Merge pull request #1446 from pybamm-team/issue-1443-yang-model

Issue 1443 yang model

examples/scripts/cycling_ageing_yang.py

@@ -1,16 +1,15 @@
 import pybamm as pb
 
-pb.set_logging_level("INFO")
-options = {"SEI": "ec reaction limited", "SEI porosity change": "true"}
-param = pb.ParameterValues(chemistry=pb.parameter_sets.Ramadass2004)
-model = pb.lithium_ion.DFN(options)
+pb.set_logging_level("NOTICE")
+model = pb.lithium_ion.Yang2017()
+
 experiment = pb.Experiment(
     [
         (
             "Charge at 1 C until 4.2 V",
             "Hold at 4.2 V until C/10",
             "Rest for 5 minutes",
-            "Discharge at 2 C until 2.8 V",
+            "Discharge at 1 C until 2.8 V",
             "Rest for 5 minutes",
         )
     ]
@@ -28,22 +27,22 @@
             "Rest for 30 minutes",
             "Discharge at 1 C until 2.8 V",
         ),
-        (
-            "Charge at 1 C until 4.2 V",
-            "Hold at 4.2 V until C/20",
-            "Rest for 30 minutes",
-            "Discharge at 2 C until 2.8 V",
-        ),
-        (
-            "Charge at 1 C until 4.2 V",
-            "Hold at 4.2 V until C/20",
-            "Rest for 30 minutes",
-            "Discharge at 3 C until 2.8 V",
-        ),
+        # (
+        #     "Charge at 1 C until 4.2 V",
+        #     "Hold at 4.2 V until C/20",
+        #     "Rest for 30 minutes",
+        #     "Discharge at 2 C until 2.8 V",
+        # ),
+        # (
+        #     "Charge at 1 C until 4.2 V",
+        #     "Hold at 4.2 V until C/20",
+        #     "Rest for 30 minutes",
+        #     "Discharge at 3 C until 2.8 V",
+        # ),
     ]
 )
-sim = pb.Simulation(model, experiment=experiment, parameter_values=param)
-sim.solve(solver=pb.CasadiSolver(mode="safe", dt_max=120))
+sim = pb.Simulation(model, experiment=experiment)
+sim.solve(solver=pb.CasadiSolver(mode="safe"))
 sim.plot(
     [
         "Current [A]",

examples/scripts/print_parameters.py

@@ -0,0 +1,9 @@
+#
+# Example for printing the (dimensional and dimensionless) parameters of a parameter set
+#
+import pybamm
+
+parameters = pybamm.LithiumIonParameters()
+parameter_values = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Yang2017)
+output_file = "lithium_ion_parameters.txt"
+parameter_values.print_parameters(parameters, output_file)

pybamm/input/parameters/lead-acid/separators/agm_Sulzer2019/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Maximum porosity of separator,0.92,,
 Separator Bruggeman coefficient (electrolyte),1.5,,
-Separator Bruggeman coefficient (electrode),1.5,,
 Separator density [kg.m-3],1680, Bulk density from Gigova 2006,
 Separator specific heat capacity [J.kg-1.K-1],700, Electronics Cooling (fiberglass),
 Separator thermal conductivity [W.m-1.K-1],0.04, University Physics Sears et al. 1999 (fiberglass),

pybamm/input/parameters/lithium-ion/cells/Yang2017/parameters.csv

@@ -29,6 +29,6 @@ Negative current collector thermal conductivity [W.m-1.K-1],401,,,,
 Positive current collector thermal conductivity [W.m-1.K-1],237,,,,
 ,,,,,
 # Electrical,,,,,
-Nominal cell capacity [A.h],12.4,Yang2017,,,
-Typical current [A],12.4,,1C current,,
-Current function [A],12.4,default current function,,,
+Nominal cell capacity [A.h],2.4,Yang2017,,,
+Typical current [A],2.4,,1C current,,
+Current function [A],2.4,default current function,,,

pybamm/input/parameters/lithium-ion/lithium_platings/yang2017_Li_plating/parameters.csv

