From 082969750f056a5695c5299cfe946a81a5146138 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:41:48 +0200 Subject: [PATCH 01/14] Add MolicelNMC cell model Port MolicelNMC cell from legacy simses: 18650 NMC, 1.9 Ah, analytical OCV (sum-of-sigmoids + linear), 1-D Rint LUT in SOC. Legacy ships a temperature- and direction-dependent Rint CSV but every column is identical in the source data, so only the single SOC-keyed curve is bundled. Source: Schuster et al., J. Energy Storage 1 (2015) 44-53. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../model/cell/data/NMC_Molicel_Rint.csv | 102 ++++++++++++++++++ src/simses/model/cell/molicel_nmc.py | 80 ++++++++++++++ tests/test_cell_models.py | 6 ++ 3 files changed, 188 insertions(+) create mode 100644 src/simses/model/cell/data/NMC_Molicel_Rint.csv create mode 100644 src/simses/model/cell/molicel_nmc.py diff --git a/src/simses/model/cell/data/NMC_Molicel_Rint.csv b/src/simses/model/cell/data/NMC_Molicel_Rint.csv new file mode 100644 index 0000000..e9e316c --- /dev/null +++ b/src/simses/model/cell/data/NMC_Molicel_Rint.csv @@ -0,0 +1,102 @@ +SOC,Rint +0.0,0.1917073789999999 +0.01,0.1881221 +0.02,0.184454034 +0.03,0.180723681 +0.04,0.176951537 +0.05,0.1731581 +0.06,0.1693638689999999 +0.07,0.165589341 +0.08,0.161855014 +0.09,0.158181385 +0.1,0.154588954 +0.11,0.1510982169999999 +0.12,0.147729672 +0.13,0.144503818 +0.14,0.141439021 +0.15,0.138539519 +0.16,0.135803796 +0.17,0.1332303209999999 +0.18,0.130816151 +0.19,0.128553571 +0.2,0.126433863 +0.21,0.1244483109999999 +0.22,0.122588613 +0.23,0.120847198 +0.24,0.11921657 +0.25,0.117689259 +0.26,0.116258344 +0.27,0.11491745 +0.28,0.113660224 +0.29,0.112480399 +0.3,0.111372525 +0.31,0.110331599 +0.32,0.109352619 +0.33,0.108430789 +0.34,0.107562245 +0.35,0.1067433909999999 +0.36,0.10597063 +0.37,0.105240459 +0.38,0.104549591 +0.39,0.103894773 +0.4,0.103272763 +0.41,0.102680862 +0.42,0.102117065 +0.43,0.101579411 +0.44,0.101065978 +0.45,0.1005753319999999 +0.46,0.100106393 +0.47,0.0996580859999999 +0.48,0.099229347 +0.49,0.09881915 +0.5,0.098426488 +0.51,0.098050353 +0.52,0.097689902 +0.53,0.097344814 +0.54,0.097014876 +0.55,0.096699874 +0.56,0.096399712 +0.57,0.096114486 +0.58,0.0958443119999999 +0.59,0.095589304 +0.6,0.095349492 +0.61,0.095124834 +0.62,0.094915283 +0.63,0.094720779 +0.64,0.094541143 +0.65,0.094376136 +0.66,0.094225519 +0.67,0.094089023 +0.68,0.0939662469999999 +0.69,0.093856758 +0.7,0.093760123 +0.71,0.09367589 +0.72,0.093603572 +0.73,0.0935426769999999 +0.74,0.09349271 +0.75,0.093453134 +0.76,0.093423353 +0.77,0.093402772 +0.78,0.093390791 +0.79,0.093386758 +0.8,0.0933899919999999 +0.81,0.093399808 +0.82,0.093415519 +0.83,0.093436423 +0.84,0.093461809 +0.85,0.093490968 +0.86,0.093523281 +0.87,0.093558395 +0.88,0.093596009 +0.89,0.09363582 +0.9,0.0936775359999999 +0.91,0.093720875 +0.92,0.09376556 +0.93,0.09381131 +0.94,0.093857847 +0.95,0.0939048919999999 +0.96,0.093952165 +0.97,0.093999388 +0.98,0.094046281 +0.99,0.094092566 +1.0,0.094137964 diff --git a/src/simses/model/cell/molicel_nmc.py b/src/simses/model/cell/molicel_nmc.py new file mode 100644 index 0000000..e45d5a7 --- /dev/null +++ b/src/simses/model/cell/molicel_nmc.py @@ -0,0 +1,80 @@ +import math +import os + +import pandas as pd + +from simses.battery.battery import BatteryState, CellType +from simses.battery.format import RoundCell +from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties +from simses.interpolation import interp1d_scalar + + +class MolicelNMC(CellType): + """Molicel INR-18650-NMC cylindrical NMC cell. + + Nickel-manganese-cobalt oxide cell, 1.9 Ah nominal capacity, 3.7 V + nominal voltage. Analytical ``OCV(SOC)`` as a sum of sigmoids and a + linear term; internal resistance is a 1-D lookup in SOC (the source + characterisation is symmetric for charge and discharge and + temperature-independent in the tested range). + + Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., + Sextl, G., & Jossen, A. (2015). *Nonlinear aging characteristics of + lithium-ion cells under different operational conditions.* Journal of + Energy Storage, 1, 44–53, doi:10.1016/j.est.2015.05.003. + """ + + def __init__(self) -> None: + super().__init__( + electrical=ElectricalCellProperties( + nominal_capacity=1.9, # Ah + nominal_voltage=3.7, # V + max_voltage=4.25, # V + min_voltage=3.0, # V + max_charge_rate=1.05, # 1/h + max_discharge_rate=2.1, # 1/h + self_discharge_rate=0.0, + coulomb_efficiency=1.0, # p.u. + ), + thermal=ThermalCellProperties( + min_temperature=0.0, # °C + max_temperature=45.0, # °C + mass=0.045, # kg per cell + specific_heat=965, # J/kgK + convection_coefficient=15, # W/m2K + ), + cell_format=RoundCell(diameter=18, length=65), + ) + path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") + df_rint = pd.read_csv(os.path.join(path, "NMC_Molicel_Rint.csv")) + self._rint_lut_soc = df_rint["SOC"].tolist() + self._rint_lut_rint = df_rint["Rint"].tolist() + + def open_circuit_voltage(self, state: BatteryState) -> float: + a1 = -1.6206 + a2 = -6.9895 + a3 = 1.4458 + a4 = 1.9530 + b1 = 3.4206 + b2 = 0.8759 + k0 = 2.0127 + k1 = 2.7684 + k2 = 1.0698 + k3 = 4.1431 + k4 = -3.8417 + k5 = -0.1856 + + soc = state.soc + + ocv = ( + k0 + + k1 / (1 + math.exp(a1 * (soc - b1))) + + k2 / (1 + math.exp(a2 * (soc - b2))) + + k3 / (1 + math.exp(a3 * (soc - 1))) + + k4 / (1 + math.exp(a4 * soc)) + + k5 * soc + ) + return ocv + + def internal_resistance(self, state: BatteryState) -> float: + return interp1d_scalar(state.soc, self._rint_lut_soc, self._rint_lut_rint) diff --git a/tests/test_cell_models.py b/tests/test_cell_models.py index ec1365e..7fd38c2 100644 --- a/tests/test_cell_models.py +++ b/tests/test_cell_models.py @@ -12,6 +12,7 @@ from simses.battery.cell import CellType from simses.battery.state import BatteryState +from simses.model.cell.molicel_nmc import MolicelNMC from simses.model.cell.samsung94Ah_nmc import Samsung94AhNMC from simses.model.cell.sony_lfp import SonyLFP @@ -37,6 +38,11 @@ class CellModelSpec: CELL_SPECS: list[CellModelSpec] = [ + CellModelSpec( + name="MolicelNMC", + factory=MolicelNMC, + rint_varies_with_soc=True, + ), CellModelSpec( name="Samsung94AhNMC", factory=Samsung94AhNMC, From c0147fec3d15bb66c927e5e64451e55b4704a151 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:42:59 +0200 Subject: [PATCH 02/14] Add PanasonicNCA cell model Port PanasonicNCA cell from legacy simses: 18650 NCA, 2.73 Ah, conservative 0.5 C charge / 3.5 C discharge, analytical OCV (sum-of-sigmoids + linear), 1-D Rint LUT in SOC with separate charge and discharge curves. Ships without a default degradation model (no matching cyclic model available in legacy). Source: Keil et al., J. Electrochem. Soc. 163(9) (2016) A1872-A1880. