamm-team/PyBaMM/pull/3999))
+- "Basic" models are now compatible with experiments ([#3995](https://github.com/pybamm-team/PyBaMM/pull/3995))
 - Updates multiprocess `Pool` in `BaseSolver.solve()` to be constructed with context `fork`. Adds small example for multiprocess inputs. ([#3974](https://github.com/pybamm-team/PyBaMM/pull/3974))
 - Lithium plating now works on composite electrodes ([#3919](https://github.com/pybamm-team/PyBaMM/pull/3919))
 - Added lithium plating parameters to `Ecker2015` and `Ecker2015_graphite_halfcell` parameter sets ([#3919](https://github.com/pybamm-team/PyBaMM/pull/3919))

docs/source/api/models/submodels/external_circuit/discharge_throughput.rst

@@ -0,0 +1,7 @@
+Discharge and throughput variables
+==================================
+
+Calculates the discharge and throughput variables (capacity and power) for the battery.
+
+.. autoclass:: pybamm.external_circuit.DischargeThroughput
+    :members:

docs/source/api/models/submodels/external_circuit/index.rst

@@ -11,5 +11,6 @@ variable to be constant.
 .. toctree::
   :maxdepth: 1
 
+  discharge_throughput
   explicit_control_external_circuit
   function_control_external_circuit

pybamm/models/base_model.py

@@ -105,6 +105,7 @@ def __init__(self, name="Unnamed model"):
         self._boundary_conditions = {}
         self._variables_by_submodel = {}
         self._variables = pybamm.FuzzyDict({})
+        self._summary_variables = []
         self._events = []
         self._concatenated_rhs = None
         self._concatenated_algebraic = None

pybamm/models/full_battery_models/base_battery_model.py

@@ -1160,6 +1160,9 @@ def set_external_circuit_submodel(self):
                 self.param, self.options["operating mode"], self.options
             )
         self.submodels["external circuit"] = model
+        self.submodels["discharge and throughput variables"] = (
+            pybamm.external_circuit.DischargeThroughput(self.param, self.options)
+        )
 
     def set_transport_efficiency_submodels(self):
         self.submodels["electrolyte transport efficiency"] = (

pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py

@@ -181,7 +181,7 @@ def set_degradation_variables(self):
             }
         )
 
-    def set_summary_variables(self):
+    def set_default_summary_variables(self):
         """
         Sets the default summary variables.
         """

pybamm/models/full_battery_models/lithium_ion/basic_dfn.py

@@ -34,6 +34,7 @@ def __init__(self, name="Doyle-Fuller-Newman model"):
         ######################
         # Variables that depend on time only are created without a domain
         Q = pybamm.Variable("Discharge capacity [A.h]")
+
         # Variables that vary spatially are created with a domain
         c_e_n = pybamm.Variable(
             "Negative electrolyte concentration [mol.m-3]",
@@ -240,18 +241,32 @@ def __init__(self, name="Doyle-Fuller-Newman model"):
         # (Some) variables
         ######################
         voltage = pybamm.boundary_value(phi_s_p, "right")
+        num_cells = pybamm.Parameter(
+            "Number of cells connected in series to make a battery"
+        )
         # The `variables` dictionary contains all variables that might be useful for
         # visualising the solution of the model
         self.variables = {
+            "Negative particle concentration [mol.m-3]": c_s_n,
             "Negative particle surface concentration [mol.m-3]": c_s_surf_n,
             "Electrolyte concentration [mol.m-3]": c_e,
+            "Negative electrolyte concentration [mol.m-3]": c_e_n,
+            "Separator electrolyte concentration [mol.m-3]": c_e_s,
+            "Positive electrolyte concentration [mol.m-3]": c_e_p,
+            "Positive particle concentration [mol.m-3]": c_s_p,
             "Positive particle surface concentration [mol.m-3]": c_s_surf_p,
             "Current [A]": I,
+            "Current variable [A]": I,  # for compatibility with pybamm.Experiment
             "Negative electrode potential [V]": phi_s_n,
             "Electrolyte potential [V]": phi_e,
+            "Negative electrolyte potential [V]": phi_e_n,
+            "Separator electrolyte potential [V]": phi_e_s,
+            "Positive electrolyte potential [V]": phi_e_p,
             "Positive electrode potential [V]": phi_s_p,
             "Voltage [V]": voltage,
+            "Battery voltage [V]": voltage * num_cells,
             "Time [s]": pybamm.t,
+            "Discharge capacity [A.h]": Q,
         }
         # Events specify points at which a solution should terminate
         self.events += [