@@ -2,6 +2,6 @@ Name [units],Value,Reference,Notes
 ,,,
 ,,,
 # Lithium plating parameters,,,
-Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,
+Lithium metal partial molar volume [m3.mol-1],1.30E-05,Yang2017,6.94e-3/534
 Exchange-current density for plating [A.m-2],0.001,,
 Initial plated lithium concentration [mol.m-3],0.00E+00,,

pybamm/input/parameters/lithium-ion/seis/yang2017_sei/README.md

@@ -1,11 +1,7 @@
 # SEI parameters
 
-Some example parameters for SEI growth from the papers:
+Some example parameters for SEI growth from the paper:
 
-> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203.
-> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462.
-> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955.
-> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), 
 > Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.
 
-Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers.
+and references therein

pybamm/input/parameters/lithium-ion/seis/yang2017_sei/parameters.csv

@@ -2,22 +2,12 @@ Name [units],Value,Reference,Notes
 # Empty rows and rows starting with ‘#’ will be ignored,,,
 ,,,
 # SEI properties,,,
-Inner SEI reaction proportion,0.5,,
-Inner SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
-Outer SEI partial molar volume [m3.mol-1],9.47E-05, Safari paper,
-SEI reaction exchange current density [A.m-2],1.50E-07, Guess,
-SEI resistivity [Ohm.m],5.00E+06, Safari paper,
-Outer SEI solvent diffusivity [m2.s-1],2.50E-22, Single paper,
-Bulk solvent concentration [mol.m-3],2.64E+03, Ploehn paper,
-Ratio of inner and outer SEI exchange current densities,1, Assume same,
-Inner SEI open-circuit potential [V],0.1,,
-Outer SEI open-circuit potential [V],0.8,,
-Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper,
-Inner SEI lithium interstitial diffusivity [m2.s-1],1.00E-20, Guess,
-Lithium interstitial reference concentration [mol.m-3],15, Single paper,
-Initial inner SEI thickness [m],2.50E-09, 2.5E-9 1/2 of initial thickness in Safari paper,
-Initial outer SEI thickness [m],2.50E-09, 1/2 of initial thickness in Safari paper,
-EC initial concentration in electrolyte [mol.m-3],4.54E+03, Safari paper,
+Inner SEI partial molar volume [m3.mol-1],9.586E-05, Safari paper, 0.162/1690
+Outer SEI partial molar volume [m3.mol-1],9.586E-05, Safari paper, 0.162/1690
+SEI resistivity [Ohm.m],2.00E+06, Safari paper (1/5e-6),
+Initial inner SEI thickness [m],0,,
+Initial outer SEI thickness [m],5E-09,,
+EC initial concentration in electrolyte [mol.m-3],4.541E+03, Safari paper,
 EC diffusivity [m2.s-1],2.00E-18, adjusted parameter in Yang paper,
 SEI kinetic rate constant [m.s-1],1.00E-12, adjusted parameter in Yang paper,
 SEI open-circuit potential [V],0.4, Safari paper,

pybamm/input/parameters/lithium-ion/separators/separator_Ai2020/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Separator porosity,0.5,Ai 2020,
 Separator Bruggeman coefficient (electrolyte),1.5,Ai 2020,theoretical
-Separator Bruggeman coefficient (electrode),0,default,
 Separator density [kg.m-3],2470,default,cell parameter
 Separator specific heat capacity [J.kg-1.K-1],1080.2,default,cell parameter
 Separator thermal conductivity [W.m-1.K-1],0.334,default,cell parameter 