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../model/cell/data/NCA_PanasonicNCR_Rint.csv | 102 ++++++++++++++++++ src/simses/model/cell/panasonic_nca.py | 81 ++++++++++++++ tests/test_cell_models.py | 7 ++ 3 files changed, 190 insertions(+) create mode 100644 src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv create mode 100644 src/simses/model/cell/panasonic_nca.py diff --git a/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv b/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv new file mode 100644 index 0000000..db6e828 --- /dev/null +++ b/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv @@ -0,0 +1,102 @@ +SOC,R_ch,R_dch +0.0,0.12,0.541090909090909 +0.01,0.117611366120219,0.4805202865013779 +0.02,0.1152227322404369,0.4199496639118459 +0.03,0.112834098360656,0.359379041322314 +0.04,0.110445464480874,0.298808418732783 +0.05,0.108056830601093,0.238237796143251 +0.06,0.105166338797814,0.2157546681118189 +0.07,0.102275846994535,0.193271540080387 +0.08,0.0993853551912568,0.170788412048955 +0.09,0.0964948633879782,0.148305284017523 +0.1,0.0936043715846995,0.125822155986091 +0.11,0.0906043817886442,0.1187407287414419 +0.12,0.0876043919925888,0.111659301496793 +0.13,0.0846044021965335,0.104577874252144 +0.14,0.0816044124004781,0.0974964470074949 +0.15,0.0786044226044228,0.090415019762846 +0.16,0.0759194103194105,0.0898518965852396 +0.17,0.0732343980343981,0.0892887734076331 +0.18,0.0705493857493858,0.0887256502300266 +0.19,0.0678643734643735,0.0881625270524202 +0.2,0.0651793611793612,0.0875994038748137 +0.21,0.065011430029135,0.0865899652260308 +0.22,0.0648434988789087,0.0855805265772479 +0.23,0.0646755677286825,0.084571087928465 +0.24,0.0645076365784563,0.0835616492796821 +0.25,0.0643397054282301,0.0825522106308992 +0.26,0.0641717742780038,0.0815427719821163 +0.27,0.0640038431277776,0.0805333333333334 +0.28,0.0638359119775514,0.0795238946845505 +0.29,0.0636679808273252,0.0785144560357676 +0.3,0.063500049677099,0.0775050173869847 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+0.75,0.0758032790662318,0.0748225589353742 +0.76,0.0773463280588179,0.0743914399965858 +0.77,0.078889377051404,0.0739603210577974 +0.78,0.0804324260439901,0.0735292021190089 +0.79,0.0819754750365763,0.0730980831802205 +0.8,0.0835185240291624,0.0726669642414321 +0.81,0.0845644132530404,0.0723820859991071 +0.82,0.0856103024769184,0.072097207756782 +0.83,0.0866561917007964,0.071812329514457 +0.84,0.0877020809246745,0.071527451272132 +0.85,0.0887479701485525,0.0712425730298069 +0.86,0.0897938593724305,0.0709576947874819 +0.87,0.0908397485963086,0.0706728165451568 +0.88,0.0918856378201866,0.0703879383028318 +0.89,0.0929315270440647,0.0701030600605068 +0.9,0.0939774162679427,0.0698181818181817 +0.91,0.0938887655502393,0.0698181818181817 +0.92,0.093800114832536,0.0698181818181817 +0.93,0.0937114641148326,0.0698181818181817 +0.94,0.0936228133971293,0.0698181818181817 +0.95,0.0935341626794259,0.0698181818181816 +0.96,0.0934455119617226,0.0698181818181816 +0.97,0.0933568612440192,0.0698181818181816 +0.98,0.0932682105263159,0.0698181818181816 +0.99,0.0931795598086125,0.0698181818181816 +1.0,0.0930909090909092,0.0698181818181816 diff --git a/src/simses/model/cell/panasonic_nca.py b/src/simses/model/cell/panasonic_nca.py new file mode 100644 index 0000000..e728f5c --- /dev/null +++ b/src/simses/model/cell/panasonic_nca.py @@ -0,0 +1,81 @@ +import math +import os + +import pandas as pd + +from simses.battery.battery import BatteryState, CellType +from simses.battery.format import RoundCell +from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties +from simses.interpolation import interp1d_scalar + + +class PanasonicNCA(CellType): + """Panasonic NCR18650 cylindrical NCA cell. + + Nickel-cobalt-aluminum oxide cell, 2.73 Ah nominal capacity, 3.6 V + nominal voltage, conservative 0.5 C charge / 3.5 C discharge. Analytical + ``OCV(SOC)`` as a sum of sigmoids and a linear term; internal resistance + is a 1-D lookup in SOC with separate charge and discharge curves. + + Source: P. Keil, S. F. Schuster, J. Wilhelm, J. Travi, A. Hauser, + R. C. Karl, A. Jossen. *Calendar aging of lithium-ion batteries.* + Journal of The Electrochemical Society 163(9) (2016) A1872-A1880, + doi:10.1149/2.0411609jes. + """ + + def __init__(self) -> None: + super().__init__( + electrical=ElectricalCellProperties( + nominal_capacity=2.73, # Ah + nominal_voltage=3.6, # V + max_voltage=4.2, # V + min_voltage=2.5, # V + max_charge_rate=0.5, # 1/h + max_discharge_rate=3.5, # 1/h + self_discharge_rate=0.0, + coulomb_efficiency=1.0, # p.u. + ), + thermal=ThermalCellProperties( + min_temperature=0.0, # °C + max_temperature=45.0, # °C + mass=0.044, # kg per cell + specific_heat=1048, # J/kgK + convection_coefficient=15, # W/m2K + ), + cell_format=RoundCell(diameter=18, length=65), + ) + path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") + df_rint = pd.read_csv(os.path.join(path, "NCA_PanasonicNCR_Rint.csv")) + self._rint_lut_soc = df_rint["SOC"].tolist() + self._rint_lut_ch = df_rint["R_ch"].tolist() + self._rint_lut_dch = df_rint["R_dch"].tolist() + + def open_circuit_voltage(self, state: BatteryState) -> float: + a1 = -0.3777 + a2 = 10.2859 + a3 = 17.0608 + a4 = -3.7820 + b1 = -5.6272 + b2 = 0.2907 + k0 = 4.9852 + k1 = -2.86523 + k2 = 0.3852 + k3 = -0.1599 + k4 = 1.2256 + k5 = 0.7412 + + soc = state.soc + + ocv = ( + k0 + + k1 / (1 + math.exp(a1 * (soc - b1))) + + k2 / (1 + math.exp(a2 * (soc - b2))) + + k3 / (1 + math.exp(a3 * (soc - 1))) + + k4 / (1 + math.exp(a4 * soc)) + + k5 * soc + ) + return ocv + + def internal_resistance(self, state: BatteryState) -> float: + lut = self._rint_lut_ch if state.is_charge else self._rint_lut_dch + return interp1d_scalar(state.soc, self._rint_lut_soc, lut) diff --git a/tests/test_cell_models.py b/tests/test_cell_models.py index 7fd38c2..931e8dc 100644 --- a/tests/test_cell_models.py +++ b/tests/test_cell_models.py @@ -13,6 +13,7 @@ from simses.battery.cell import CellType from simses.battery.state import BatteryState from simses.model.cell.molicel_nmc import MolicelNMC +from simses.model.cell.panasonic_nca import PanasonicNCA from simses.model.cell.samsung94Ah_nmc import Samsung94AhNMC from simses.model.cell.sony_lfp import SonyLFP @@ -43,6 +44,12 @@ class CellModelSpec: factory=MolicelNMC, rint_varies_with_soc=True, ), + CellModelSpec( + name="PanasonicNCA", + factory=PanasonicNCA, + rint_varies_with_soc=True, + rint_differs_charge_discharge=True, + ), CellModelSpec( name="Samsung94AhNMC", factory=Samsung94AhNMC, From 8ccdfc3ea65802443821200a27cd06f622c443a7 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:48:14 +0200 Subject: [PATCH 03/14] Pass accumulated_rinc to update_resistance Extend CalendarDegradation and CyclicDegradation protocols so update_resistance receives the accumulated resistance rise, symmetric with the existing accumulated_qloss argument on update_capacity. Unlocks aging models with non-linear R rise (e.g. sqrt-time calendar, power-law cyclic) that need virtual-time continuation. Behaviour-preserving: the NoOp stubs and SonyLFP models (linear in time / FEC) simply ignore the new argument. Tests, the extending- degradation example, and the concept + guide docs are updated for the new signature. