From 0e70d3e94819c7109aa78c4d87e0029a369cc7b9 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 27 Aug 2024 10:51:58 +0100 Subject: [PATCH 01/20] Started modifying porosity change submodel to work with phases --- .../porosity/reaction_driven_porosity.py | 71 ++++++++++++------- 1 file changed, 47 insertions(+), 24 deletions(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index fc69d0f1fd..3dceb14eba 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -23,38 +23,61 @@ def __init__(self, param, options, x_average): self.x_average = x_average def get_coupled_variables(self, variables): + param = self.param eps_dict = {} for domain in self.options.whole_cell_domains: + delta_eps_k = 0 if domain == "separator": - delta_eps_k = 0 # separator porosity does not change + pass # separator porosity does not change else: - Domain = domain.split()[0].capitalize() - L_sei_k = variables[f"{Domain} total SEI thickness [m]"] - if Domain == "Negative": - L_sei_0 = self.param.n.prim.L_inner_0 + self.param.n.prim.L_outer_0 - elif Domain == "Positive": - L_sei_0 = self.param.p.prim.L_inner_0 + self.param.p.prim.L_outer_0 - L_pl_k = variables[f"{Domain} lithium plating thickness [m]"] - L_dead_k = variables[f"{Domain} dead lithium thickness [m]"] - L_sei_cr_k = variables[f"{Domain} total SEI on cracks thickness [m]"] + dom = domain.split()[0] + Domain = dom.capitalize() roughness_k = variables[f"{Domain} electrode roughness ratio"] + phases = self.options.phases[dom] + for phase in phases: + L_sei_k = variables[f"{Domain} total {phase} SEI thickness [m]"] + if Domain == "Negative": + if phase == "secondary": + L_sei_0 = ( + param.n.sec.L_inner_0 + param.n.sec.L_outer_0 + ) + else: + L_sei_0 = ( + param.n.prim.L_inner_0 + param.n.prim.L_outer_0 + ) + elif Domain == "Positive": + if phase == "secondary": + L_sei_0 = ( + param.p.sec.L_inner_0 + param.p.sec.L_outer_0 + ) + else: + L_sei_0 = ( + param.p.prim.L_inner_0 + param.p.prim.L_outer_0 + ) + L_pl_k = variables[ + f"{Domain} {phase} lithium plating thickness [m]" + ] + L_dead_k = variables[f"{Domain} {phase} dead lithium thickness [m]"] + L_sei_cr_k = variables[ + f"{Domain} total {phase} SEI on cracks thickness [m]" + ] - L_tot = ( - (L_sei_k - L_sei_0) - + L_pl_k - + L_dead_k - + L_sei_cr_k * (roughness_k - 1) - ) + L_tot = ( + (L_sei_k - L_sei_0) + + L_pl_k + + L_dead_k + + L_sei_cr_k * (roughness_k - 1) + ) - a_k = variables[ - f"{Domain} electrode surface area to volume ratio [m-1]" - ] + a_k = variables[ + f"{Domain} electrode {phase} surface area to volume ratio [m-1]" + ] - # This assumes a thin film so curvature effects are neglected. - # They could be included (e.g. for a sphere it is - # a_n * (L_tot + L_tot ** 2 / R_n + L_tot ** # 3 / (3 * R_n ** 2))) - # but it is not clear if it is relevant or not. - delta_eps_k = -a_k * L_tot + # This assumes a thin film so curvature effects are neglected. + # They could be included (e.g. for a sphere it is + # a_n * (L_tot + L_tot ** 2 / R_n + L_tot ** # 3 / (3 * R_n ** 2))) + # but it is not clear if it is relevant or not. + delta_eps_k += -a_k * L_tot domain_param = self.param.domain_params[domain.split()[0]] eps_k = domain_param.epsilon_init + delta_eps_k From b8dd0138d134a9f372276b257ae6f76b626fafa3 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 27 Aug 2024 11:03:22 +0100 Subject: [PATCH 02/20] Porosity now works on composite electrode --- .../models/submodels/porosity/reaction_driven_porosity.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 3dceb14eba..8a9d195878 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -33,7 +33,11 @@ def get_coupled_variables(self, variables): dom = domain.split()[0] Domain = dom.capitalize() roughness_k = variables[f"{Domain} electrode roughness ratio"] - phases = self.options.phases[dom] + phases_option = getattr(self.options, dom)["particle phases"] + if phases_option == "1": + phases = "" + else: + phases = self.options.phases[dom] for phase in phases: L_sei_k = variables[f"{Domain} total {phase} SEI thickness [m]"] if Domain == "Negative": From e8fc8f47764693a7f98dcee898e7bfcd47ccdbd5 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 27 Aug 2024 11:17:23 +0100 Subject: [PATCH 03/20] Now works for non-composite electrode as well! --- .../porosity/reaction_driven_porosity.py | 21 +++++++++++-------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 8a9d195878..700d6dc0c4 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -34,12 +34,13 @@ def get_coupled_variables(self, variables): Domain = dom.capitalize() roughness_k = variables[f"{Domain} electrode roughness ratio"] phases_option = getattr(self.options, dom)["particle phases"] - if phases_option == "1": - phases = "" - else: - phases = self.options.phases[dom] + phases = self.options.phases[dom] for phase in phases: - L_sei_k = variables[f"{Domain} total {phase} SEI thickness [m]"] + if phases_option == "1" and phase == "primary": + phase_name = "" + else: + phase_name = phase + " " + L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"] if Domain == "Negative": if phase == "secondary": L_sei_0 = ( @@ -59,11 +60,13 @@ def get_coupled_variables(self, variables): param.p.prim.L_inner_0 + param.p.prim.L_outer_0 ) L_pl_k = variables[ - f"{Domain} {phase} lithium plating thickness [m]" + f"{Domain} {phase_name}lithium plating thickness [m]" + ] + L_dead_k = variables[ + f"{Domain} {phase_name}dead lithium thickness [m]" ] - L_dead_k = variables[f"{Domain} {phase} dead lithium thickness [m]"] L_sei_cr_k = variables[ - f"{Domain} total {phase} SEI on cracks thickness [m]" + f"{Domain} total {phase_name}SEI on cracks thickness [m]" ] L_tot = ( @@ -74,7 +77,7 @@ def get_coupled_variables(self, variables): ) a_k = variables[ - f"{Domain} electrode {phase} surface area to volume ratio [m-1]" + f"{Domain} electrode {phase_name}surface area to volume ratio [m-1]" ] # This assumes a thin film so curvature effects are neglected. From b76a2ca33d2954f4e3990965b857ce59950c68d9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 5 Sep 2024 11:04:04 +0000 Subject: [PATCH 04/20] style: pre-commit fixes --- .../porosity/reaction_driven_porosity.py | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 700d6dc0c4..b9bb898bcb 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -43,22 +43,14 @@ def get_coupled_variables(self, variables): L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"] if Domain == "Negative": if phase == "secondary": - L_sei_0 = ( - param.n.sec.L_inner_0 + param.n.sec.L_outer_0 - ) + L_sei_0 = param.n.sec.L_inner_0 + param.n.sec.L_outer_0 else: - L_sei_0 = ( - param.n.prim.L_inner_0 + param.n.prim.L_outer_0 - ) + L_sei_0 = param.n.prim.L_inner_0 + param.n.prim.L_outer_0 elif Domain == "Positive": if phase == "secondary": - L_sei_0 = ( - param.p.sec.L_inner_0 + param.p.sec.L_outer_0 - ) + L_sei_0 = param.p.sec.L_inner_0 + param.p.sec.L_outer_0 else: - L_sei_0 = ( - param.p.prim.L_inner_0 + param.p.prim.L_outer_0 - ) + L_sei_0 = param.p.prim.L_inner_0 + param.p.prim.L_outer_0 L_pl_k = variables[ f"{Domain} {phase_name}lithium plating thickness [m]" ] From 6b870412c2cf9a91488b0fee4af1e92a28f70c48 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Thu, 5 Sep 2024 12:05:48 +0100 Subject: [PATCH 05/20] changelog --- CHANGELOG.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5203229bd3..4491e93b4c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # [Unreleased](https://github.com/pybamm-team/PyBaMM/) +## Features + +- Porosity change now works for composite electrode ([#4417](https://github.com/pybamm-team/PyBaMM/pull/4417)) + ## Optimizations - Removed the `start_step_offset` setting and disabled minimum `dt` warnings for drive cycles with the (`IDAKLUSolver`). ([#4416](https://github.com/pybamm-team/PyBaMM/pull/4416)) From 4758ab88f6ef17c53e094db12a591cba2ae00c48 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Thu, 5 Sep 2024 12:08:44 +0100 Subject: [PATCH 06/20] style fix --- .../models/submodels/porosity/reaction_driven_porosity.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index b9bb898bcb..2998330c45 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -69,7 +69,8 @@ def get_coupled_variables(self, variables): ) a_k = variables[ - f"{Domain} electrode {phase_name}surface area to volume ratio [m-1]" + f"{Domain} electrode {phase_name}" + "surface area to volume ratio [m-1]" ] # This assumes a thin film so curvature effects are neglected. From adcb65284fe57597e81d8e623987340489568bec Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Thu, 5 Sep 2024 12:52:53 +0100 Subject: [PATCH 07/20] Updated tests --- .../test_lithium_ion/base_lithium_ion_tests.py | 1 + .../test_lithium_ion/base_lithium_ion_tests.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 60e8dfb819..7e7bed7ea5 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -333,6 +333,7 @@ def test_composite_graphite_silicon_sei(self): "particle phases": ("2", "1"), "open-circuit potential": (("single", "current sigmoid"), "single"), "SEI": "ec reaction limited", + "SEI porosity change": "true", } parameter_values = pybamm.ParameterValues("Chen2020_composite") name = "Negative electrode active material volume fraction" diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 9c093c0c65..2772fc56a1 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -569,6 +569,7 @@ def test_well_posed_composite_different_degradation(self): options = { "particle phases": ("2", "1"), "SEI": ("ec reaction limited", "none"), + "SEI porosity change": "true", "lithium plating": ("reversible", "none"), "open-circuit potential": (("current sigmoid", "single"), "single"), } @@ -577,6 +578,7 @@ def test_well_posed_composite_different_degradation(self): options = { "particle phases": ("2", "1"), "SEI": (("ec reaction limited", "solvent-diffusion limited"), "none"), + "SEI porosity change": "true", "lithium plating": (("reversible", "irreversible"), "none"), "open-circuit potential": (("current sigmoid", "single"), "single"), } From 1985fb169700a88d148f68735fccc69e2f8fd681 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Sat, 7 Sep 2024 22:52:39 +0100 Subject: [PATCH 08/20] Changed how pref is handled --- .../porosity/reaction_driven_porosity.py | 25 ++++++++++--------- 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 2998330c45..17e93879f0 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -23,7 +23,6 @@ def __init__(self, param, options, x_average): self.x_average = x_average def get_coupled_variables(self, variables): - param = self.param eps_dict = {} for domain in self.options.whole_cell_domains: delta_eps_k = 0 @@ -36,21 +35,23 @@ def get_coupled_variables(self, variables): phases_option = getattr(self.options, dom)["particle phases"] phases = self.options.phases[dom] for phase in phases: - if phases_option == "1" and phase == "primary": + if self.options["particle phases"] == "1": phase_name = "" + pref = "" + elif phases_option == "1" and phase == "primary": + phase_name = "" + pref = "Primary: " else: phase_name = phase + " " + pref = phase.capitalize() + ": " L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"] - if Domain == "Negative": - if phase == "secondary": - L_sei_0 = param.n.sec.L_inner_0 + param.n.sec.L_outer_0 - else: - L_sei_0 = param.n.prim.L_inner_0 + param.n.prim.L_outer_0 - elif Domain == "Positive": - if phase == "secondary": - L_sei_0 = param.p.sec.L_inner_0 + param.p.sec.L_outer_0 - else: - L_sei_0 = param.p.prim.L_inner_0 + param.p.prim.L_outer_0 + L_inner_0 = pybamm.Parameter( + f"{pref}Initial inner SEI thickness [m]" + ) + L_outer_0 = pybamm.Parameter( + f"{pref}Initial outer SEI thickness [m]" + ) + L_sei_0 = L_inner_0 + L_outer_0 L_pl_k = variables[ f"{Domain} {phase_name}lithium plating thickness [m]" ] From 709d634a62681c11ed4b9be6fe61483f63e2a080 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Sat, 7 Sep 2024 23:12:36 +0100 Subject: [PATCH 09/20] Added comments to the new if statements --- .../models/submodels/porosity/reaction_driven_porosity.