pybamm/models/full_battery_models/lithium_ion/basic_dfn_composite.py

@@ -341,18 +341,40 @@
         ocp_av_p = pybamm.x_average(ocp_p)
         a_j_n_p1_av = pybamm.x_average(a_j_n_p1)
         a_j_n_p2_av = pybamm.x_average(a_j_n_p2)
+        num_cells = pybamm.Parameter(
+            "Number of cells connected in series to make a battery"
+        )
         # The `variables` dictionary contains all variables that might be useful for
         # visualising the solution of the model
         self.variables = {
             "Negative primary particle concentration [mol.m-3]": c_s_n_p1,
             "Negative secondary particle concentration [mol.m-3]": c_s_n_p2,
+            "R-averaged negative primary particle concentration "
+            "[mol.m-3]": c_s_rav_n_p1,
+            "R-averaged negative secondary particle concentration "
+            "[mol.m-3]": c_s_rav_n_p2,
+            "Average negative primary particle concentration "
+            "[mol.m-3]": c_s_xrav_n_p1,
+            "Average negative secondary particle concentration "
+            "[mol.m-3]": c_s_xrav_n_p2,
+            "Positive particle concentration [mol.m-3]": c_s_p,
+            "Average positive particle concentration [mol.m-3]": c_s_xrav_p,
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Negative electrolyte concentration [mol.m-3]": c_e_n,
+            "Separator electrolyte concentration [mol.m-3]": c_e_s,
+            "Positive electrolyte concentration [mol.m-3]": c_e_p,
             "Negative electrode potential [V]": phi_s_n,
-            "Electrolyte potential [V]": phi_e,
             "Positive electrode potential [V]": phi_s_p,
+            "Electrolyte potential [V]": phi_e,
+            "Negative electrolyte potential [V]": phi_e_n,
+            "Separator electrolyte potential [V]": phi_e_s,
+            "Positive electrolyte potential [V]": phi_e_p,
             "Current [A]": I,
+            "Current variable [A]": I,  # for compatibility with pybamm.Experiment
             "Discharge capacity [A.h]": Q,
             "Time [s]": pybamm.t,
             "Voltage [V]": voltage,
+            "Battery voltage [V]": voltage * num_cells,
             "Negative electrode primary open-circuit potential [V]": ocp_n_p1,
             "Negative electrode secondary open-circuit potential [V]": ocp_n_p2,
             "X-averaged negative electrode primary open-circuit potential "
@@ -361,15 +383,6 @@
             "[V]": ocp_av_n_p2,
             "Positive electrode open-circuit potential [V]": ocp_p,
             "X-averaged positive electrode open-circuit potential [V]": ocp_av_p,
-            "R-averaged negative primary particle concentration "
-            "[mol.m-3]": c_s_rav_n_p1,
-            "R-averaged negative secondary particle concentration "
-            "[mol.m-3]": c_s_rav_n_p2,
-            "Average negative primary particle concentration "
-            "[mol.m-3]": c_s_xrav_n_p1,
-            "Average negative secondary particle concentration "
-            "[mol.m-3]": c_s_xrav_n_p2,
-            "Average positive particle concentration [mol.m-3]": c_s_xrav_p,
             "Negative electrode primary interfacial current density [A.m-2]": j_n_p1,
             "Negative electrode secondary interfacial current density [A.m-2]": j_n_p2,
             "X-averaged negative electrode primary interfacial current density "
@@ -385,7 +398,12 @@
             "X-averaged negative electrode secondary volumetric "
             "interfacial current density [A.m-3]": a_j_n_p2_av,
         }
+        # Events specify points at which a solution should terminate
         self.events += [
             pybamm.Event("Minimum voltage [V]", voltage - param.voltage_low_cut),
             pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - voltage),
         ]
+
+    @property
+    def default_parameter_values(self):
+        return pybamm.ParameterValues("Chen2020_composite")

pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py

@@ -30,6 +30,7 @@ class BasicDFNHalfCell(BaseModel):
     """
 
     def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"):
+        options = {"working electrode": "positive"}
         super().__init__(options, name)
         pybamm.citations.register("Marquis2019")
         # `param` is a class containing all the relevant parameters and functions for
@@ -244,6 +245,10 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"):
         vdrop_cell = pybamm.boundary_value(phi_s_w, "right") - ref_potential
         vdrop_Li = -eta_Li - delta_phis_Li
         voltage = vdrop_cell + vdrop_Li
+        num_cells = pybamm.Parameter(
+            "Number of cells connected in series to make a battery"
+        )
+
         c_e_total = pybamm.x_average(eps * c_e)
         c_s_surf_w_av = pybamm.x_average(c_s_surf_w)
 
@@ -280,22 +285,29 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"):
         # visualising the solution of the model
         self.variables = {
             "Time [s]": pybamm.t,
+            "Discharge capacity [A.h]": Q,
             "Positive particle surface concentration [mol.m-3]": c_s_surf_w,
             "X-averaged positive particle surface concentration "
             "[mol.m-3]": c_s_surf_w_av,
             "Positive particle concentration [mol.m-3]": c_s_w,
             "Total lithium in positive electrode [mol]": c_s_vol_av * L_w * param.A_cc,
             "Electrolyte concentration [mol.m-3]": c_e,
+            "Separator electrolyte concentration [mol.m-3]": c_e_s,
+            "Positive electrolyte concentration [mol.m-3]": c_e_w,
             "Total lithium in electrolyte [mol]": c_e_total * param.L_x * param.A_cc,
             "Current [A]": I,
+            "Current variable [A]": I,  # for compatibility with pybamm.Experiment
             "Current density [A.m-2]": i_cell,
             "Positive electrode potential [V]": phi_s_w,
             "Positive electrode open-circuit potential [V]": U_w(sto_surf_w, T),
             "Electrolyte potential [V]": phi_e,
+            "Separator electrolyte potential [V]": phi_e_s,
+            "Positive electrolyte potential [V]": phi_e_w,
             "Voltage drop in the cell [V]": vdrop_cell,
             "Negative electrode exchange current density [A.m-2]": j_Li,
             "Negative electrode reaction overpotential [V]": eta_Li,
             "Negative electrode potential drop [V]": delta_phis_Li,
             "Voltage [V]": voltage,
+            "Battery voltage [V]": voltage * num_cells,
             "Instantaneous power [W.m-2]": i_cell * voltage,
         }

pybamm/models/full_battery_models/lithium_ion/basic_spm.py

@@ -139,26 +139,33 @@ def __init__(self, name="Single Particle Model"):
         phi_e = -eta_n - param.n.prim.U(sto_surf_n, T)
         phi_s_p = eta_p + phi_e + param.p.prim.U(sto_surf_p, T)
         V = phi_s_p
+        num_cells = pybamm.Parameter(
+            "Number of cells connected in series to make a battery"
+        )
 