pybamm/input/parameters/lithium-ion/separators/separator_Chen2020/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Separator porosity,0.47,Chen 2020,
 Separator Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical
-Separator Bruggeman coefficient (electrode),1.5,default,
 Separator density [kg.m-3],397,default,
 Separator specific heat capacity [J.kg-1.K-1],700,default,
 Separator thermal conductivity [W.m-1.K-1],0.16,default,

pybamm/input/parameters/lithium-ion/separators/separator_Ecker2015/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Separator porosity,0.508,,
 Separator Bruggeman coefficient (electrolyte),1.9804586773134942, Solve for permeability factor B=0.304=eps^b,
-Separator Bruggeman coefficient (electrode),0,No Bruggeman correction to solid conductivity,
 Separator density [kg.m-3],397,default,
 Separator specific heat capacity [J.kg-1.K-1],700,default,
 Separator thermal conductivity [W.m-1.K-1],0.16,default,

pybamm/input/parameters/lithium-ion/separators/separator_Kim2011/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Separator porosity,0.4,,
 Separator Bruggeman coefficient (electrolyte),2,,
-Separator Bruggeman coefficient (electrode),2,,
 Separator density [kg.m-3],511.86798,397 * 1.28934,
 Separator specific heat capacity [J.kg-1.K-1],700,,
 Separator thermal conductivity [W.m-1.K-1],0.10672, 0.16 * 0.667,

pybamm/input/parameters/lithium-ion/separators/separator_Marquis2019/parameters.csv

@@ -3,7 +3,6 @@ Name [units],Value,Reference,Notes
 ,,,
 Separator porosity,1,,
 Separator Bruggeman coefficient (electrolyte),1.5,,
-Separator Bruggeman coefficient (electrode),1.5,,
 Separator density [kg.m-3],397,,
 Separator specific heat capacity [J.kg-1.K-1],700,,
 Separator thermal conductivity [W.m-1.K-1],0.16,,

pybamm/input/parameters/lithium-ion/separators/separator_Mohtat2020/parameters.csv

@@ -2,7 +2,6 @@ Name [units],Value,Reference,Notes
 # Empty rows and rows starting with ‘#’ will be ignored,,,
 ,,,
 Separator porosity,0.4,Peyman MPM,
-Separator Bruggeman coefficient (electrode),1.5,Peyman MPM,
 Separator Bruggeman coefficient (electrolyte),1.5,Peyman MPM,
 Separator density [kg.m-3],397,,no info from Peyman MPM
 Separator specific heat capacity [J.kg-1.K-1],700,,no info from Peyman MPM

pybamm/input/parameters/lithium-ion/separators/separator_Yang2017/README.md

@@ -0,0 +1,9 @@
+# Separator parameters
+
+Parameters for the separator from the papers
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery i. determination of parameters." Journal of the Electrochemical Society 162.9 (2015): A1836-A1848.
+
+> Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a lithium-ion battery II. Model validation." Journal of The Electrochemical Society 162.9 (2015): A1849-A1857.
+
+> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40.

pybamm/input/parameters/lithium-ion/separators/separator_Yang2017/parameters.csv

@@ -0,0 +1,8 @@
+Name [units],Value,Reference,Notes
+# Empty rows and rows starting with ‘#’ will be ignored,,,
+,,,
+Separator porosity,0.4,,
+Separator Bruggeman coefficient (electrolyte),1.5,,
+Separator density [kg.m-3],397,default,
+Separator specific heat capacity [J.kg-1.K-1],700,default,
+Separator thermal conductivity [W.m-1.K-1],0.16,default,

pybamm/models/submodels/interface/base_interface.py

@@ -480,25 +480,15 @@ def _get_standard_overpotential_variables(self, eta_r):
         elif eta_r.domain == ["current collector"]:
             eta_r = pybamm.PrimaryBroadcast(eta_r, self.domain_for_broadcast)
 