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index c7328936c2..00c4251a72 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -36,12 +36,15 @@ def get_coupled_variables(self, variables): phases = self.options.phases[dom] for phase in phases: if self.options["particle phases"] == "1": + # both electrodes have one phase phase_name = "" pref = "" elif phases_option == "1" and phase == "primary": + # `domain` has one phase phase_name = "" pref = "Primary: " else: + # `domain` has more than one phase phase_name = phase + " " pref = phase.capitalize() + ": " L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"] From f0a5a7935227beeb6cc466f09cc55a391ff6901c Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Mon, 9 Sep 2024 17:28:00 +0100 Subject: [PATCH 10/20] Why did I not think of this before?! --- .../porosity/reaction_driven_porosity.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 00c4251a72..d2a01b750e 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -32,29 +32,29 @@ def get_coupled_variables(self, variables): dom = domain.split()[0] Domain = dom.capitalize() roughness_k = variables[f"{Domain} electrode roughness ratio"] + SEI_option = getattr(self.options, dom)["SEI"] phases_option = getattr(self.options, dom)["particle phases"] phases = self.options.phases[dom] for phase in phases: - if self.options["particle phases"] == "1": - # both electrodes have one phase - phase_name = "" - pref = "" - elif phases_option == "1" and phase == "primary": + if phases_option == "1" and phase == "primary": # `domain` has one phase phase_name = "" - pref = "Primary: " + pref = "" else: # `domain` has more than one phase phase_name = phase + " " pref = phase.capitalize() + ": " L_sei_k = variables[f"{Domain} total {phase_name}SEI thickness [m]"] - L_inner_0 = pybamm.Parameter( - f"{pref}Initial inner SEI thickness [m]" - ) - L_outer_0 = pybamm.Parameter( - f"{pref}Initial outer SEI thickness [m]" - ) - L_sei_0 = L_inner_0 + L_outer_0 + if SEI_option == "none": + L_sei_0 = pybamm.Scalar(0) + else: + L_inner_0 = pybamm.Parameter( + f"{pref}Initial inner SEI thickness [m]" + ) + L_outer_0 = pybamm.Parameter( + f"{pref}Initial outer SEI thickness [m]" + ) + L_sei_0 = L_inner_0 + L_outer_0 L_pl_k = variables[ f"{Domain} {phase_name}lithium plating thickness [m]" ] From afb6f115be2dc8277a37b265ae4a83af13b2d4ad Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 10 Sep 2024 16:07:51 +0100 Subject: [PATCH 11/20] All SEI and plating parameters now have domains --- .../input/parameters/lithium_ion/Ai2020.py | 44 ++++----- .../input/parameters/lithium_ion/Chen2020.py | 40 ++++---- .../lithium_ion/Chen2020_composite.py | 85 +++++++++-------- .../input/parameters/lithium_ion/Ecker2015.py | 70 +++++++------- .../Ecker2015_graphite_halfcell.py | 90 +++++++++++------- .../parameters/lithium_ion/Marquis2019.py | 41 ++++---- .../parameters/lithium_ion/Mohtat2020.py | 48 +++++----- .../parameters/lithium_ion/NCA_Kim2011.py | 40 ++++---- .../input/parameters/lithium_ion/OKane2022.py | 74 +++++++-------- .../OKane2022_graphite_SiOx_halfcell.py | 95 +++++++++++-------- .../parameters/lithium_ion/Ramadass2004.py | 42 ++++---- .../input/parameters/lithium_ion/Xu2019.py | 41 ++++---- .../interface/lithium_plating/plating.py | 2 +- .../porosity/reaction_driven_porosity.py | 4 +- .../parameters/lithium_ion_parameters.py | 93 +++++++++++------- 15 files changed, 437 insertions(+), 372 deletions(-) diff --git a/src/pybamm/input/parameters/lithium_ion/Ai2020.py b/src/pybamm/input/parameters/lithium_ion/Ai2020.py index f578d59fa5..d80f20cee2 100644 --- a/src/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/src/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -533,28 +533,28 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "Initial inner SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero - "Initial outer SEI on cracks thickness [m]": 2.5e-13, # avoid division by zero - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Initial negative inner SEI on cracks thickness [m]": 2.5e-13, + "Initial negative outer SEI on cracks thickness [m]": 2.5e-13, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020.py b/src/pybamm/input/parameters/lithium_ion/Chen2020.py index b3655513a1..1bd0fb279f 100644 --- a/src/pybamm/input/parameters/lithium_ion/Chen2020.py +++ b/src/pybamm/input/parameters/lithium_ion/Chen2020.py @@ -226,26 +226,26 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index 69b622a7c5..5ea1d38190 100644 --- a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -327,46 +327,51 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Primary: Ratio of lithium moles to SEI moles": 2.0, - "Primary: Inner SEI reaction proportion": 0.5, - "Primary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Primary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Primary: SEI reaction exchange current density [A.m-2]": 1.5e-07, - "Primary: SEI resistivity [Ohm.m]": 200000.0, - "Primary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Primary: Bulk solvent concentration [mol.m-3]": 2636.0, - "Primary: Inner SEI open-circuit potential [V]": 0.1, - "Primary: Outer SEI open-circuit potential [V]": 0.8, - "Primary: Inner SEI electron conductivity [S.m-1]": 8.95e-14, - "Primary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Primary: Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Primary: Initial inner SEI thickness [m]": 2.5e-09, - "Primary: Initial outer SEI thickness [m]": 2.5e-09, - "Primary: EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "Primary: EC diffusivity [m2.s-1]": 2e-18, - "Primary: SEI kinetic rate constant [m.s-1]": 1e-12, - "Primary: SEI open-circuit potential [V]": 0.4, - "Primary: SEI growth activation energy [J.mol-1]": 0.0, - "Secondary: Ratio of lithium moles to SEI moles": 2.0, - "Secondary: Inner SEI reaction proportion": 0.5, - "Secondary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Secondary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Secondary: SEI reaction exchange current density [A.m-2]": 1.5e-07, - "Secondary: SEI resistivity [Ohm.m]": 200000.0, - "Secondary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Secondary: Bulk solvent concentration [mol.m-3]": 2636.0, - "Secondary: Inner SEI open-circuit potential [V]": 0.1, - "Secondary: Outer SEI open-circuit potential [V]": 0.8, - "Secondary: Inner SEI electron conductivity [S.m-1]": 8.95e-14, - "Secondary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Secondary: Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Secondary: Initial inner SEI thickness [m]": 2.5e-09, - "Secondary: Initial outer SEI thickness [m]": 2.5e-09, - "Secondary: EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "Secondary: EC diffusivity [m2.s-1]": 2e-18, - "Secondary: SEI kinetic rate constant [m.s-1]": 1e-12, - "Secondary: SEI open-circuit potential [V]": 0.4, - "Secondary: SEI growth activation energy [J.mol-1]": 0.0, + "Primary: Ratio of lithium moles to negative SEI moles": 2.0, + "Primary: Negative inner SEI reaction proportion": 0.5, + "Primary: Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Primary: Negatuve outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Primary: Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Primary: Negative SEI resistivity [Ohm.m]": 200000.0, + "Primary: Negative outer SEI solvent diffusivity [m2.s-1]" + "": 2.5000000000000002e-22, + "Primary: Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Primary: Negative inner SEI open-circuit potential [V]": 0.1, + "Primary: Negative outer SEI open-circuit potential [V]": 0.8, + "Primary: Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Primary: Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Primary: Negative lithium interstitial reference concentration [mol.m-3]" + "": 15.0, + "Primary: Initial negative inner SEI thickness [m]": 2.5e-09, + "Primary: Initial negative outer SEI thickness [m]": 2.5e-09, + "Primary: Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "Primary: EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Primary: Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Primary: Negative SEI open-circuit potential [V]": 0.4, + "Primary: Negative SEI growth activation energy [J.mol-1]": 0.0, + "Secondary: Ratio of lithium moles to negative SEI moles": 2.0, + "Secondary: Negative inner SEI reaction proportion": 0.5, + "Secondary: Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Secondary: Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Secondary: Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Secondary: Negative SEI resistivity [Ohm.m]": 200000.0, + "Secondary: Negative outer SEI solvent diffusivity [m2.s-1]" + "": 2.5000000000000002e-22, + "Secondary: Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Secondary: Negative inner SEI open-circuit potential [V]": 0.1, + "Secondary: Negative outer SEI open-circuit potential [V]": 0.8, + "Secondary: Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Secondary: Negative inner SEI lithium interstitial diffusivity [m2.s-1]" + "": 1e-20, + "Secondary: Negative lithium interstitial reference concentration [mol.m-3]" + "": 15.0, + "Secondary: Initial negative inner SEI thickness [m]": 2.5e-09, + "Secondary: Initial negative outer SEI thickness [m]": 2.5e-09, + "Secondary: Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "Secondary: EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Secondary: Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Secondary: Negative SEI open-circuit potential [V]": 0.4, + "Secondary: Negative SEI growth activation energy [J.