         whole_cell = ["negative electrode", "separator", "positive electrode"]
         # The `variables` dictionary contains all variables that might be useful for
         # visualising the solution of the model
         # Primary broadcasts are used to broadcast scalar quantities across a domain
         # into a vector of the right shape, for multiplying with other vectors
         self.variables = {
+            "Time [s]": pybamm.t,
             "Discharge capacity [A.h]": Q,
+            "X-averaged negative particle concentration [mol.m-3]": c_s_n,
             "Negative particle surface "
             "concentration [mol.m-3]": pybamm.PrimaryBroadcast(
                 c_s_surf_n, "negative electrode"
             ),
             "Electrolyte concentration [mol.m-3]": pybamm.PrimaryBroadcast(
                 param.c_e_init_av, whole_cell
             ),
+            "X-averaged positive particle concentration [mol.m-3]": c_s_p,
             "Positive particle surface "
             "concentration [mol.m-3]": pybamm.PrimaryBroadcast(
                 c_s_surf_p, "positive electrode"
             ),
             "Current [A]": I,
+            "Current variable [A]": I,  # for compatibility with pybamm.Experiment
             "Negative electrode potential [V]": pybamm.PrimaryBroadcast(
                 phi_s_n, "negative electrode"
             ),
@@ -167,7 +174,9 @@ def __init__(self, name="Single Particle Model"):
                 phi_s_p, "positive electrode"
             ),
             "Voltage [V]": V,
+            "Battery voltage [V]": V * num_cells,
         }
+        # Events specify points at which a solution should terminate
         self.events += [
             pybamm.Event("Minimum voltage [V]", V - param.voltage_low_cut),
             pybamm.Event("Maximum voltage [V]", param.voltage_high_cut - V),

pybamm/models/full_battery_models/lithium_ion/dfn.py

@@ -121,3 +121,6 @@ def set_electrolyte_potential_submodel(self):
             self.submodels[f"{domain} surface potential difference"] = surf_model(
                 self.param, domain, self.options
             )
+
+    def set_summary_variables(self):
+        self.set_default_summary_variables()

pybamm/models/full_battery_models/lithium_ion/spm.py

@@ -160,3 +160,6 @@ def set_electrolyte_potential_submodel(self):
             self.submodels[f"{domain} surface potential difference"] = surf_model(
                 self.param, domain, options=self.options
             )
+
+    def set_summary_variables(self):
+        self.set_default_summary_variables()

pybamm/models/submodels/external_circuit/__init__.py

@@ -1,4 +1,5 @@
 from .base_external_circuit import BaseModel
+from .discharge_throughput import DischargeThroughput
 from .explicit_control_external_circuit import (
     ExplicitCurrentControl,
     ExplicitPowerControl,

pybamm/models/submodels/external_circuit/base_external_circuit.py

@@ -9,58 +9,3 @@ class BaseModel(pybamm.BaseSubModel):
 
     def __init__(self, param, options):
         super().__init__(param, options=options)
-
-    def get_fundamental_variables(self):
-        Q_Ah = pybamm.Variable("Discharge capacity [A.h]")
-        Q_Ah.print_name = "Q_Ah"
-        # Throughput capacity (cumulative)
-        Qt_Ah = pybamm.Variable("Throughput capacity [A.h]")
-        Qt_Ah.print_name = "Qt_Ah"
-
-        variables = {
-            "Discharge capacity [A.h]": Q_Ah,
-            "Throughput capacity [A.h]": Qt_Ah,
-        }
-        if self.options["calculate discharge energy"] == "true":
-            Q_Wh = pybamm.Variable("Discharge energy [W.h]")
-            # Throughput energy (cumulative)
-            Qt_Wh = pybamm.Variable("Throughput energy [W.h]")
-            variables.update(
-                {
-                    "Discharge energy [W.h]": Q_Wh,
-                    "Throughput energy [W.h]": Qt_Wh,
-                }
-            )
-        else:
-            variables.update(
-                {
-                    "Discharge energy [W.h]": pybamm.Scalar(0),
-                    "Throughput energy [W.h]": pybamm.Scalar(0),
-                }
-            )
-        return variables
-
-    def set_initial_conditions(self, variables):
-        Q_Ah = variables["Discharge capacity [A.h]"]
-        Qt_Ah = variables["Throughput capacity [A.h]"]
-        self.initial_conditions[Q_Ah] = pybamm.Scalar(0)
-        self.initial_conditions[Qt_Ah] = pybamm.Scalar(0)
-        if self.options["calculate discharge energy"] == "true":
-            Q_Wh = variables["Discharge energy [W.h]"]
-            Qt_Wh = variables["Throughput energy [W.h]"]
-            self.initial_conditions[Q_Wh] = pybamm.Scalar(0)
-            self.initial_conditions[Qt_Wh] = pybamm.Scalar(0)
-
-    def set_rhs(self, variables):
-        # ODEs for discharge capacity and throughput capacity
-        Q_Ah = variables["Discharge capacity [A.h]"]
-        Qt_Ah = variables["Throughput capacity [A.h]"]
-        I = variables["Current [A]"]
-        self.rhs[Q_Ah] = I / 3600  # Returns to zero after a complete cycle
-        self.rhs[Qt_Ah] = abs(I) / 3600  # Increases with each cycle
-        if self.options["calculate discharge energy"] == "true":
-            Q_Wh = variables["Discharge energy [W.h]"]
-            Qt_Wh = variables["Throughput energy [W.h]"]
-            V = variables["Voltage [V]"]
-            self.rhs[Q_Wh] = I * V / 3600  # Returns to zero after a complete cycle
-            self.rhs[Qt_Wh] = abs(I * V) / 3600  # Increases with each cycle