+        domain_reaction = (
+            self.domain + " electrode" + self.reaction_name + " reaction overpotential"
+        )
+
         variables = {
-            self.domain
-            + " electrode"
-            + self.reaction_name
-            + " reaction overpotential": eta_r,
-            "X-averaged "
-            + self.domain.lower()
-            + " electrode"
-            + self.reaction_name
-            + " reaction overpotential": eta_r_av,
-            self.domain
-            + " electrode"
-            + self.reaction_name
-            + " reaction overpotential [V]": eta_r * pot_scale,
-            "X-averaged "
-            + self.domain.lower()
-            + " electrode"
-            + self.reaction_name
-            + " reaction overpotential [V]": eta_r_av * pot_scale,
+            domain_reaction: eta_r,
+            "X-averaged " + domain_reaction.lower(): eta_r_av,
+            domain_reaction + " [V]": eta_r * pot_scale,
+            "X-averaged " + domain_reaction.lower() + " [V]": eta_r_av * pot_scale,
         }
 
         return variables
@@ -515,11 +505,13 @@ def _get_standard_sei_film_overpotential_variables(self, eta_sei):
         elif eta_sei.domain == ["current collector"]:
             eta_sei = pybamm.PrimaryBroadcast(eta_sei, self.domain_for_broadcast)
 
-        domain = self.domain.lower() + " electrode"
+        Domain = self.domain + " electrode"
+        domain = Domain.lower()
+
         variables = {
-            self.domain + " electrode SEI film overpotential": eta_sei,
+            Domain + " SEI film overpotential": eta_sei,
             "X-averaged " + domain + " SEI film overpotential": eta_sei_av,
-            self.domain + " electrode SEI film overpotential [V]": eta_sei * pot_scale,
+            Domain + " SEI film overpotential [V]": eta_sei * pot_scale,
             "X-averaged "
             + domain
             + " SEI film overpotential [V]": eta_sei_av * pot_scale,

pybamm/models/submodels/tortuosity/bruggeman_tortuosity.py

@@ -26,10 +26,10 @@ def get_coupled_variables(self, variables):
             )
         elif self.phase == "Electrode":
             eps_n = variables["Negative electrode active material volume fraction"]
-            eps_s = pybamm.FullBroadcast(0, "separator", "current collector")
+            tor_s = pybamm.FullBroadcast(0, "separator", "current collector")
             eps_p = variables["Positive electrode active material volume fraction"]
             tor = pybamm.Concatenation(
-                eps_n ** param.b_s_n, eps_s ** param.b_s_s, eps_p ** param.b_s_p
+                eps_n ** param.b_s_n, tor_s, eps_p ** param.b_s_p
             )
 
         variables.update(

pybamm/parameters/geometric_parameters.py

@@ -70,7 +70,6 @@ def _set_dimensional_parameters(self):
         self.b_s_n = pybamm.Parameter(
             "Negative electrode Bruggeman coefficient (electrode)"
         )
-        self.b_s_s = pybamm.Parameter("Separator Bruggeman coefficient (electrode)")
         self.b_s_p = pybamm.Parameter(
             "Positive electrode Bruggeman coefficient (electrode)"
         )

pybamm/parameters/lead_acid_parameters.py

@@ -92,7 +92,6 @@ def _set_dimensional_parameters(self):
         self.b_e_s = self.geo.b_e_s
         self.b_e_p = self.geo.b_e_p
         self.b_s_n = self.geo.b_s_n
-        self.b_s_s = self.geo.b_s_s
         self.b_s_p = self.geo.b_s_p
         self.xi_n = pybamm.Parameter("Negative electrode morphological parameter")
         self.xi_p = pybamm.Parameter("Positive electrode morphological parameter")

pybamm/parameters/lithium_ion_parameters.py

@@ -146,7 +146,6 @@ def _set_dimensional_parameters(self):
         self.b_e_s = self.geo.b_e_s
         self.b_e_p = self.geo.b_e_p
         self.b_s_n = self.geo.b_s_n
-        self.b_s_s = self.geo.b_s_s
         self.b_s_p = self.geo.b_s_p
 