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell "Negative current collector thickness [m]": 1.2e-05, diff --git a/src/pybamm/input/parameters/lithium_ion/Ecker2015.py b/src/pybamm/input/parameters/lithium_ion/Ecker2015.py index 05fbbb2fd7..a3b10a0367 100644 --- a/src/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/src/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -316,9 +316,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_e + return pybamm.constants.F * k_pl * c_e def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): @@ -350,9 +350,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_Li + return pybamm.constants.F * k_pl * c_Li def SEI_limited_dead_lithium_OKane2022(L_sei): @@ -374,9 +374,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei): Dead lithium decay rate [s-1] """ - gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") - L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") - L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + gamma_0 = pybamm.Parameter("Negative dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial negative inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial negative outer SEI thickness [m]") L_sei_0 = L_inner_0 + L_outer_0 gamma = gamma_0 * L_sei_0 / L_sei @@ -502,37 +502,37 @@ def get_parameter_values(): "chemistry": "lithium_ion", # lithium plating "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, - "Lithium plating kinetic rate constant [m.s-1]": 1e-10, - "Exchange-current density for plating [A.m-2]" + "Negative lithium plating kinetic rate constant [m.s-1]": 1e-10, + "Exchange-current density for negative lithium plating [A.m-2]" "": plating_exchange_current_density_OKane2020, - "Exchange-current density for stripping [A.m-2]" + "Exchange-current density for negative lithium stripping [A.m-2]" "": stripping_exchange_current_density_OKane2020, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.5, - "Dead lithium decay constant [s-1]": 1e-06, - "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, + "Initial negative lithium plating concentration [mol.m-3]": 0.0, + "Negative lithium plating reference concentration [mol.m-3]": 1000.0, + "Negative lithium plating transfer coefficient": 0.5, + "Negative dead lithium decay constant [s-1]": 1e-06, + "Negative dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index 267f55e774..0f24aaa309 100644 --- a/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/src/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -203,9 +203,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_e + return pybamm.constants.F * k_pl * c_e def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): @@ -237,9 +237,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_Li + return pybamm.constants.F * k_pl * c_Li def SEI_limited_dead_lithium_OKane2022(L_sei): @@ -261,9 +261,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei): Dead lithium decay rate [s-1] """ - gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") - L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") - L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + gamma_0 = pybamm.Parameter("Positive dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial positive inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial positive outer SEI thickness [m]") L_sei_0 = L_inner_0 + L_outer_0 gamma = gamma_0 * L_sei_0 / L_sei @@ -424,37 +424,57 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # lithium plating - "Lithium plating kinetic rate constant [m.s-1]": 1e-10, - "Exchange-current density for plating [A.m-2]" + "Positive lithium plating kinetic rate constant [m.s-1]": 1e-10, + "Exchange-current density for positive lithium plating [A.m-2]" "": plating_exchange_current_density_OKane2020, - "Exchange-current density for stripping [A.m-2]" + "Exchange-current density for positive lithium stripping [A.m-2]" "": stripping_exchange_current_density_OKane2020, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.5, - "Dead lithium decay constant [s-1]": 1e-06, - "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, + "Initial positive lithium plating concentration [mol.m-3]": 0.0, + "Positive lithium plating reference concentration [mol.m-3]": 1000.0, + "Positive lithium plating transfer coefficient": 0.5, + "Positive dead lithium decay constant [s-1]": 1e-06, + "Positive dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to positive SEI moles": 2.0, + "Positive inner SEI reaction proportion": 0.5, + "Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Positive SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Positive SEI resistivity [Ohm.m]": 200000.0, + "Positive outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0, + "Positive inner SEI open-circuit potential [V]": 0.1, + "Positive outer SEI open-circuit potential [V]": 0.8, + "Positive inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Positive lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial positive inner SEI thickness [m]": 2.5e-09, + "Initial positive outer SEI thickness [m]": 2.5e-09, + "Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through positive SEI [m2.s-1]": 2e-18, + "Positive SEI kinetic rate constant [m.s-1]": 1e-12, + "Positive SEI open-circuit potential [V]": 0.4, + "Positive SEI growth activation energy [J.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell "Negative current collector thickness [m]": 1.4e-05, diff --git a/src/pybamm/input/parameters/lithium_ion/Marquis2019.py b/src/pybamm/input/parameters/lithium_ion/Marquis2019.py index 16591eac2d..ff9c570877 100644 --- a/src/pybamm/input/parameters/lithium_ion/Marquis2019.py +++ b/src/pybamm/input/parameters/lithium_ion/Marquis2019.py @@ -351,27 +351,26 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI growth transfer coefficient": 0.5, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py index 0176c3f6a0..37c6c034f6 100644 --- a/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py +++ b/src/pybamm/input/parameters/lithium_ion/Mohtat2020.py @@ -338,31 +338,31 @@ def get_parameter_values(): "chemistry": "lithium_ion", # lithium plating "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, - "Exchange-current density for plating [A.m-2]": 0.001, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.7, + "Exchange-current density for negative lithium plating [A.m-2]": 0.001, + "Initial negative lithium plating concentration [mol.m-3]": 0.0, + "Negative lithium plating reference concentration [mol.m-3]": 1000.0, + "Negative lithium plating transfer coefficient": 0.7, # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py index 1af610f58a..487b782505 100644 --- a/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py +++ b/src/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py @@ -310,26 +310,26 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/OKane2022.py b/src/pybamm/input/parameters/lithium_ion/OKane2022.py index 4ccb72bf62..af4ac47f15 100644 --- a/src/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/src/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -26,9 +26,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_e + return pybamm.constants.F * k_pl * c_e def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): @@ -60,9 +60,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Negative lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_Li + return pybamm.constants.F * k_pl * c_Li def SEI_limited_dead_lithium_OKane2022(L_sei): @@ -84,9 +84,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei): Dead lithium decay rate [s-1] """ - gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") - L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") - L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + gamma_0 = pybamm.Parameter("Negative dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial negative inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial negative outer SEI thickness [m]") L_sei_0 = L_inner_0 + L_outer_0 gamma = gamma_0 * L_sei_0 / L_sei @@ -511,39 +511,39 @@ def get_parameter_values(): "chemistry": "lithium_ion", # lithium plating "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, - "Lithium plating kinetic rate constant [m.s-1]": 1e-09, - "Exchange-current density for plating [A.m-2]" + "Negative lithium plating kinetic rate constant [m.s-1]": 1e-09, + "Exchange-current density for negative lithium plating [A.m-2]" "": plating_exchange_current_density_OKane2020, - "Exchange-current density for stripping [A.m-2]" + "Exchange-current density for negative lithium stripping [A.m-2]" "": stripping_exchange_current_density_OKane2020, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.65, - "Dead lithium decay constant [s-1]": 1e-06, - "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, + "Initial negative lithium plating concentration [mol.m-3]": 0.0, + "Negative lithium plating reference concentration [mol.m-3]": 1000.0, + "Negative lithium plating transfer coefficient": 0.65, + "Negative dead lithium decay constant [s-1]": 1e-06, + "Negative dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei - "Ratio of lithium moles to SEI moles": 1.0, - "Inner SEI reaction proportion": 0.0, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 0.0, - "Initial outer SEI thickness [m]": 5e-09, - "Initial inner SEI on cracks thickness [m]": 0, - "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 38000.0, + "Ratio of lithium moles to negative SEI moles": 1.0, + "Negative inner SEI reaction proportion": 0.0, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 0.0, + "Initial negative outer SEI thickness [m]": 5e-09, + "Initial negative inner SEI on cracks thickness [m]": 0, + "Initial negative outer SEI on cracks thickness [m]": 5e-13, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 38000.0, "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell diff --git a/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py index c343dd23f4..f17ec09a4c 100644 --- a/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py +++ b/src/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py @@ -56,9 +56,9 @@ def plating_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_e + return pybamm.constants.F * k_pl * c_e def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): @@ -90,9 +90,9 @@ def stripping_exchange_current_density_OKane2020(c_e, c_Li, T): Exchange-current density [A.m-2] """ - k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]") + k_pl = pybamm.Parameter("Positive lithium plating kinetic rate constant [m.s-1]") - return pybamm.constants.F * k_plating * c_Li + return pybamm.constants.F * k_pl * c_Li def SEI_limited_dead_lithium_OKane2022(L_sei): @@ -114,9 +114,9 @@ def SEI_limited_dead_lithium_OKane2022(L_sei): Dead