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/concepts/degradation.md | 8 +++--- docs/guides/extending-degradation.md | 16 ++++++------ examples/extending/custom_degradation.py | 6 ++--- src/simses/degradation/calendar.py | 5 +++- src/simses/degradation/cyclic.py | 5 +++- src/simses/degradation/degradation.py | 8 +++--- .../model/degradation/sony_lfp_calendar.py | 3 ++- .../model/degradation/sony_lfp_cyclic.py | 3 ++- tests/test_degradation.py | 4 +-- tests/test_degradation_models.py | 26 ++++++++++++------- 10 files changed, 49 insertions(+), 35 deletions(-) diff --git a/docs/concepts/degradation.md b/docs/concepts/degradation.md index e518760..3771a96 100644 --- a/docs/concepts/degradation.md +++ b/docs/concepts/degradation.md @@ -11,7 +11,7 @@ A simses degradation setup is three things composed together. [`DegradationModel`][simses.degradation.degradation.DegradationModel] is the **composer**. It owns the mutable [`DegradationState`][simses.degradation.state.DegradationState] (the running ledger of accumulated damage), holds a [`HalfCycleDetector`][simses.degradation.cycle_detector.HalfCycleDetector], and delegates the actual aging laws to two stateless sub-models. At each call to `Battery.step()` the battery hands the current `BatteryState` to `DegradationModel.step(state, dt)`, which applies calendar aging and — if a half-cycle has just completed — cyclic aging, then writes the updated `soh_Q` and `soh_R` back onto `BatteryState`. -[`CalendarDegradation`][simses.degradation.calendar.CalendarDegradation] and [`CyclicDegradation`][simses.degradation.cyclic.CyclicDegradation] are **protocols** — the aging-law equivalents of `CellType`: stateless, chemistry-specific descriptions of how damage accumulates under given stress. Concrete implementations are typically **(semi-)empirical fits** to accelerated-aging measurements on a specific cell — polynomial, Arrhenius, power-law, or lookup forms calibrated to observed fade curves, not first-principles electrochemistry. A `CalendarDegradation` exposes `update_capacity(state, dt, accumulated_qloss)` and `update_resistance(state, dt)`; a `CyclicDegradation` exposes the same two methods but takes a `HalfCycle` instead of `dt`. Each call returns a *delta* (a non-negative increment), never an absolute value. +[`CalendarDegradation`][simses.degradation.calendar.CalendarDegradation] and [`CyclicDegradation`][simses.degradation.cyclic.CyclicDegradation] are **protocols** — the aging-law equivalents of `CellType`: stateless, chemistry-specific descriptions of how damage accumulates under given stress. Concrete implementations are typically **(semi-)empirical fits** to accelerated-aging measurements on a specific cell — polynomial, Arrhenius, power-law, or lookup forms calibrated to observed fade curves, not first-principles electrochemistry. A `CalendarDegradation` exposes `update_capacity(state, dt, accumulated_qloss)` and `update_resistance(state, dt, accumulated_rinc)`; a `CyclicDegradation` exposes the same two methods but takes a `HalfCycle` instead of `dt`. Each call returns a *delta* (a non-negative increment), never an absolute value. [`HalfCycleDetector`][simses.degradation.cycle_detector.HalfCycleDetector] is the **trigger**. It watches SOC across timesteps and raises a completed [`HalfCycle`][simses.degradation.cycle_detector.HalfCycle] whenever the SOC reverses direction. The `HalfCycle` carries the stress factors — depth of discharge, mean SOC, average C-rate, and full-equivalent-cycle contribution — that the cyclic model needs. @@ -59,15 +59,15 @@ At each step, `DegradationModel` sums the calendar contribution and — when the One call to `DegradationModel.step(state, dt)` runs two passes. -**Calendar pass (every step).** The calendar sub-model is asked for the capacity loss and resistance rise that accumulate over this timestep, given the current temperature and SOC. `DegradationModel` also hands it the current value of `qloss_cal` — the calendar damage already accumulated — as `accumulated_qloss`. The sub-model returns a non-negative delta, which is added to `qloss_cal` and subtracted from `state.soh_Q`. Resistance follows the same pattern through `rinc_cal` and `soh_R`. +**Calendar pass (every step).** The calendar sub-model is asked for the capacity loss and resistance rise that accumulate over this timestep, given the current temperature and SOC. `DegradationModel` also hands it the current values of `qloss_cal` and `rinc_cal` — the calendar damage already accumulated — as `accumulated_qloss` and `accumulated_rinc`. The sub-model returns non-negative deltas, which are added to the accumulators and reflected on `state.soh_Q` and `state.soh_R`. -**Cyclic pass (on direction reversal).** The cycle detector is advanced with the new SOC. If it signals a completed half-cycle, the cyclic sub-model is called with the `HalfCycle` object and the current `qloss_cyc` accumulator. Again the sub-model returns a delta, which is added to `qloss_cyc` and subtracted from `state.soh_Q`. If no half-cycle completes this step, the cyclic pass is skipped entirely. +**Cyclic pass (on direction reversal).** The cycle detector is advanced with the new SOC. If it signals a completed half-cycle, the cyclic sub-model is called with the `HalfCycle` object and the current `qloss_cyc` / `rinc_cyc` accumulators. Again the sub-model returns deltas, which are added to the accumulators and reflected on `state.soh_Q` / `state.soh_R`. If no half-cycle completes this step, the cyclic pass is skipped entirely. ### Why the accumulator is passed in Aging laws are typically nonlinear in their independent variable — calendar damage often grows as $\sqrt{t}$, $t^{0.75}$, or a double-exponential SEI form, and cyclic damage grows as $\sqrt{\text{FEC}}$ or a power law in charge throughput. Under *constant* stress these laws are straightforward to integrate. But in a real simulation, stress varies every timestep — temperature drifts, SOC swings, C-rate changes with operating profile — and a nonlinear law needs to know how much damage has already accumulated to compute the next increment correctly. -Passing `accumulated_qloss` in as an argument lets the sub-model do this reconstruction on the fly without maintaining its own internal state. The `DegradationState` on `DegradationModel` is the *only* place aging state lives, which means checkpointing, warm-starting from a prior aging history, or swapping sub-models between runs all work without any coordination between the framework and the laws. Memoryless laws (e.g. linear-in-time calendar) are free to ignore the accumulator entirely. +Passing `accumulated_qloss` / `accumulated_rinc` in as arguments lets the sub-model do this reconstruction on the fly without maintaining its own internal state. The `DegradationState` on `DegradationModel` is the *only* place aging state lives, which means checkpointing, warm-starting from a prior aging history, or swapping sub-models between runs all work without any coordination between the framework and the laws. Memoryless laws (e.g. linear-in-time calendar) are free to ignore the accumulators entirely. The concrete example below walks through one common continuation technique — virtual-time reconstruction — as used by the Sony LFP calendar model. diff --git a/docs/guides/extending-degradation.md b/docs/guides/extending-degradation.md index 80077e8..9e312a7 100644 --- a/docs/guides/extending-degradation.md +++ b/docs/guides/extending-degradation.md @@ -15,12 +15,12 @@ Fires **every timestep** — even when the battery is idle. ```python def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: ... -def