pybamm/models/submodels/external_circuit/discharge_throughput.py

@@ -0,0 +1,64 @@
+#
+# Variables related to discharge and throughput capacity and energy
+#
+import pybamm
+from .base_external_circuit import BaseModel
+
+
+class DischargeThroughput(BaseModel):
+    """Model calculate discharge and throughput capacity and energy."""
+
+    def get_fundamental_variables(self):
+        Q_Ah = pybamm.Variable("Discharge capacity [A.h]")
+        Q_Ah.print_name = "Q_Ah"
+        # Throughput capacity (cumulative)
+        Qt_Ah = pybamm.Variable("Throughput capacity [A.h]")
+        Qt_Ah.print_name = "Qt_Ah"
+
+        variables = {
+            "Discharge capacity [A.h]": Q_Ah,
+            "Throughput capacity [A.h]": Qt_Ah,
+        }
+        if self.options["calculate discharge energy"] == "true":
+            Q_Wh = pybamm.Variable("Discharge energy [W.h]")
+            # Throughput energy (cumulative)
+            Qt_Wh = pybamm.Variable("Throughput energy [W.h]")
+            variables.update(
+                {
+                    "Discharge energy [W.h]": Q_Wh,
+                    "Throughput energy [W.h]": Qt_Wh,
+                }
+            )
+        else:
+            variables.update(
+                {
+                    "Discharge energy [W.h]": pybamm.Scalar(0),
+                    "Throughput energy [W.h]": pybamm.Scalar(0),
+                }
+            )
+        return variables
+
+    def set_initial_conditions(self, variables):
+        Q_Ah = variables["Discharge capacity [A.h]"]
+        Qt_Ah = variables["Throughput capacity [A.h]"]
+        self.initial_conditions[Q_Ah] = pybamm.Scalar(0)
+        self.initial_conditions[Qt_Ah] = pybamm.Scalar(0)
+        if self.options["calculate discharge energy"] == "true":
+            Q_Wh = variables["Discharge energy [W.h]"]
+            Qt_Wh = variables["Throughput energy [W.h]"]
+            self.initial_conditions[Q_Wh] = pybamm.Scalar(0)
+            self.initial_conditions[Qt_Wh] = pybamm.Scalar(0)
+
+    def set_rhs(self, variables):
+        # ODEs for discharge capacity and throughput capacity
+        Q_Ah = variables["Discharge capacity [A.h]"]
+        Qt_Ah = variables["Throughput capacity [A.h]"]
+        I = variables["Current [A]"]
+        self.rhs[Q_Ah] = I / 3600  # Returns to zero after a complete cycle
+        self.rhs[Qt_Ah] = abs(I) / 3600  # Increases with each cycle
+        if self.options["calculate discharge energy"] == "true":
+            Q_Wh = variables["Discharge energy [W.h]"]
+            Qt_Wh = variables["Throughput energy [W.h]"]
+            V = variables["Voltage [V]"]
+            self.rhs[Q_Wh] = I * V / 3600  # Returns to zero after a complete cycle
+            self.rhs[Qt_Wh] = abs(I * V) / 3600  # Increases with each cycle