lithium decay rate [s-1] """ - gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]") - L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]") - L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]") + gamma_0 = pybamm.Parameter("Positive dead lithium decay constant [s-1]") + L_inner_0 = pybamm.Parameter("Initial positive inner SEI thickness [m]") + L_outer_0 = pybamm.Parameter("Initial positive outer SEI thickness [m]") L_sei_0 = L_inner_0 + L_outer_0 gamma = gamma_0 * L_sei_0 / L_sei @@ -396,40 +396,59 @@ def get_parameter_values(): "chemistry": "lithium_ion", # lithium plating "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05, - "Lithium plating kinetic rate constant [m.s-1]": 1e-09, - "Exchange-current density for plating [A.m-2]" + "Positive lithium plating kinetic rate constant [m.s-1]": 1e-09, + "Exchange-current density for positive lithium plating [A.m-2]" "": plating_exchange_current_density_OKane2020, - "Exchange-current density for stripping [A.m-2]" + "Exchange-current density for positive lithium stripping [A.m-2]" "": stripping_exchange_current_density_OKane2020, - "Initial plated lithium concentration [mol.m-3]": 0.0, - "Typical plated lithium concentration [mol.m-3]": 1000.0, - "Lithium plating transfer coefficient": 0.65, - "Dead lithium decay constant [s-1]": 1e-06, - "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, + "Initial positive lithium plating concentration [mol.m-3]": 0.0, + "Positive lithium plating reference concentration [mol.m-3]": 1000.0, + "Positive lithium plating transfer coefficient": 0.65, + "Positive dead lithium decay constant [s-1]": 1e-06, + "Positive dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022, # sei - "Ratio of lithium moles to SEI moles": 1.0, - "Inner SEI reaction proportion": 0.0, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 0.0, - "Initial outer SEI thickness [m]": 5e-09, - "Initial inner SEI on cracks thickness [m]": 0, - "Initial outer SEI on cracks thickness [m]": 5e-13, # avoid division by zero - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 38000.0, - "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 1.0, + "Negative inner SEI reaction proportion": 0.0, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 0.0, + "Initial negative outer SEI thickness [m]": 5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 38000.0, + "Ratio of lithium moles to positive SEI moles": 1.0, + "Positive inner SEI reaction proportion": 0.0, + "Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Positive SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Positive SEI resistivity [Ohm.m]": 200000.0, + "Positive outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0, + "Positive inner SEI open-circuit potential [V]": 0.1, + "Positive outer SEI open-circuit potential [V]": 0.8, + "Positive inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Positive lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial positive inner SEI thickness [m]": 0.0, + "Initial positive outer SEI thickness [m]": 5e-09, + "Initial positive inner SEI on cracks thickness [m]": 0, + "Initial positive outer SEI on cracks thickness [m]": 5e-13, + "Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through positive SEI [m2.s-1]": 2e-18, + "Positive SEI kinetic rate constant [m.s-1]": 1e-12, + "Positive SEI open-circuit potential [V]": 0.4, + "Positive SEI growth activation energy [J.mol-1]": 38000.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell "Negative current collector thickness [m]": 1.2e-05, diff --git a/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py index a1e24da7e3..a8ab71ebc4 100644 --- a/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py +++ b/src/pybamm/input/parameters/lithium_ion/Ramadass2004.py @@ -365,26 +365,28 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-06, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.0, - "SEI growth activation energy [J.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, + "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, + "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell "Negative current collector thickness [m]": 1.7e-05, "Negative electrode thickness [m]": 8.8e-05, diff --git a/src/pybamm/input/parameters/lithium_ion/Xu2019.py b/src/pybamm/input/parameters/lithium_ion/Xu2019.py index caee487339..76962c3f62 100644 --- a/src/pybamm/input/parameters/lithium_ion/Xu2019.py +++ b/src/pybamm/input/parameters/lithium_ion/Xu2019.py @@ -214,27 +214,26 @@ def get_parameter_values(): return { "chemistry": "lithium_ion", # sei - "Ratio of lithium moles to SEI moles": 2.0, - "Inner SEI reaction proportion": 0.5, - "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05, - "SEI reaction exchange current density [A.m-2]": 1.5e-07, - "SEI resistivity [Ohm.m]": 200000.0, - "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, - "Bulk solvent concentration [mol.m-3]": 2636.0, - "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, - "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, - "Lithium interstitial reference concentration [mol.m-3]": 15.0, - "Initial inner SEI thickness [m]": 2.5e-09, - "Initial outer SEI thickness [m]": 2.5e-09, - "EC initial concentration in electrolyte [mol.m-3]": 4541.0, - "EC diffusivity [m2.s-1]": 2e-18, - "SEI kinetic rate constant [m.s-1]": 1e-12, - "SEI open-circuit potential [V]": 0.4, - "SEI growth activation energy [J.mol-1]": 0.0, - "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0, + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0, # cell "Negative electrode thickness [m]": 0.0007, diff --git a/src/pybamm/models/submodels/interface/lithium_plating/plating.py b/src/pybamm/models/submodels/interface/lithium_plating/plating.py index f019c3b9d8..9950ca1450 100644 --- a/src/pybamm/models/submodels/interface/lithium_plating/plating.py +++ b/src/pybamm/models/submodels/interface/lithium_plating/plating.py @@ -27,7 +27,7 @@ def __init__(self, param, domain, x_average, options, phase="primary"): def get_fundamental_variables(self): domain, Domain = self.domain_Domain - scale = self.phase_param.c_Li_typ + scale = self.phase_param.c_Li_ref if self.x_average is True: c_plated_Li_av = pybamm.Variable( f"X-averaged {domain} {self.phase_name}lithium plating concentration " diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index d2a01b750e..87ddcd2a83 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -49,10 +49,10 @@ def get_coupled_variables(self, variables): L_sei_0 = pybamm.Scalar(0) else: L_inner_0 = pybamm.Parameter( - f"{pref}Initial inner SEI thickness [m]" + f"{pref}Initial {domain} inner SEI thickness [m]" ) L_outer_0 = pybamm.Parameter( - f"{pref}Initial outer SEI thickness [m]" + f"{pref}Initial {domain} outer SEI thickness [m]" ) L_sei_0 = L_inner_0 + L_outer_0 L_pl_k = variables[ diff --git a/src/pybamm/parameters/lithium_ion_parameters.py b/src/pybamm/parameters/lithium_ion_parameters.py index e372cab4a4..2f6dac49a0 100644 --- a/src/pybamm/parameters/lithium_ion_parameters.py +++ b/src/pybamm/parameters/lithium_ion_parameters.py @@ -390,64 +390,83 @@ def _set_parameters(self): # SEI parameters self.V_bar_inner = pybamm.Parameter( - f"{pref}Inner SEI partial molar volume [m3.mol-1]" + f"{pref}{Domain} inner SEI partial molar volume [m3.mol-1]" ) self.V_bar_outer = pybamm.Parameter( - f"{pref}Outer SEI partial molar volume [m3.mol-1]" + f"{pref}{Domain} outer SEI partial molar volume [m3.mol-1]" ) - self.j0_sei = pybamm.Parameter( - f"{pref}SEI reaction exchange current density [A.m-2]" + f"{pref}{Domain} SEI reaction exchange current density [A.m-2]" + ) + self.R_sei = pybamm.Parameter(f"{pref}{Domain} SEI resistivity [Ohm.m]") + self.D_sol = pybamm.Parameter( + f"{pref}{Domain} outer SEI solvent diffusivity [m2.s-1]" + ) + self.c_sol = pybamm.Parameter( + f"{pref} Bulk solvent concentration for {domain} SEI [mol.m-3]" + ) + self.U_inner = pybamm.Parameter( + f"{pref}{Domain} inner SEI open-circuit potential [V]" + ) + self.U_outer = pybamm.Parameter( + f"{pref}{Domain} outer SEI open-circuit potential [V]" ) - - self.R_sei = pybamm.Parameter(f"{pref}SEI resistivity [Ohm.m]") - self.D_sol = pybamm.Parameter(f"{pref}Outer SEI solvent diffusivity [m2.s-1]") - self.c_sol = pybamm.Parameter(f"{pref}Bulk solvent concentration [mol.m-3]") - self.U_inner = pybamm.Parameter(f"{pref}Inner SEI open-circuit potential [V]") - self.U_outer = pybamm.Parameter(f"{pref}Outer SEI open-circuit potential [V]") self.kappa_inner = pybamm.Parameter( - f"{pref}Inner SEI electron conductivity [S.m-1]" + f"{pref}{Domain} inner SEI electron conductivity [S.m-1]" ) self.D_li = pybamm.Parameter( - f"{pref}Inner SEI lithium interstitial diffusivity [m2.s-1]" + f"{pref}{Domain} inner SEI lithium interstitial diffusivity [m2.s-1]" ) self.c_li_0 = pybamm.Parameter( - f"{pref}Lithium interstitial reference concentration [mol.m-3]" + f"{pref}{Domain} lithium interstitial reference concentration [mol.m-3]" + ) + self.L_inner_0 = pybamm.Parameter( + f"{pref}Initial {domain} inner SEI thickness [m]" + ) + self.L_outer_0 = pybamm.Parameter( + f"{pref}Initial {domain} outer SEI thickness [m]" ) - self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]") - self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]") + self.L_sei_0 = self.L_inner_0 + self.L_outer_0 self.L_inner_crack_0 = pybamm.Parameter( - f"{pref}Initial inner SEI on cracks thickness [m]" + f"{pref}Initial {domain} inner SEI on cracks thickness [m]" ) self.L_outer_crack_0 = pybamm.Parameter( - f"{pref}Initial outer SEI on cracks thickness [m]" + f"{pref}Initial {domain} outer SEI on cracks thickness [m]" + ) + self.E_sei = pybamm.Parameter( + f"{pref}{Domain} SEI growth activation energy [J.mol-1]" + ) + self.alpha_SEI = pybamm.Parameter( + f"{pref}{Domain} SEI growth transfer coefficient" ) - - self.L_sei_0 = self.L_inner_0 + self.L_outer_0 - self.E_sei = pybamm.Parameter(f"{pref}SEI growth activation energy [J.mol-1]") - self.alpha_SEI = pybamm.Parameter(f"{pref}SEI growth transfer coefficient") self.inner_sei_proportion = pybamm.Parameter( - f"{pref}Inner SEI reaction proportion" + f"{pref}{Domain} inner SEI reaction proportion" + ) + self.z_sei = pybamm.Parameter( + f"{pref}Ratio of lithium moles to {domain} SEI moles" ) - self.z_sei = pybamm.Parameter(f"{pref}Ratio of lithium moles to SEI moles") # EC reaction self.c_ec_0 = pybamm.Parameter( - f"{pref}EC initial concentration in electrolyte [mol.m-3]" + f"{pref}{Domain} EC initial concentration in electrolyte [mol.m-3]" + ) + self.D_ec = pybamm.Parameter( + f"{pref} EC diffusivity through {domain} SEI [m2.s-1]" ) - self.D_ec = pybamm.Parameter(f"{pref}EC diffusivity [m2.s-1]") - self.k_sei = pybamm.Parameter(f"{pref}SEI kinetic rate constant [m.s-1]") - self.U_sei = pybamm.Parameter(f"{pref}SEI open-circuit potential [V]") + self.k_sei = pybamm.Parameter( + f"{pref}{Domain} SEI kinetic rate constant [m.s-1]" + ) + self.U_sei = pybamm.Parameter(f"{pref}{Domain} SEI open-circuit potential [V]") # Lithium plating parameters - self.c_Li_typ = pybamm.Parameter( - f"{pref}Typical plated lithium concentration [mol.m-3]" + self.c_Li_ref = pybamm.Parameter( + f"{pref}{Domain} lithium plating reference concentration [mol.m-3]" ) self.c_plated_Li_0 = pybamm.Parameter( - f"{pref}Initial plated lithium concentration [mol.m-3]" + f"{pref}Initial {domain} lithium plating concentration [mol.m-3]" ) self.alpha_plating = pybamm.Parameter( - f"{pref}Lithium plating transfer coefficient" + f"{pref}{Domain} lithium plating transfer coefficient" ) self.alpha_stripping = 1 - self.alpha_plating @@ -591,27 +610,29 @@ def j0(self, c_e, c_s_surf, T, lithiation=None): def j0_stripping(self, c_e, c_Li, T): """Dimensional exchange-current density for stripping [A.m-2]""" - Domain = self.domain.capitalize() + domain, Domain = self.domain_Domain inputs = { f"{Domain} electrolyte concentration [mol.m-3]": c_e, f"{Domain} plated lithium concentration [mol.m-3]": c_Li, f"{Domain} temperature [K]": T, } return pybamm.FunctionParameter( - f"{self.phase_prefactor}Exchange-current density for stripping [A.m-2]", + f"{self.phase_prefactor}" + f"Exchange-current density for {domain} lithium stripping [A.m-2]", inputs, ) def j0_plating(self, c_e, c_Li, T): """Dimensional exchange-current density for plating [A.m-2]""" - Domain = self.domain.capitalize() + domain, Domain = self.domain_Domain inputs = { f"{Domain} electrolyte concentration [mol.m-3]": c_e, f"{Domain} plated lithium concentration [mol.m-3]": c_Li, f"{Domain} temperature [K]": T, } return pybamm.FunctionParameter( - f"{self.phase_prefactor}Exchange-current density for plating [A.m-2]", + f"{self.phase_prefactor}" + f"Exchange-current density for {domain} lithium plating [A.m-2]", inputs, ) @@ -620,7 +641,7 @@ def dead_lithium_decay_rate(self, L_sei): Domain = self.domain.capitalize() inputs = {f"{Domain} total {self.phase_name}SEI thickness [m]": L_sei} return pybamm.FunctionParameter( - f"{self.phase_prefactor}Dead lithium decay rate [s-1]", inputs + f"{self.phase_prefactor}{Domain} dead lithium decay rate [s-1]", inputs ) def U(self, sto, T, lithiation=None): From d1ed350461682fd072b0cbc2491844a05ffcf2c5 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 10 Sep 2024 16:58:59 +0100 Subject: [PATCH 12/20] Updated half-cell example notebook to showcase new capability --- .../examples/notebooks/models/half-cell.ipynb | 157 +++++++++++++----- .../porosity/reaction_driven_porosity.py | 4 +- 2 files changed, 113 insertions(+), 48 deletions(-) diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb index 2085162694..80cad842ca 100644 --- a/docs/source/examples/notebooks/models/half-cell.ipynb +++ b/docs/source/examples/notebooks/models/half-cell.ipynb @@ -65,14 +65,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -109,19 +107,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "At t = 285.669 and h = 7.17426e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n" + "At t = 285.669 and h = 2.09913e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n" ] }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -180,7 +174,6 @@ " }\n", ")\n", "param_GrSi = pybamm.ParameterValues(\"OKane2022_graphite_SiOx_halfcell\")\n", - "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 1.5e-07})\n", "var_pts = {\"x_n\": 1, \"x_s\": 5, \"x_p\": 7, \"r_n\": 1, \"r_p\": 30}\n", "exp_degradation = pybamm.Experiment(\n", " [\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"]\n", @@ -217,14 +210,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -249,7 +240,7 @@ "id": "fbc7da60", "metadata": {}, "source": [ - "The SEI growth is slow compared to the reversible component of the lithium plating. What happens if the SEI growth rate is increased?" + "The SEI growth is slow compared to the reversible component of the lithium plating. What happens if the SEI growth rate on the lithium metal electrode is increased?" ] }, { @@ -260,19 +251,17 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 6e-07})\n", + "param_GrSi.update({\"Negative SEI reaction exchange current density [A.m-2]\": 6e-07}) # 300% increase\n", "sim4 = pybamm.Simulation(\n", " model_with_degradation,\n", " parameter_values=param_GrSi,\n", @@ -297,14 +286,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -329,12 +316,89 @@ "id": "6e900be5", "metadata": {}, "source": [ - "The additional SEI increases the cell resistance, preventing the graphite-silicon composite from being fully lithiated, so there is less plating than before." + "The additional SEI increases the cell resistance, preventing the graphite-silicon composite from being fully lithiated, so there is less plating than before. What happens if the increase is applied to the porous electrode instead?" ] }, { "cell_type": "code", "execution_count": 9, + "id": "20d2680d-daae-46e0-a296-4c9dfd35178e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "param_GrSi.update({\"Negative SEI reaction exchange current density [A.m-2]\": 1.5e-07}) # reset to original value\n", + "param_GrSi.update({\"Positive SEI reaction exchange current density [A.m-2]\": 6e-07}) # 300% increase\n", + "sim5 = pybamm.Simulation(\n", + " model_with_degradation,\n", + " parameter_values=param_GrSi,\n", + " experiment=exp_degradation,\n", + " var_pts=var_pts,\n", + ")\n", + "sol5 = sim5.solve()\n", + "t = sol5[\"Time [s]\"].entries\n", + "V = sol5[\"Voltage [V]\"].entries\n", + "plt.figure()\n", + "plt.plot(t, V)\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Voltage [V]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "86c0c41f-0b12-46f6-922c-4ee318d7fa3a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q_SEI_n = sol5[\"Loss of capacity to negative SEI [A.h]\"].entries\n", + "Q_SEI_p = sol5[\"Loss of capacity to positive SEI [A.h]\"].entries\n", + "Q_SEI_cr = sol5[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n", + "Q_pl = sol5[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n", + "plt.figure()\n", + "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n", + "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n", + "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n", + "plt.plot(t, Q_pl, label=\"Lithium plating\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5f326c29-b3da-4932-a9de-240346e908a6", + "metadata": {}, + "source": [ + "SEI on the porous electrode has a smaller effect on the cell resistance, because it is spread over the microstructure and is therefore much thinner." + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "faa82d38", "metadata": {}, "outputs": [ @@ -344,16 +408,17 @@ "text": [ "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", - "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[11] Lars Ole Valøen and Jan N Reimers. Transport properties of lipf6-based li-ion battery electrolytes. Journal of The Electrochemical Society, 152(5):A882, 2005.\n", - "[12] Shanshan Xu, Kuan-Hung Chen, Neil P Dasgupta, Jason B Siegel, and Anna G Stefanopoulou. Evolution of dead lithium growth in lithium metal batteries: experimentally validated model of the apparent capacity loss. Journal of The Electrochemical Society, 166(14):A3456, 2019.\n", + "[3] Von DAG Bruggeman. Berechnung verschiedener physikalischer konstanten von heterogenen substanzen. i. dielektrizitätskonstanten und leitfähigkeiten der mischkörper aus isotropen substanzen. Annalen der physik, 416(7):636–664, 1935.\n", + "[4] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[5] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[6] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[7] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[8] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n", + "[9] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[10] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[11] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[12] Lars Ole Valøen and Jan N Reimers. Transport properties of lipf6-based li-ion battery electrolytes. Journal of The Electrochemical Society, 152(5):A882, 2005.\n", + "[13] Shanshan Xu, Kuan-Hung Chen, Neil P Dasgupta, Jason B Siegel, and Anna G Stefanopoulou. Evolution of dead lithium growth in lithium metal batteries: experimentally validated model of the apparent capacity loss. Journal of The Electrochemical Society, 166(14):A3456, 2019.\n", "\n" ] } @@ -387,7 +452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 87ddcd2a83..0ead0d1134 100644 --- a/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/src/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -49,10 +49,10 @@ def get_coupled_variables(self, variables): L_sei_0 = pybamm.Scalar(0) else: L_inner_0 = pybamm.Parameter( - f"{pref}Initial {domain} inner SEI thickness [m]" + f"{pref}Initial {dom} inner SEI thickness [m]" ) L_outer_0 = pybamm.Parameter( - f"{pref}Initial {domain} outer SEI thickness [m]" + f"{pref}Initial {dom} outer SEI thickness [m]" ) L_sei_0 = L_inner_0 + L_outer_0 L_pl_k = variables[ From a61797a1b80defac862492782e680be2fa352a09 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 14:44:29 +0100 Subject: [PATCH 13/20] Added Chen2020_composite_halfcell parameter set --- .../Chen2020_composite_halfcell.py | 441 ++++++++++++++++++ 1 file changed, 441 insertions(+) create mode 100644 src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py new file mode 100644 index 0000000000..30234c7c35 --- /dev/null +++ b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite_halfcell.py @@ -0,0 +1,441 @@ +import pybamm +import os +import numpy as np + + +def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T): + """ + Exchange-current density for Butler-Volmer reactions between li metal and LiPF6 in + EC:DMC. + + References + ---------- + .. [1] Xu, Shanshan, Chen, Kuan-Hung, Dasgupta, Neil P., Siegel, Jason B. and + Stefanopoulou, Anna G. "Evolution of Dead Lithium Growth in Lithium Metal Batteries: + Experimentally Validated Model of the Apparent Capacity Loss." Journal of The + Electrochemical Society 166.14 (2019): A3456-A3463. + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_Li : :class:`pybamm.Symbol` + Pure metal lithium concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + m_ref = 3.5e-8 * pybamm.constants.F # (A/m2)(mol/m3) - includes ref concentrations + + return m_ref * c_Li**0.7 * c_e**0.3 + + +def graphite_LGM50_electrolyte_exchange_current_density_Chen2020( + c_e, c_s_surf, c_s_max, T +): + """ + Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in + EC:DMC. + + References + ---------- + .