         # Electrochemical reactions
@@ -209,7 +208,6 @@ def _set_dimensional_parameters(self):
         self.U_sei_dim = pybamm.Parameter("SEI open-circuit potential [V]")
 
         # Li plating parameters
-
         self.V_bar_plated_Li = pybamm.Parameter(
             "Lithium metal partial molar volume [m3.mol-1]"
         )
@@ -713,6 +711,12 @@ def _set_dimensionless_parameters(self):
         )
 
         self.v_bar = self.V_bar_outer_dimensional / self.V_bar_inner_dimensional
+        self.c_sei_scale = (
+            self.L_sei_0_dim * self.a_n_typ / self.V_bar_inner_dimensional
+        )
+        self.c_sei_outer_scale = (
+            self.L_sei_0_dim * self.a_n_typ / self.V_bar_outer_dimensional
+        )
 
         self.L_inner_0 = self.L_inner_0_dim / self.L_sei_0_dim
         self.L_outer_0 = self.L_outer_0_dim / self.L_sei_0_dim
@@ -747,6 +751,7 @@ def _set_dimensionless_parameters(self):
             )
         )
         self.beta_sei_n = self.a_n_typ * self.L_sei_0_dim * self.Gamma_SEI_n
+        self.c_sei_init = self.c_ec_0_dim / self.c_sei_outer_scale
 
         # lithium plating parameters
         self.c_Li_typ = self.c_e_typ

pybamm/parameters/parameter_sets.py

@@ -119,7 +119,7 @@
     "chemistry": "lithium-ion",
     "cell": "Yang2017",
     "negative electrode": "graphite_Yang2017",
-    "separator": "separator_Ecker2015",
+    "separator": "separator_Yang2017",
     "positive electrode": "nmc_Yang2017",
     "electrolyte": "lipf6_Ecker2015",
     "experiment": "1C_discharge_from_full_Ecker2015",

pybamm/parameters/parameter_values.py

@@ -847,6 +847,7 @@ def print_parameters(self, parameters, output_file=None):
             "print_function",
             "unicode_literals",
             "pybamm",
+            "_options",
             "constants",
             "np",
             "geo",
@@ -870,17 +871,29 @@ def print_parameters(self, parameters, output_file=None):
                     {"Current function [A]": Crate * capacity},
                     check_already_exists=False,
                 )
+
+            # Turn to regular dictionary for faster KeyErrors
+            self._dict_items = dict(self._dict_items)
+
             for name, symbol in parameters.items():
                 if not callable(symbol):
-                    proc_symbol = self.process_symbol(symbol)
+                    try:
+                        proc_symbol = self.process_symbol(symbol)
+                    except KeyError:
+                        # skip parameters that don't have a value in that parameter set
+                        proc_symbol = None
                     if not (
                         callable(proc_symbol)
+                        or proc_symbol is None
                         or proc_symbol.has_symbol_of_classes(
                             (pybamm.Concatenation, pybamm.Broadcast)
                         )
                     ):
                         evaluated_parameters[name].append(proc_symbol.evaluate(t=0))
 
+            # Turn back to FuzzyDict
+            self._dict_items = pybamm.FuzzyDict(self._dict_items)
+
         # Calculate C-dependence of the parameters based on the difference between the
         # value at 1C and the value at C / 10
         for name, values in evaluated_parameters.items():

tests/unit/test_parameters/test_lead_acid_parameters.py

@@ -13,7 +13,7 @@ def test_scipy_constants(self):
         self.assertAlmostEqual(param.R.evaluate(), 8.314, places=3)
         self.assertAlmostEqual(param.F.evaluate(), 96485, places=0)
 
-    def test_all_defined(self):
+    def test_print_parameters(self):
         parameters = pybamm.LeadAcidParameters()
         parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
         output_file = "lead_acid_parameters.txt"