update_resistance(self, state: BatteryState, dt: float) -> float: ... +def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: ... ``` - `state` — current battery state (read SOC, T, etc.). - `dt` — timestep in seconds. -- `accumulated_qloss` — calendar capacity loss accumulated so far (p.u., ≥ 0). Your model reads this to continue a non-linear aging law under varying stress; memoryless laws can ignore it. +- `accumulated_qloss` / `accumulated_rinc` — calendar capacity loss and resistance increase accumulated so far (p.u., ≥ 0). Your model reads these to continue a non-linear aging law under varying stress; memoryless laws (linear in time) can ignore them. - Returns a **non-negative delta** — never an absolute value. ### `CyclicDegradation` @@ -29,16 +29,16 @@ Fires **only on completed half-cycles** — `DegradationModel` delegates to the ```python def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: ... -def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: ... +def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: ... ``` - `half_cycle` — a [`HalfCycle`][simses.degradation.cycle_detector.HalfCycle] carrying `depth_of_discharge`, `mean_soc`, `c_rate`, and `full_equivalent_cycles`. -- Same `accumulated_qloss` pattern on the capacity side. +- Same accumulator pattern on both sides — virtual-FEC continuation available when the law is non-linear in throughput. - Same delta-only return convention. ### The statelessness rule -Both sub-models must be **stateless**. All accumulators live on the [`DegradationState`][simses.degradation.state.DegradationState] that `DegradationModel` owns. The framework passes `accumulated_qloss` into `update_capacity` so your model can reconstruct history without storing anything internally; resistance rise doesn't accumulate the same way (most rise laws are memoryless in their independent variable). +Both sub-models must be **stateless**. All accumulators live on the [`DegradationState`][simses.degradation.state.DegradationState] that `DegradationModel` owns. The framework passes `accumulated_qloss` into `update_capacity` and `accumulated_rinc` into `update_resistance` so your model can reconstruct history without storing anything internally. Memoryless laws (linear-in-time calendar R rise, linear-in-FEC cyclic R rise) are free to ignore the accumulator. This rule keeps checkpointing, warm-starts, and sub-model swapping trivial — the only state lives in one place. @@ -68,11 +68,11 @@ class SqrtTimeCalendar: t_virt = (accumulated_qloss / stress) ** 2 return stress * math.sqrt(t_virt + dt) - accumulated_qloss - def update_resistance(self, state: BatteryState, dt: float) -> float: + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: return 1e-8 * self._stress(state.T) / self.K_REF * dt ``` -The capacity method inverts the √t law at each call to find the *virtual* time that would have produced `accumulated_qloss` under the *current* stress, then steps forward — so T can change between steps without double-counting. If your law is linear in time (`dq = k · dt`), just ignore `accumulated_qloss` and return `k(state) · dt`. If it follows a different exponent (`t^0.75`, SEI double-exponential, etc.), apply the same inversion principle with the right formula. +The capacity method inverts the √t law at each call to find the *virtual* time that would have produced `accumulated_qloss` under the *current* stress, then steps forward — so T can change between steps without double-counting. If your law is linear in time (`dq = k · dt`), just ignore the accumulator and return `k(state) · dt`. If it follows a different exponent (`t^0.75`, SEI double-exponential, etc.), apply the same inversion principle with the right formula. The same principle applies to `update_resistance` via `accumulated_rinc` when the R-rise law is non-linear in time. **Cyclic** — `Δq_cyc = K_CYC · DoD² · ΔFEC` per completed half-cycle, no memory across cycles: @@ -87,7 +87,7 @@ class DodSquaredCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return self.K_CYC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: return self.K_RINC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles ``` diff --git a/examples/extending/custom_degradation.py b/examples/extending/custom_degradation.py index e7d257f..8144283 100644 --- a/examples/extending/custom_degradation.py +++ b/examples/extending/custom_degradation.py @@ -61,8 +61,8 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo t_virt = (accumulated_qloss / stress) ** 2 return stress * math.sqrt(t_virt + dt) - accumulated_qloss - def update_resistance(self, state: BatteryState, dt: float) -> float: - """Resistance rise — linear in time, simple memoryless model.""" + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + """Resistance rise — linear in time, simple memoryless model (``accumulated_rinc`` unused).""" return 1e-8 * self._stress(state.T) / self.K_REF * dt @@ -83,7 +83,7 @@ class DodSquaredCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return self.K_CYC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: return self.K_RINC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles diff --git a/src/simses/degradation/calendar.py b/src/simses/degradation/calendar.py index 851ecd1..ee3a075 100644 --- a/src/simses/degradation/calendar.py +++ b/src/simses/degradation/calendar.py @@ -28,12 +28,15 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo """ ... - def update_resistance(self, state: BatteryState, dt: float) -> float: + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: """Compute incremental calendar resistance increase. Args: state: Current battery state. dt: Timestep in seconds. + accumulated_rinc: Calendar resistance increase accumulated so far + (p.u., positive), used to seed virtual-time continuation. + Memoryless laws (e.g. linear-in-time) can ignore it. Returns: delta_soh_R — positive increment in p.u. (resistance increases). diff --git a/src/simses/degradation/cyclic.py b/src/simses/degradation/cyclic.py index 47fb09f..15138aa 100644 --- a/src/simses/degradation/cyclic.py +++ b/src/simses/degradation/cyclic.py @@ -29,12 +29,15 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate """ ... - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: """Compute incremental cyclic resistance increase for a completed half-cycle. Args: state: Current battery state. half_cycle: Stress factors of the completed half-cycle. + accumulated_rinc: Cyclic resistance increase accumulated so far + (p.u., positive), used to seed virtual-FEC continuation. + Memoryless laws (e.g. linear-in-FEC) can ignore it. Returns: delta_soh_R — positive increment in p.u. (resistance increases). diff --git a/src/simses/degradation/degradation.py b/src/simses/degradation/degradation.py index 4942b46..5c8ad3a 100644 --- a/src/simses/degradation/degradation.py +++ b/src/simses/degradation/degradation.py @@ -11,7 +11,7 @@ class _NoOpCalendar: def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: return 0.0 - def update_resistance(self, state: BatteryState, dt: float) -> float: + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: return 0.0 @@ -21,7 +21,7 @@ class _NoOpCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return 0.0 - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: return 0.0 @@ -86,7 +86,7 @@ def step(self, state: BatteryState, dt: float) -> None: """ # Calendar aging dq_cal = self.calendar.update_capacity(state, dt, self.state.qloss_cal) - dr_cal = self.calendar.update_resistance(state, dt) + dr_cal = self.calendar.update_resistance(state, dt, self.state.rinc_cal) self.state.qloss_cal += dq_cal self.state.rinc_cal += dr_cal state.soh_Q -= dq_cal @@ -96,7 +96,7 @@ def step(self, state: BatteryState, dt: float) -> None: if self.cycle_detector.step(state.soc, dt): half_cycle = self.cycle_detector.last_cycle dq_cyc = self.cyclic.update_capacity(state, half_cycle, self.state.qloss_cyc) - dr_cyc = self.cyclic.update_resistance(state, half_cycle) + dr_cyc = self.cyclic.update_resistance(state, half_cycle, self.state.rinc_cyc) self.state.qloss_cyc += dq_cyc self.state.rinc_cyc += dr_cyc state.soh_Q -= dq_cyc diff --git a/src/simses/model/degradation/sony_lfp_calendar.py b/src/simses/model/degradation/sony_lfp_calendar.py index 34e2bcc..62860ab 100644 --- a/src/simses/model/degradation/sony_lfp_calendar.py +++ b/src/simses/model/degradation/sony_lfp_calendar.py @@ -56,7 +56,8 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo return delta_q - def update_resistance(self, state: BatteryState, dt: float) -> float: + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + # accumulated_rinc is unused: this model is linear-in-time, no virtual-time needed. if dt == 0.0: return 0.0 diff --git a/src/simses/model/degradation/sony_lfp_cyclic.py b/src/simses/model/degradation/sony_lfp_cyclic.py index 4e04648..65cedb9 100644 --- a/src/simses/model/degradation/sony_lfp_cyclic.py +++ b/src/simses/model/degradation/sony_lfp_cyclic.py @@ -51,7 +51,8 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate return delta_q - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + # accumulated_rinc is unused: this model is linear-in-FEC, no virtual-FEC needed. delta_fec = half_cycle.full_equivalent_cycles if delta_fec == 0.0: return 0.0 diff --git a/tests/test_degradation.py b/tests/test_degradation.py index fbb02b5..108988b 100644 --- a/tests/test_degradation.py +++ b/tests/test_degradation.py @@ -26,7 +26,7 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo self.call_count += 1 return self.dq - def update_resistance(self, state: BatteryState, dt: float) -> float: + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: return self.dr @@ -42,7 +42,7 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate self.call_count += 1 return self.dq - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: return self.dr diff --git a/tests/test_degradation_models.py b/tests/test_degradation_models.py index f800076..1be6ff8 100644 --- a/tests/test_degradation_models.py +++ b/tests/test_degradation_models.py @@ -110,14 +110,14 @@ def test_capacity_loss_positive(self, cal_model): def test_delta_soh_r_positive(self, cal_model): """Calendar aging should increase resistance (delta_soh_R > 0).""" state = _make_state() - dr = cal_model.update_resistance(state, dt=3600.0) + dr = cal_model.update_resistance(state, dt=3600.0, accumulated_rinc=0.0) assert dr > 0 def test_zero_dt_zero_change(self, cal_model): """Zero timestep should produce zero degradation.""" state = _make_state() assert cal_model.update_capacity(state, dt=0.0, accumulated_qloss=0.0) == 0.0 - assert cal_model.update_resistance(state, dt=0.0) == 0.0 + assert cal_model.update_resistance(state, dt=0.0, accumulated_rinc=0.0) == 0.0 def test_longer_time_more_degradation(self, cal_model): """More time should produce more capacity loss.""" @@ -145,7 +145,7 @@ def test_delta_soh_r_positive(self, cyc_model): """Cyclic aging should increase resistance (delta_soh_R > 0).""" state = _make_state() hc = _make_half_cycle() - dr = cyc_model.update_resistance(state, hc) + dr = cyc_model.update_resistance(state, hc, accumulated_rinc=0.0) assert dr > 0 def test_zero_fec_zero_change(self, cyc_model): @@ -153,7 +153,7 @@ def test_zero_fec_zero_change(self, cyc_model): state = _make_state() hc = HalfCycle(depth_of_discharge=0.0, mean_soc=0.5, c_rate=0.5, full_equivalent_cycles=0.0) assert cyc_model.update_capacity(state, hc, accumulated_qloss=0.0) == 0.0 - assert cyc_model.update_resistance(state, hc) == 0.0 + assert cyc_model.update_resistance(state, hc, accumulated_rinc=0.0) == 0.0 # =================================================================== @@ -176,8 +176,8 @@ def test_higher_temperature_more_rinc(self): model_hot = SonyLFPCalendarDegradation() state_cold = _make_state(T=5.0) state_hot = _make_state(T=45.0) - dr_cold = model_cold.update_resistance(state_cold, dt=86400.0) - dr_hot = model_hot.update_resistance(state_hot, dt=86400.0) + dr_cold = model_cold.update_resistance(state_cold, dt=86400.0, accumulated_rinc=0.0) + dr_hot = model_hot.update_resistance(state_hot, dt=86400.0, accumulated_rinc=0.0) assert dr_hot > dr_cold def test_sqrt_time_behavior(self): @@ -199,17 +199,20 @@ def test_accumulated_loss_continuity(self): total_time = 86400.0 # 1 day dq_single = model.update_capacity(state, dt=total_time, accumulated_qloss=0.0) - dr_single = model.update_resistance(state, dt=total_time) + dr_single = model.update_resistance(state, dt=total_time, accumulated_rinc=0.0) n_steps = 100 accumulated_qloss = 0.0 + accumulated_rinc = 0.0 dq_total = 0.0 dr_total = 0.0 for _ in range(n_steps): dq = model.update_capacity(state, dt=total_time / n_steps, accumulated_qloss=accumulated_qloss) accumulated_qloss += dq dq_total += dq - dr_total += model.update_resistance(state, dt=total_time / n_steps) + dr = model.update_resistance(state, dt=total_time / n_steps, accumulated_rinc=accumulated_rinc) + accumulated_rinc += dr + dr_total += dr assert dq_total == pytest.approx(dq_single, rel=0.02) assert dr_total == pytest.approx(dr_single, rel=0.02) @@ -258,10 +261,11 @@ def test_accumulated_loss_continuity(self): total_fec = 1.0 hc_single = HalfCycle(depth_of_discharge=0.5, mean_soc=0.5, c_rate=0.5, full_equivalent_cycles=total_fec) dq_single = model.update_capacity(state, hc_single, accumulated_qloss=0.0) - dr_single = model.update_resistance(state, hc_single) + dr_single = model.update_resistance(state, hc_single, accumulated_rinc=0.0) n_steps = 100 accumulated_qloss = 0.0 + accumulated_rinc = 0.0 dq_total = 0.0 dr_total = 0.0 for _ in range(n_steps): @@ -274,7 +278,9 @@ def test_accumulated_loss_continuity(self): dq = model.update_capacity(state, hc, accumulated_qloss=accumulated_qloss) accumulated_qloss += dq dq_total += dq - dr_total += model.update_resistance(state, hc) + dr = model.update_resistance(state, hc, accumulated_rinc=accumulated_rinc) + accumulated_rinc += dr + dr_total += dr assert dq_total == pytest.approx(dq_single, rel=0.02) assert dr_total == pytest.approx(dr_single, rel=0.02) From 63b43d2a7afc13353994a94c11919e08dca591db Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:52:51 +0200 Subject: [PATCH 04/14] Add MolicelNMC default degradation model MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Port Molicel NMC calendar + cyclic aging from legacy simses: - Calendar: t^0.75 capacity fade and sqrt(t) resistance rise with virtual-time continuation. Stress factors are 2-D lookups