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the + Electrochemical Society 167 (2020): 080534. + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_s_surf : :class:`pybamm.Symbol` + Particle concentration [mol.m-3] + c_s_max : :class:`pybamm.Symbol` + Maximum particle concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + m_ref = 6.48e-7 # (A/m2)(m3/mol)**1.5 - includes ref concentrations + E_r = 35000 + arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) + + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 + + +def silicon_ocp_lithiation_Mark2016(sto): + """ + silicon Open-circuit Potential (OCP) as a a function of the + stoichiometry. The fit is taken from the Enertech cell [1], which is only accurate + for 0 < sto < 1. + + References + ---------- + .. [1] Verbrugge M, Baker D, Xiao X. Formulation for the treatment of multiple + electrochemical reactions and associated speciation for the Lithium-Silicon + electrode[J]. Journal of The Electrochemical Society, 2015, 163(2): A262. + + Parameters + ---------- + sto: double + stoichiometry of material (li-fraction) + + Returns + ------- + :class:`pybamm.Symbol` + OCP [V] + """ + p1 = -96.63 + p2 = 372.6 + p3 = -587.6 + p4 = 489.9 + p5 = -232.8 + p6 = 62.99 + p7 = -9.286 + p8 = 0.8633 + + U_lithiation = ( + p1 * sto**7 + + p2 * sto**6 + + p3 * sto**5 + + p4 * sto**4 + + p5 * sto**3 + + p6 * sto**2 + + p7 * sto + + p8 + ) + return U_lithiation + + +def silicon_ocp_delithiation_Mark2016(sto): + """ + silicon Open-circuit Potential (OCP) as a a function of the + stoichiometry. The fit is taken from the Enertech cell [1], which is only accurate + for 0 < sto < 1. + + References + ---------- + .. [1] Verbrugge M, Baker D, Xiao X. Formulation for the treatment of multiple + electrochemical reactions and associated speciation for the Lithium-Silicon + electrode[J]. Journal of The Electrochemical Society, 2015, 163(2): A262. + + Parameters + ---------- + sto: double + stoichiometry of material (li-fraction) + + Returns + ------- + :class:`pybamm.Symbol` + OCP [V] + """ + p1 = -51.02 + p2 = 161.3 + p3 = -205.7 + p4 = 140.2 + p5 = -58.76 + p6 = 16.87 + p7 = -3.792 + p8 = 0.9937 + + U_delithiation = ( + p1 * sto**7 + + p2 * sto**6 + + p3 * sto**5 + + p4 * sto**4 + + p5 * sto**3 + + p6 * sto**2 + + p7 * sto + + p8 + ) + return U_delithiation + + +def silicon_LGM50_electrolyte_exchange_current_density_Chen2020( + c_e, c_s_surf, c_s_max, T +): + """ + Exchange-current density for Butler-Volmer reactions between silicon and LiPF6 in + EC:DMC. + + References + ---------- + .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the + Electrochemical Society 167 (2020): 080534. + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_s_surf : :class:`pybamm.Symbol` + Particle concentration [mol.m-3] + c_s_max : :class:`pybamm.Symbol` + Maximum particle concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + + m_ref = ( + 6.48e-7 * 28700 / 278000 + ) # (A/m2)(m3/mol)**1.5 - includes ref concentrations + E_r = 35000 + arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T)) + + return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5 + + +def electrolyte_diffusivity_Nyman2008(c_e, T): + """ + Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1] + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10 + + # Nyman et al. (2008) does not provide temperature dependence + + return D_c_e + + +def electrolyte_conductivity_Nyman2008(c_e, T): + """ + Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data + comes from [1]. + + References + ---------- + .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and + modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte," + Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008. + + Parameters + ---------- + c_e: :class:`pybamm.Symbol` + Dimensional electrolyte concentration + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + sigma_e = ( + 0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000) + ) + + # Nyman et al. (2008) does not provide temperature dependence + + return sigma_e + + +# Load data in the appropriate format +path, _ = os.path.split(os.path.abspath(__file__)) +graphite_ocp_Enertech_Ai2020_data = pybamm.parameters.process_1D_data( + "graphite_ocp_Enertech_Ai2020.csv", path=path +) + + +def graphite_ocp_Enertech_Ai2020(sto): + name, (x, y) = graphite_ocp_Enertech_Ai2020_data + return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic") + + +# Call dict via a function to avoid errors when editing in place +def get_parameter_values(): + """ + Parameters for a composite graphite/silicon negative electrode, from the paper + :footcite:t:`Ai2022`, based on the paper :footcite:t:`Chen2020`, and references + therein. + + SEI parameters are example parameters for composite SEI on silicon/graphite. Both + phases use the same values, from the paper :footcite:t:`Yang2017` + """ + + return { + "chemistry": "lithium_ion", + # sei + "Ratio of lithium moles to negative SEI moles": 2.0, + "Negative inner SEI reaction proportion": 0.5, + "Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Negative SEI resistivity [Ohm.m]": 200000.0, + "Negative outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22, + "Bulk solvent concentration for negative SEI [mol.m-3]": 2636.0, + "Negative inner SEI open-circuit potential [V]": 0.1, + "Negative outer SEI open-circuit potential [V]": 0.8, + "Negative inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Negative inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Negative lithium interstitial reference concentration [mol.m-3]": 15.0, + "Initial negative inner SEI thickness [m]": 2.5e-09, + "Initial negative outer SEI thickness [m]": 2.5e-09, + "Negative EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "EC diffusivity through negative SEI [m2.s-1]": 2e-18, + "Negative SEI kinetic rate constant [m.s-1]": 1e-12, + "Negative SEI open-circuit potential [V]": 0.4, + "Negative SEI growth activation energy [J.mol-1]": 0.0, + "Primary: Ratio of lithium moles to positive SEI moles": 2.0, + "Primary: Positive inner SEI reaction proportion": 0.5, + "Primary: Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Primary: Posituve outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Primary: Positive SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Primary: Positive SEI resistivity [Ohm.m]": 200000.0, + "Primary: Positive outer SEI solvent diffusivity [m2.s-1]" + "": 2.5000000000000002e-22, + "Primary: Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0, + "Primary: Positive inner SEI open-circuit potential [V]": 0.1, + "Primary: Positive outer SEI open-circuit potential [V]": 0.8, + "Primary: Positive inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Primary: Positive inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20, + "Primary: Positive lithium interstitial reference concentration [mol.m-3]" + "": 15.0, + "Primary: Initial positive inner SEI thickness [m]": 2.5e-09, + "Primary: Initial positive outer SEI thickness [m]": 2.5e-09, + "Primary: Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "Primary: EC diffusivity through positive SEI [m2.s-1]": 2e-18, + "Primary: Positive SEI kinetic rate constant [m.s-1]": 1e-12, + "Primary: Positive SEI open-circuit potential [V]": 0.4, + "Primary: Positive SEI growth activation energy [J.mol-1]": 0.0, + "Secondary: Ratio of lithium moles to positive SEI moles": 2.0, + "Secondary: Positive inner SEI reaction proportion": 0.5, + "Secondary: Positive inner SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Secondary: Positive outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Secondary: Positive SEI reaction exchange current density [A.m-2]": 1.5e-07, + "Secondary: Positive SEI resistivity [Ohm.m]": 200000.0, + "Secondary: Positive outer SEI solvent diffusivity [m2.s-1]" + "": 2.5000000000000002e-22, + "Secondary: Bulk solvent concentration for positive SEI [mol.m-3]": 2636.0, + "Secondary: Positive inner SEI open-circuit potential [V]": 0.1, + "Secondary: Positive outer SEI open-circuit potential [V]": 0.8, + "Secondary: Positive inner SEI electron conductivity [S.m-1]": 8.95e-14, + "Secondary: Positive inner SEI lithium interstitial diffusivity [m2.s-1]" + "": 1e-20, + "Secondary: Positive lithium interstitial reference concentration [mol.m-3]" + "": 15.0, + "Secondary: Initial positive inner SEI thickness [m]": 2.5e-09, + "Secondary: Initial positive outer SEI thickness [m]": 2.5e-09, + "Secondary: Positive EC initial concentration in electrolyte [mol.m-3]": 4541.0, + "Secondary: EC diffusivity through positive SEI [m2.s-1]": 2e-18, + "Secondary: Positive SEI kinetic rate constant [m.s-1]": 1e-12, + "Secondary: Positive SEI open-circuit potential [V]": 0.4, + "Secondary: Positive SEI growth activation energy [J.mol-1]": 0.0, + # cell + "Negative current collector thickness [m]": 1.2e-05, + "Negative electrode thickness [m]": 0.0007, + "Positive current collector thickness [m]": 1.2e-05, + "Positive electrode thickness [m]": 8.52e-05, + "Separator thickness [m]": 1.2e-05, + "Electrode height [m]": 0.065, + "Electrode width [m]": 1.58, + "Cell cooling surface area [m2]": 0.00531, + "Cell volume [m3]": 2.42e-05, + "Cell thermal expansion coefficient [m.K-1]": 1.1e-06, + "Positive current collector conductivity [S.m-1]": 58411000.0, + "Positive current collector density [kg.m-3]": 8960.0, + "Positive current collector specific heat capacity [J.kg-1.K-1]": 385.0, + "Positive current collector thermal conductivity [W.m-1.K-1]": 401.0, + "Nominal cell capacity [A.h]": 5.0, + "Current function [A]": 5.0, + "Contact resistance [Ohm]": 0, + # negative electrode + "Negative electrode OCP [V]": 0.0, + "Negative electrode conductivity [S.m-1]": 10776000.0, + "Negative electrode OCP entropic change [V.K-1]": 0.0, + "Exchange-current density for lithium metal electrode [A.m-2]" + "": li_metal_electrolyte_exchange_current_density_Xu2019, + "Negative electrode charge transfer coefficient": 0.5, + "Negative electrode double-layer capacity [F.m-2]": 0.2, + # positive electrode + "Positive electrode conductivity [S.m-1]": 215.0, + "Primary: Maximum concentration in positive electrode [mol.m-3]": 28700.0, + "Primary: Initial concentration in positive electrode [mol.m-3]": 27700.0, + "Primary: Positive particle diffusivity [m2.s-1]": 5.5e-14, + "Primary: Positive electrode OCP [V]": graphite_ocp_Enertech_Ai2020, + "Positive electrode porosity": 0.25, + "Primary: Positive electrode active material volume fraction": 0.735, + "Primary: Positive particle radius [m]": 5.86e-06, + "Positive electrode Bruggeman coefficient (electrolyte)": 1.5, + "Positive electrode Bruggeman coefficient (electrode)": 0, + "Positive electrode charge transfer coefficient": 0.5, + "Positive electrode double-layer capacity [F.m-2]": 0.2, + "Primary: Positive electrode exchange-current density [A.m-2]" + "": graphite_LGM50_electrolyte_exchange_current_density_Chen2020, + "Primary: Positive electrode density [kg.m-3]": 1657.0, + "Positive electrode specific heat capacity [J.kg-1.K-1]": 700.0, + "Positive electrode thermal conductivity [W.m-1.K-1]": 1.7, + "Primary: Positive electrode OCP entropic change [V.K-1]": 0.0, + "Secondary: Maximum concentration in positive electrode [mol.m-3]": 278000.0, + "Secondary: Initial concentration in positive electrode [mol.m-3]": 276610.0, + "Secondary: Positive particle diffusivity [m2.s-1]": 1.67e-14, + "Secondary: Positive electrode lithiation OCP [V]" + "": silicon_ocp_lithiation_Mark2016, + "Secondary: Positive electrode delithiation OCP [V]" + "": silicon_ocp_delithiation_Mark2016, + "Secondary: Positive electrode active material volume fraction": 0.015, + "Secondary: Positive particle radius [m]": 1.52e-06, + "Secondary: Positive electrode exchange-current density [A.m-2]" + "": silicon_LGM50_electrolyte_exchange_current_density_Chen2020, + "Secondary: Positive electrode density [kg.m-3]": 2650.0, + "Secondary: Positive electrode OCP entropic change [V.K-1]": 0.0, + # separator + "Separator porosity": 0.47, + "Separator Bruggeman coefficient (electrolyte)": 1.5, + "Separator density [kg.m-3]": 397.0, + "Separator specific heat capacity [J.kg-1.K-1]": 700.0, + "Separator thermal conductivity [W.m-1.K-1]": 0.16, + # electrolyte + "Initial concentration in electrolyte [mol.m-3]": 1000.0, + "Cation transference number": 0.2594, + "Thermodynamic factor": 1.0, + "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008, + "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008, + # experiment + "Reference temperature [K]": 298.15, + "Total heat transfer coefficient [W.m-2.K-1]": 10.0, + "Ambient temperature [K]": 298.15, + "Number of electrodes connected in parallel to make a cell": 1.0, + "Number of cells connected in series to make a battery": 1.0, + "Lower voltage cut-off [V]": 0.005, + "Upper voltage cut-off [V]": 1.5, + "Open-circuit voltage at 0% SOC [V]": 0.005, + "Open-circuit voltage at 100% SOC [V]": 1.5, + "Initial concentration in positive electrode [mol.m-3]": 29866.0, + "Initial temperature [K]": 298.15, + # citations + "citations": ["Chen2020", "Ai2022", "Xu2019"], + } From c987339976eb5157c2516bf3de7a9d54bc0d89ab Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 24 Sep 2024 14:00:57 +0000 Subject: [PATCH 14/20] style: pre-commit fixes --- .../source/examples/notebooks/models/half-cell.ipynb | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb index 80cad842ca..90de72868b 100644 --- a/docs/source/examples/notebooks/models/half-cell.ipynb +++ b/docs/source/examples/notebooks/models/half-cell.ipynb @@ -261,7 +261,9 @@ } ], "source": [ - "param_GrSi.update({\"Negative SEI reaction exchange current density [A.m-2]\": 6e-07}) # 300% increase\n", + "param_GrSi.update(\n", + " {\"Negative SEI reaction exchange current density [A.m-2]\": 6e-07}\n", + ") # 300% increase\n", "sim4 = pybamm.Simulation(\n", " model_with_degradation,\n", " parameter_values=param_GrSi,\n", @@ -337,8 +339,12 @@ } ], "source": [ - "param_GrSi.update({\"Negative SEI reaction exchange current density [A.m-2]\": 1.5e-07}) # reset to original value\n", - "param_GrSi.update({\"Positive SEI reaction exchange current density [A.m-2]\": 6e-07}) # 300% increase\n", + "param_GrSi.update(\n", + " {\"Negative SEI reaction exchange current density [A.m-2]\": 1.5e-07}\n", + ") # reset to original value\n", + "param_GrSi.update(\n", + " {\"Positive SEI reaction exchange current density [A.m-2]\": 6e-07}\n", + ") # 300% increase\n", "sim5 = pybamm.Simulation(\n", " model_with_degradation,\n", " parameter_values=param_GrSi,\n", From 7e0d34221790cf77ef27f05194f949fbc299b917 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 15:05:01 +0100 Subject: [PATCH 15/20] changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index a812385317..98d7527bd7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ ## Breaking changes +- The names of most SEI and plating parameters have been changed so that they have domains ([#4463](https://github.com/pybamm-team/PyBaMM/pull/4463)) - The parameters "... electrode OCP entropic change [V.K-1]" and "... electrode volume change" are now expected to be functions of stoichiometry only instead of functions of both stoichiometry and maximum concentration ([#4427](https://github.com/pybamm-team/PyBaMM/pull/4427)) - Renamed `set_events` function to `add_events_from` to better reflect its purpose. ([#4421](https://github.com/pybamm-team/PyBaMM/pull/4421)) From a5303f3bf840164cc615bd40ec1f091f07a94675 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 15:57:53 +0100 Subject: [PATCH 16/20] Fixed tests and parameter names --- src/pybamm/parameters/lithium_ion_parameters.py | 4 ++-- .../test_experiments/test_simulation_with_experiment.py | 4 ++-- .../test_parameter_sets/test_OKane2022.py | 9 ++++++--- .../test_OKane2022_negative_halfcell.py | 9 ++++++--- 4 files changed, 16 insertions(+), 10 deletions(-) diff --git a/src/pybamm/parameters/lithium_ion_parameters.py b/src/pybamm/parameters/lithium_ion_parameters.py index 4b8ac11aa9..342eca8059 100644 --- a/src/pybamm/parameters/lithium_ion_parameters.py +++ b/src/pybamm/parameters/lithium_ion_parameters.py @@ -372,7 +372,7 @@ def _set_parameters(self): f"{pref}{Domain} outer SEI solvent diffusivity [m2.s-1]" ) self.c_sol = pybamm.Parameter( - f"{pref} Bulk solvent concentration for {domain} SEI [mol.m-3]" + f"{pref}Bulk solvent concentration for {domain} SEI [mol.m-3]" ) self.U_inner = pybamm.Parameter( f"{pref}{Domain} inner SEI open-circuit potential [V]" @@ -420,7 +420,7 @@ def _set_parameters(self): f"{pref}{Domain} EC initial concentration in electrolyte [mol.m-3]" ) self.D_ec = pybamm.Parameter( - f"{pref} EC diffusivity through {domain} SEI [m2.s-1]" + f"{pref}EC diffusivity through {domain} SEI [m2.s-1]" ) self.k_sei = pybamm.Parameter( f"{pref}{Domain} SEI kinetic rate constant [m.s-1]" diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index 4f981ba04c..b46d59419e 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -382,7 +382,7 @@ def test_run_experiment_termination_capacity(self): ) model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"}) param = pybamm.ParameterValues("Chen2020") - param["SEI kinetic rate constant [m.s-1]"] = 1e-14 + param["Negative SEI kinetic rate constant [m.s-1]"] = 1e-14 sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) sol = sim.solve(solver=pybamm.CasadiSolver()) C = sol.summary_variables["Capacity [A.h]"] @@ -404,7 +404,7 @@ def test_run_experiment_termination_capacity(self): ) model = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"}) param = pybamm.ParameterValues("Chen2020") - param["SEI kinetic rate constant [m.s-1]"] = 1e-14 + param["Negative SEI kinetic rate constant [m.s-1]"] = 1e-14 sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) sol = sim.solve(solver=pybamm.CasadiSolver()) # all but the last value should be above the termination condition diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 014b467715..9ffd92d2bf 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -14,12 +14,15 @@ def test_functions(self): fun_test = { # Lithium plating - "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-2), - "Exchange-current density for stripping [A.m-2]": ( + "Exchange-current density for negayive lithium plating [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-2, + ), + "Exchange-current density for negative lithium stripping [A.m-2]": ( [1e3, 1e4, T], 9.6485e-1, ), - "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), + "Negative dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Negative electrode "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Negative electrode exchange-current density [A.m-2]": ( diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py index beebeb35e3..327f074e0b 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py @@ -13,12 +13,15 @@ def test_functions(self): fun_test = { # Lithium plating - "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-2), - "Exchange-current density for stripping [A.m-2]": ( + "Exchange-current density for positive lithium plating [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-2, + ), + "Exchange-current density for positive lithium stripping [A.m-2]": ( [1e3, 1e4, T], 9.6485e-1, ), - "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), + "Positive dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Positive electrode "Positive particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Positive electrode exchange-current density [A.m-2]": ( From bb75cb17cc8771e4daa1d0d662b85071c7a6ab17 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 16:37:01 +0100 Subject: [PATCH 17/20] Updated example notebooks with new parameter names --- docs/source/examples/notebooks/batch_study.ipynb | 14 +++++++------- .../notebooks/models/lithium-plating.ipynb | 8 ++++---- .../models/loss_of_active_materials.ipynb | 4 ++-- .../rpt-experiment.ipynb | 4 ++-- .../simulating-long-experiments.ipynb | 4 ++-- .../test_parameter_sets/test_Ecker2015.py | 9 ++++++--- .../test_Ecker2015_graphite_halfcell.py | 9 ++++++--- .../test_parameter_sets/test_OKane2022.py | 2 +- 8 files changed, 30 insertions(+), 24 deletions(-) diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb index 63169e6a07..9281f744cf 100644 --- a/docs/source/examples/notebooks/batch_study.ipynb +++ b/docs/source/examples/notebooks/batch_study.ipynb @@ -501,15 +501,15 @@ " \"Mohtat2020_3\": pybamm.ParameterValues(\"Mohtat2020\"),\n", "}\n", "\n", - "# different values for the parameter \"Inner SEI open-circuit potential [V]\"\n", + "# different values for the parameter \"Negative inner SEI open-circuit potential [V]\"\n", "inner_sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n", "\n", - "# updating the value of \"Inner SEI open-circuit potential [V]\" in all the dictionary items\n", + "# updating the value of \"Negative inner SEI open-circuit potential [V]\" in all the dictionary items\n", "for _, v, inner_sei_oc_v in zip(\n", " parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values\n", "):\n", " v.update(\n", - " {\"Inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n", + " {\"Negative inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n", " )\n", "\n", "# creating a Single Particle Model with \"electron-mitigation limited\" SEI\n", @@ -527,7 +527,7 @@ "batch_study.solve(initial_soc=1)\n", "\n", "labels = [\n", - " f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n", + " f\"Negative inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n", " for inner_sei_oc_v in inner_sei_oc_v_values\n", "]\n", "batch_study.plot(labels=labels)" @@ -627,7 +627,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -641,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" }, "toc": { "base_numbering": 1, @@ -663,5 +663,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb index e84fdbb1ac..184f12638d 100644 --- a/docs/source/examples/notebooks/models/lithium-plating.ipynb +++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb @@ -46,9 +46,9 @@ "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n", "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n", "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n", - "# parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n", - "parameter_values.update({\"Lithium plating transfer coefficient\": 0.5})\n", - "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1e-4})" + "# parameter_values.update({\"Negative lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n", + "parameter_values.update({\"Negative lithium plating transfer coefficient\": 0.5})\n", + "parameter_values.update({\"Negative dead lithium decay constant [s-1]\": 1e-4})" ] }, { @@ -366,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.10.12" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb index 1ce1cca826..dcb1dac7c5 100644 --- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb +++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb @@ -632,7 +632,7 @@ "# Changing secondary SEI solvent diffusivity to show different degradation between phases\n", "parameter_values.update(\n", " {\n", - " \"Secondary: Outer SEI solvent diffusivity [m2.s-1]\": 2.5000000000000002e-24,\n", + " \"Secondary: Negative outer SEI solvent diffusivity [m2.s-1]\": 2.5000000000000002e-24,\n", " }\n", ")\n", "\n", @@ -722,7 +722,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.10.12" }, "toc": { "base_numbering": 1, diff --git a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb index fe06dadffe..5d7bd092b4 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb @@ -57,7 +57,7 @@ "source": [ "model = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})\n", "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n", - "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})" + "parameter_values.update({\"Negative SEI kinetic rate constant [m.s-1]\": 1e-14})" ] }, { @@ -450,7 +450,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb index c7f1f0e634..a3e97facfd 100644 --- a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb +++ b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb @@ -64,7 +64,7 @@ "outputs": [], "source": [ "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n", - "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})\n", + "parameter_values.update({\"Negative SEI kinetic rate constant [m.s-1]\": 1e-14})\n", "spm = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})" ] }, @@ -1950,7 +1950,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" }, "toc": { "base_numbering": 1, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py index 4be67175d7..b47f9009df 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py @@ -14,12 +14,15 @@ def test_functions(self): fun_test = { # Lithium plating - "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3), - "Exchange-current density for stripping [A.m-2]": ( + "Exchange-current density for negative lithium plating [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-3 + ), + "Exchange-current density for negative lithium stripping [A.m-2]": ( [1e3, 1e4, T], 9.6485e-2, ), - "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), + "Negative dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Negative electrode "Negative particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Negative electrode exchange-current density [A.m-2]": ( diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py index 6000b997b7..d8db8839d9 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py @@ -13,12 +13,15 @@ def test_functions(self): fun_test = { # Lithium plating - "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-3), - "Exchange-current density for stripping [A.m-2]": ( + "Exchange-current density for positive lithium plating [A.m-2]": ( + [1e3, 1e4, T], + 9.6485e-3, + ), + "Exchange-current density for positive lithium stripping [A.m-2]": ( [1e3, 1e4, T], 9.6485e-2, ), - "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), + "Positive dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Positive electrode "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Positive electrode exchange-current density [A.m-2]": ( diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 9ffd92d2bf..49ff25de76 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -14,7 +14,7 @@ def test_functions(self): fun_test = { # Lithium plating - "Exchange-current density for negayive lithium plating [A.m-2]": ( + "Exchange-current density for negative lithium plating [A.m-2]": ( [1e3, 1e4, T], 9.6485e-2, ), From 3cda3fef58915e1a4688885bf9941c5ee22abb31 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 24 Sep 2024 15:38:03 +0000 Subject: [PATCH 18/20] style: pre-commit fixes --- .../unit/test_parameters/test_parameter_sets/test_Ecker2015.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py index b47f9009df..d5ad55c1f2 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py @@ -16,7 +16,7 @@ def test_functions(self): # Lithium plating "Exchange-current density for negative lithium plating [A.m-2]": ( [1e3, 1e4, T], - 9.6485e-3 + 9.6485e-3, ), "Exchange-current density for negative lithium stripping [A.m-2]": ( [1e3, 1e4, T], From 2127d7360ae610fbdd14015b79f895c039005e5b Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 20:24:19 +0100 Subject: [PATCH 19/20] Fixed integration tests --- .../input/parameters/lithium_ion/Chen2020_composite.py | 2 +- .../base_lithium_ion_half_cell_tests.py | 10 ++++++++-- .../test_lithium_ion/base_lithium_ion_tests.py | 4 ++-- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index 5ea1d38190..3b4a3cc2b0 100644 --- a/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/src/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -330,7 +330,7 @@ def get_parameter_values(): "Primary: Ratio of lithium moles to negative SEI moles": 2.0, "Primary: Negative inner SEI reaction proportion": 0.5, "Primary: Negative inner SEI partial molar volume [m3.mol-1]": 9.585e-05, - "Primary: Negatuve outer SEI partial molar volume [m3.mol-1]": 9.585e-05, + "Primary: Negative outer SEI partial molar volume [m3.mol-1]": 9.585e-05, "Primary: Negative SEI reaction exchange current density [A.m-2]": 1.5e-07, "Primary: Negative SEI resistivity [Ohm.m]": 200000.0, "Primary: Negative outer SEI solvent diffusivity [m2.s-1]" diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py index 5dc5b2dc94..a8107f0261 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py @@ -66,7 +66,10 @@ def test_sei_asymmetric_reaction_limited(self): options = {"SEI": "reaction limited (asymmetric)"} parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") parameter_values.update( - {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826}, + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, check_already_exists=False, ) self.run_basic_processing_test(options, parameter_values=parameter_values) @@ -91,7 +94,10 @@ def test_sei_asymmetric_ec_reaction_limited(self): options = {"SEI": "ec reaction limited (asymmetric)"} parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") parameter_values.update( - {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826}, + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, check_already_exists=False, ) self.run_basic_processing_test(options, parameter_values=parameter_values) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 76e35e2838..2b5f875bae 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -180,7 +180,7 @@ def test_sei_asymmetric_reaction_limited(self): options = {"SEI": "reaction limited (asymmetric)"} parameter_values = pybamm.ParameterValues("Marquis2019") parameter_values.update( - {"SEI growth transfer coefficient": 0.2}, + {"Negative SEI growth transfer coefficient": 0.2}, check_already_exists=False, ) self.run_basic_processing_test(options, parameter_values=parameter_values) @@ -211,7 +211,7 @@ def test_sei_asymmetric_ec_reaction_limited(self): } parameter_values = pybamm.ParameterValues("Marquis2019") parameter_values.update( - {"SEI growth transfer coefficient": 0.2}, + {"Negative SEI growth transfer coefficient": 0.2}, check_already_exists=False, ) self.run_basic_processing_test(options, parameter_values=parameter_values) From 01ed5ed8ad390a56194368bd4aa81a05037b4323 Mon Sep 17 00:00:00 2001 From: Simon O'Kane Date: Tue, 24 Sep 2024 20:51:48 +0100 Subject: [PATCH 20/20] All half-cell tests with SEI now use Ecker2015_halfcell parameters --- .../base_lithium_ion_half_cell_tests.py | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py index a8107f0261..9bdee1e565 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py @@ -60,6 +60,14 @@ def test_sei_constant(self): def test_sei_reaction_limited(self): options = {"SEI": "reaction limited"} + parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") + parameter_values.update( + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, + check_already_exists=False, + ) self.run_basic_processing_test(options) def test_sei_asymmetric_reaction_limited(self): @@ -76,18 +84,50 @@ def test_sei_asymmetric_reaction_limited(self): def test_sei_solvent_diffusion_limited(self): options = {"SEI": "solvent-diffusion limited"} + parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") + parameter_values.update( + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, + check_already_exists=False, + ) self.run_basic_processing_test(options) def test_sei_electron_migration_limited(self): options = {"SEI": "electron-migration limited"} + parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") + parameter_values.update( + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, + check_already_exists=False, + ) self.run_basic_processing_test(options) def test_sei_interstitial_diffusion_limited(self): options = {"SEI": "interstitial-diffusion limited"} + parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") + parameter_values.update( + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, + check_already_exists=False, + ) self.run_basic_processing_test(options) def test_sei_ec_reaction_limited(self): options = {"SEI": "ec reaction limited"} + parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell") + parameter_values.update( + { + "Positive SEI growth transfer coefficient": 0.2, + "Current function [A]": -0.07826, + }, + check_already_exists=False, + ) self.run_basic_processing_test(options) def test_sei_asymmetric_ec_reaction_limited(self):