over (SOC, T) valid in T ∈ [10, 50] °C. - Cyclic: Q^0.5562 power law in charge throughput with virtual- throughput continuation for both capacity fade and resistance rise. Stress factors are 1-D lookups over DoD. The legacy model's asymmetric C-rate branching (separate coefficients above 0.5 C for charge vs discharge) is omitted — it requires charge/discharge direction on the HalfCycle, which the detector does not expose. DoD is the dominant stress factor and is preserved. Wired up as MolicelNMC.default_degradation_model so `Battery(MolicelNMC(), ..., degradation=True)` picks it up. Source: Ni Chuanqin (EES, TUM); Schmalstieg et al. 2014. Co-Authored-By: Claude Opus 4.7 (1M context) --- src/simses/model/cell/molicel_nmc.py | 24 +- .../data/NMC_Molicel_capacity_cal.csv | 102 ++ .../data/NMC_Molicel_capacity_cyc.csv | 1002 +++++++++++++++++ .../degradation/data/NMC_Molicel_ri_cal.csv | 102 ++ .../degradation/data/NMC_Molicel_ri_cyc.csv | 1002 +++++++++++++++++ .../model/degradation/molicel_nmc_calendar.py | 69 ++ .../model/degradation/molicel_nmc_cyclic.py | 82 ++ tests/test_degradation_models.py | 4 + 8 files changed, 2386 insertions(+), 1 deletion(-) create mode 100644 src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv create mode 100644 src/simses/model/degradation/data/NMC_Molicel_capacity_cyc.csv create mode 100644 src/simses/model/degradation/data/NMC_Molicel_ri_cal.csv create mode 100644 src/simses/model/degradation/data/NMC_Molicel_ri_cyc.csv create mode 100644 src/simses/model/degradation/molicel_nmc_calendar.py create mode 100644 src/simses/model/degradation/molicel_nmc_cyclic.py diff --git a/src/simses/model/cell/molicel_nmc.py b/src/simses/model/cell/molicel_nmc.py index e45d5a7..8f7bdad 100644 --- a/src/simses/model/cell/molicel_nmc.py +++ b/src/simses/model/cell/molicel_nmc.py @@ -6,7 +6,11 @@ from simses.battery.battery import BatteryState, CellType from simses.battery.format import RoundCell from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties +from simses.degradation import DegradationModel +from simses.degradation.state import DegradationState from simses.interpolation import interp1d_scalar +from simses.model.degradation.molicel_nmc_calendar import MolicelNMCCalendarDegradation +from simses.model.degradation.molicel_nmc_cyclic import MolicelNMCCyclicDegradation class MolicelNMC(CellType): @@ -16,7 +20,12 @@ class MolicelNMC(CellType): nominal voltage. Analytical ``OCV(SOC)`` as a sum of sigmoids and a linear term; internal resistance is a 1-D lookup in SOC (the source characterisation is symmetric for charge and discharge and - temperature-independent in the tested range). + temperature-independent in the tested range). Ships a default + degradation model + (:class:`~simses.model.degradation.molicel_nmc_calendar.MolicelNMCCalendarDegradation` + + :class:`~simses.model.degradation.molicel_nmc_cyclic.MolicelNMCCyclicDegradation`) + that :class:`~simses.battery.battery.Battery` picks up when constructed + with ``degradation=True``. Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., Sextl, G., & Jossen, A. (2015). *Nonlinear aging characteristics of @@ -78,3 +87,16 @@ def open_circuit_voltage(self, state: BatteryState) -> float: def internal_resistance(self, state: BatteryState) -> float: return interp1d_scalar(state.soc, self._rint_lut_soc, self._rint_lut_rint) + + @classmethod + def default_degradation_model( + cls, + initial_soc: float, + initial_state: DegradationState | None = None, + ) -> DegradationModel: + return DegradationModel( + calendar=MolicelNMCCalendarDegradation(), + cyclic=MolicelNMCCyclicDegradation(), + initial_soc=initial_soc, + initial_state=initial_state, + ) diff --git a/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv b/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv new file mode 100644 index 0000000..5ae884e --- /dev/null +++ b/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv @@ -0,0 +1,102 @@ 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lookup tables over (SOC, T). Ni Chuanqin (EES, TUM). +""" + +import os + +import pandas as pd + +from simses.battery.state import BatteryState +from simses.degradation.calendar import CalendarDegradation +from simses.interpolation import interp2d_scalar + +_SEC_PER_WEEK = 86400.0 * 7.0 + + +def _load_stress_matrix(filename: str) -> tuple[list[float], list[float], list[list[float]]]: + path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", filename) + df = pd.read_csv(path) + soc_lut = df["SOC"].tolist() + T_lut = df["Temp"].dropna().tolist() + mat = df.iloc[:, 2 : 2 + len(T_lut)].values.tolist() + return soc_lut, T_lut, mat + + +class MolicelNMCCalendarDegradation(CalendarDegradation): + """Calendar aging for Molicel INR-18650-NMC cells. + + Capacity loss follows a ``t^0.75`` power law with virtual-time + continuation; resistance rise follows a ``sqrt(t)`` law with + virtual-time continuation. Both stress factors are 2-D lookups over + ``(SOC, T)`` valid in the range T ∈ [10, 50] °C and SOC ∈ [0, 1]. + Out-of-range inputs raise ``ValueError`` from the interpolation + helper. + + This model is **stateless**: accumulated values are owned by the + :class:`~simses.degradation.degradation.DegradationModel` and passed + in on every call. + """ + + def __init__(self) -> None: + self._soc_lut, self._T_lut, self._cap_mat = _load_stress_matrix("NMC_Molicel_capacity_cal.csv") + _, _, self._ri_mat = _load_stress_matrix("NMC_Molicel_ri_cal.csv") + + def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: + if dt == 0.0: + return 0.0 + + k_q = interp2d_scalar(state.soc, state.T, self._soc_lut, self._T_lut, self._cap_mat) + if k_q <= 0.0: + return 0.0 + + dt_weeks = dt / _SEC_PER_WEEK + virtual_weeks = (accumulated_qloss / k_q) ** (4.0 / 3.0) + return k_q * (virtual_weeks + dt_weeks) ** 0.75 - accumulated_qloss + + def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + if dt == 0.0: + return 0.0 + + k_r = interp2d_scalar(state.soc, state.T, self._soc_lut, self._T_lut, self._ri_mat) + if k_r <= 0.0: + return 0.0 + + dt_weeks = dt / _SEC_PER_WEEK + virtual_weeks = (accumulated_rinc / k_r) ** 2 + return k_r * (virtual_weeks + dt_weeks) ** 0.5 - accumulated_rinc diff --git a/src/simses/model/degradation/molicel_nmc_cyclic.py b/src/simses/model/degradation/molicel_nmc_cyclic.py new file mode 100644 index 0000000..7776263 --- /dev/null +++ b/src/simses/model/degradation/molicel_nmc_cyclic.py @@ -0,0 +1,82 @@ +"""Cyclic degradation model for Molicel INR-18650-NMC cells. + +Source: parameterised from accelerated-aging measurements adapted to the +same structure as the Naumann SonyLFP cyclic law, with stress factors +bundled as 1-D lookup tables over DoD. Ni Chuanqin (EES, TUM). +""" + +import os + +import pandas as pd + +from simses.battery.state import BatteryState +from simses.degradation.cycle_detector import HalfCycle +from simses.degradation.cyclic import CyclicDegradation +from simses.interpolation import interp1d_scalar + +# Nominal single-cell capacity (Ah). The legacy cyclic law uses charge +# throughput in Ah as its independent variable; we reconstruct it from +# the half-cycle's depth-of-discharge. +_NOMINAL_CAPACITY_AH = 1.9 + +# Power-law exponents (dimensionless). +_EXPONENT_QLOSS = 0.5562 +_EXPONENT_RINC = 0.5562 + + +def _load_1d_stress(filename: str, column: str) -> tuple[list[float], list[float]]: + path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", filename) + df = pd.read_csv(path) + return df["DOD"].tolist(), df[column].tolist() + + +class MolicelNMCCyclicDegradation(CyclicDegradation): + """Cyclic aging for Molicel INR-18650-NMC cells. + + Capacity loss follows a power law in charge throughput ``Q^0.5562`` + with virtual-throughput continuation; resistance rise follows the + same power law. The stress factors are 1-D lookups over DoD. Charge + throughput per half-cycle is reconstructed as ``DoD * 1.9 Ah`` using + the Molicel's nominal cell capacity. + + The legacy model also includes an asymmetric C-rate scaling + (separate coefficients above 0.5 C for charge vs discharge). That + branching requires charge/discharge direction on the + :class:`HalfCycle`, which the simses-lite detector does not expose; + the scaling is therefore omitted here. DoD is the dominant stress + factor and is preserved. + + This model is **stateless**: accumulated values are owned by the + :class:`~simses.degradation.degradation.DegradationModel` and passed + in on every call. + """ + + def __init__(self) -> None: + self._dod_lut_cap, self._cap_stress = _load_1d_stress("NMC_Molicel_capacity_cyc.csv", "f_capacity_cyc") + self._dod_lut_ri, self._ri_stress = _load_1d_stress("NMC_Molicel_ri_cyc.csv", "f_ri_cyc") + + def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: + dod = half_cycle.depth_of_discharge + if dod == 0.0: + return 0.0 + + k_q = interp1d_scalar(dod, self._dod_lut_cap, self._cap_stress) + if k_q <= 0.0: + return 0.0 + + throughput_ah = dod * _NOMINAL_CAPACITY_AH + virtual_q = (accumulated_qloss / k_q) ** (1.0 / _EXPONENT_QLOSS) + return max(0.0, k_q * (virtual_q + throughput_ah) ** _EXPONENT_QLOSS - accumulated_qloss) + + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + dod = half_cycle.depth_of_discharge + if dod == 0.0: + return 0.0 + + k_r = interp1d_scalar(dod, self._dod_lut_ri, self._ri_stress) + if k_r <= 0.0: + return 0.0 + + throughput_ah = dod * _NOMINAL_CAPACITY_AH + virtual_q = (accumulated_rinc / k_r) ** (1.0 / _EXPONENT_RINC) + return max(0.0, k_r * (virtual_q + throughput_ah) ** _EXPONENT_RINC - accumulated_rinc) diff --git a/tests/test_degradation_models.py b/tests/test_degradation_models.py index 1be6ff8..c3afbcf 100644 --- a/tests/test_degradation_models.py +++ b/tests/test_degradation_models.py @@ -11,6 +11,8 @@ from simses.battery.state import BatteryState from simses.degradation.cycle_detector import HalfCycle +from simses.model.degradation.molicel_nmc_calendar import MolicelNMCCalendarDegradation +from simses.model.degradation.molicel_nmc_cyclic import MolicelNMCCyclicDegradation from simses.model.degradation.sony_lfp_calendar import SonyLFPCalendarDegradation from simses.model.degradation.sony_lfp_cyclic import SonyLFPCyclicDegradation @@ -59,6 +61,7 @@ class CalendarModelSpec: CALENDAR_SPECS = [ + CalendarModelSpec(name="MolicelNMCCalendar", factory=MolicelNMCCalendarDegradation), CalendarModelSpec(name="SonyLFPCalendar", factory=SonyLFPCalendarDegradation), ] @@ -70,6 +73,7 @@ class CyclicModelSpec: CYCLIC_SPECS = [ + CyclicModelSpec(name="MolicelNMCCyclic", factory=MolicelNMCCyclicDegradation), CyclicModelSpec(name="SonyLFPCyclic", factory=SonyLFPCyclicDegradation), ] From 53a0a0a75a87f186d0579a6456533ceb67d9b4b0 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:55:03 +0200 Subject: [PATCH 05/14] Add Notton converter loss model MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Port Notton generic parametric PV-inverter loss model: η(p) = p / (p + P0 + K·p²), with three coefficient presets (Type 1, 2, 3) from the reference paper and support for user-supplied coefficients. Follows the SinamicsS120Fit pattern — sample the fit at 201 points (101 per direction, mirrored about zero) at construction, interpolate at runtime so ac_to_dc and dc_to_ac stay numerically invertible. Source: Notton et al., Renewable Energy 35(2) (2010) 541-554. Co-Authored-By: Claude Opus 4.7 (1M context) --- src/simses/model/converter/notton.py | 57 ++++++++++++++++++++++++++++ tests/test_converter_models.py | 9 +++++ 2 files changed, 66 insertions(+) create mode 100644 src/simses/model/converter/notton.py diff --git a/src/simses/model/converter/notton.py b/src/simses/model/converter/notton.py new file mode 100644 index 0000000..2f63ed6 --- /dev/null +++ b/src/simses/model/converter/notton.py @@ -0,0 +1,57 @@ +import numpy as np + +from simses.interpolation import interp1d_scalar + + +class Notton: + """Generic parametric PV-inverter loss model. + + Efficiency curve of the form ``η(p) = p / (p + P0 + K·p²)`` where ``p`` + is the magnitude of normalised power (p.u. of the converter's rated + max power). The fit is sampled at 201 points (101 per direction, + mirrored about zero) at construction and interpolated at runtime, so + ``ac_to_dc`` and ``dc_to_ac`` remain numerical inverses of each other. + + Three coefficient sets are published in Notton et al. (2010): + ``TYPE_1`` (P0=0.0145, K=0.0437), ``TYPE_2`` (P0=0.0072, K=0.0345, + used by default here), ``TYPE_3`` (P0=0.0088, K=0.1149). Custom + coefficients can also be supplied directly. + + Source: Notton, G.; Lazarov, V.; Stoyanov, L. (2010). *Optimal sizing + of a grid-connected PV system for various PV module technologies and + inclinations, inverter efficiency characteristics and locations.* + Renewable Energy 35(2) 541–554, doi:10.1016/j.renene.2009.07.013. + """ + + TYPE_1 = (0.0145, 0.0437) + TYPE_2 = (0.0072, 0.0345) + TYPE_3 = (0.0088, 0.1149) + + def __init__(self, coefficients: tuple[float, float] = TYPE_2) -> None: + """ + Args: + coefficients: ``(P0, K)`` tuple of Notton fit coefficients. + Defaults to the published Type-2 inverter parameters. + """ + P0, K = coefficients + + # Evaluate at non-zero magnitudes only; index 0 (p=0) is handled as output=0. + p = np.linspace(0, 1, 101) + eff = np.zeros_like(p) + eff[1:] = p[1:] / (p[1:] + P0 + K * p[1:] ** 2) + + input_ch = p + output_ch = input_ch * eff + + input_dch = -p + output_dch = np.zeros_like(p) + output_dch[1:] = input_dch[1:] / eff[1:] + + self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() + self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + + def ac_to_dc(self, power_ac: float) -> float: + return interp1d_scalar(power_ac, self._inp, self._out) + + def dc_to_ac(self, power_dc: float) -> float: + return interp1d_scalar(power_dc, self._out, self._inp) diff --git a/tests/test_converter_models.py b/tests/test_converter_models.py index 7968a39..306a927 100644 --- a/tests/test_converter_models.py +++ b/tests/test_converter_models.py @@ -11,6 +11,7 @@ import pytest from simses.model.converter.fix_efficiency import FixedEfficiency +from simses.model.converter.notton import Notton from simses.model.converter.sinamics import SinamicsS120, SinamicsS120Fit @@ -34,6 +35,14 @@ class ConverterModelSpec: name="FixedEfficiency_Asymmetric", factory=lambda: FixedEfficiency((0.96, 0.94)), ), + ConverterModelSpec( + name="Notton_Type2", + factory=Notton, + ), + ConverterModelSpec( + name="Notton_Type1", + factory=lambda: Notton(Notton.TYPE_1), + ), ConverterModelSpec( name="SinamicsS120", factory=SinamicsS120, From 29cc02eeef921eb87d625b885b7784ad107abf9d Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:56:00 +0200 Subject: [PATCH 06/14] Add Bonfiglioli RPS TL-4Q converter loss model MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Port Bonfiglioli RPS TL-4Q converter: Notton-form fit with asymmetric charge/discharge coefficients and a minimum-efficiency floor. Two published coefficient sets are provided: - DATASHEET (default): manufacturer datasheet, symmetric ch/dch. - FIELD_DATA: measured on FCR battery systems, asymmetric; reflects real deployment losses including auxiliary consumption. Source: F. Müller (M.Sc. thesis, TUM); Bonfiglioli RPS TL-4Q datasheet. Co-Authored-By: Claude Opus 4.7 (1M context) --- src/simses/model/converter/bonfiglioli.py | 59 +++++++++++++++++++++++ tests/test_converter_models.py | 9 ++++ 2 files changed, 68 insertions(+) create mode 100644 src/simses/model/converter/bonfiglioli.py diff --git a/src/simses/model/converter/bonfiglioli.py b/src/simses/model/converter/bonfiglioli.py new file mode 100644 index 0000000..54f1d08 --- /dev/null +++ b/src/simses/model/converter/bonfiglioli.py @@ -0,0 +1,59 @@ +import numpy as np + +from simses.interpolation import interp1d_scalar + + +class Bonfiglioli: + """Bonfiglioli RPS TL-4Q converter loss model. + + Notton-form efficiency fit with asymmetric charge and discharge + coefficients and a minimum-efficiency floor that clips the fit at + low normalised power. Two parameter sets are published: + + * :attr:`DATASHEET` (default) — manufacturer datasheet measurements. + Symmetric: ``P0=0.0072, K=0.034, min_eff=0.5813`` for both + directions. + * :attr:`FIELD_DATA` — measured on FCR battery systems. Asymmetric: + charge ``P0=0.00195, K=0.01349, min_eff=0.3441``; discharge + ``P0=0.00292, K=0.03609, min_eff=0.2742``. Reflects real + deployment losses including auxiliary consumption. + + Source: field fit and datasheet reading by F. Müller (M.Sc. thesis, + TUM), from the + `Bonfiglioli RPS TL-4Q datasheet + `_. + """ + + # (P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, min_eff_dch) + DATASHEET = (0.0072, 0.034, 0.5813, 0.0072, 0.034, 0.5813) + FIELD_DATA = (0.00195, 0.01349, 0.3441, 0.00292, 0.03609, 0.2742) + + def __init__(self, coefficients: tuple[float, float, float, float, float, float] = DATASHEET) -> None: + """ + Args: + coefficients: ``(P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, + min_eff_dch)`` tuple. Defaults to :attr:`DATASHEET`. + """ + P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, min_eff_dch = coefficients + + p = np.linspace(0, 1, 101) + + eff_ch = np.zeros_like(p) + eff_ch[1:] = np.maximum(min_eff_ch, p[1:] / (p[1:] + P0_ch + K_ch * p[1:] ** 2)) + input_ch = p + output_ch = input_ch * eff_ch + + eff_dch = np.zeros_like(p) + eff_dch[1:] = np.maximum(min_eff_dch, p[1:] / (p[1:] + P0_dch + K_dch * p[1:] ** 2)) + input_dch = -p + output_dch = np.zeros_like(p) + output_dch[1:] = input_dch[1:] / eff_dch[1:] + + self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() + self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + + def ac_to_dc(self, power_ac: float) -> float: + return interp1d_scalar(power_ac, self._inp, self._out) + + def dc_to_ac(self, power_dc: float) -> float: + return interp1d_scalar(power_dc, self._out, self._inp) diff --git a/tests/test_converter_models.py b/tests/test_converter_models.py index 306a927..87fd5be 100644 --- a/tests/test_converter_models.py +++ b/tests/test_converter_models.py @@ -10,6 +10,7 @@ import pytest +from simses.model.converter.bonfiglioli import Bonfiglioli from simses.model.converter.fix_efficiency import FixedEfficiency from simses.model.converter.notton import Notton from simses.model.converter.sinamics import SinamicsS120, SinamicsS120Fit @@ -43,6 +44,14 @@ class ConverterModelSpec: name="Notton_Type1", factory=lambda: Notton(Notton.TYPE_1), ), + ConverterModelSpec( + name="Bonfiglioli_Datasheet", + factory=Bonfiglioli, + ), + ConverterModelSpec( + name="Bonfiglioli_FieldData", + factory=lambda: Bonfiglioli(Bonfiglioli.FIELD_DATA), + ), ConverterModelSpec( name="SinamicsS120", factory=SinamicsS120, From 882deb6ad46d4eeea3bcee1fe07924063b91cabe Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 13:57:03 +0200 Subject: [PATCH 07/14] Add Sungrow SC1000TL converter loss model MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Port Sungrow SC1000TL manufacturer-specific fit with asymmetric ch/dch coefficients backed by field data from an FCR BESS. Three fit families selectable via the ``fit`` constructor argument: - notton (default): classic Notton form, with a 0.2092 minimum efficiency floor on the discharge branch. - rampinelli: three-parameter loss polynomial, same discharge floor. - rational: direct rational efficiency curve, no floor needed. Source: field fit by F. Müller (M.Sc. thesis, TUM). Co-Authored-By: Claude Opus 4.7 (1M context) --- src/simses/model/converter/sungrow.py | 104 ++++++++++++++++++++++++++ tests/test_converter_models.py | 13 ++++ 2 files changed, 117 insertions(+) create mode 100644 src/simses/model/converter/sungrow.py diff --git a/src/simses/model/converter/sungrow.py b/src/simses/model/converter/sungrow.py new file mode 100644 index 0000000..fdc4f07 --- /dev/null +++ b/src/simses/model/converter/sungrow.py @@ -0,0 +1,104 @@ +import numpy as np + +from simses.interpolation import interp1d_scalar + + +class Sungrow: + """Sungrow SC1000TL converter loss model. + + Manufacturer-specific fit with asymmetric charge and discharge + coefficients, backed by field data from a frequency containment + reserve storage system. Three fit families are available via the + ``fit`` argument: + + * ``"notton"`` (default) — ``η(p) = p / (p + P0 + K·p²)``. Minimum + efficiency floor of 0.2092 applied to the discharge branch. + * ``"rampinelli"`` — ``η(p) = p / (p + K0 + K1·p + K2·p²)``. Same + discharge floor. + * ``"rational"`` — ``η(p) = (a1·p + a0) / (p² + b1·p + b0)``. No + minimum floor (the rational form stays bounded naturally). + + The fit is sampled at 201 points (101 per direction) at construction + and interpolated at runtime, so ``ac_to_dc`` and ``dc_to_ac`` remain + numerical inverses of each other. + + Source: field fit by F. Müller (M.Sc. thesis, TUM) on a + Sungrow SC1000TL inverter deployed in an FCR BESS. + """ + + _MIN_EFF_DCH = 0.2092 + + # (P0, K) for each direction + _NOTTON_CH = (0.007701864, 0.017290859) + _NOTTON_DCH = (0.005511580, 0.018772838) + + # (K0, K1, K2) for each direction + _RAMPINELLI_CH = (0.007421847, 0.003452202, 0.011994448) + _RAMPINELLI_DCH = (0.003407887, 0.013809826, 0.003155305) + + # (a1, a0, b1, b0) for each direction + _RATIONAL_CH = (47.773200770, 0.210333852, 47.572383928, 0.630988885) + _RATIONAL_DCH = (57.341420538, 0.092381040, 57.318868901, 0.441493908) + + def __init__(self, fit: str = "notton") -> None: + """ + Args: + fit: Fit family, one of ``"notton"``, ``"rampinelli"``, + ``"rational"``. Defaults to ``"notton"``. + """ + if fit == "notton": + eff_ch = self._sample_notton(self._NOTTON_CH, apply_floor=False) + eff_dch = self._sample_notton(self._NOTTON_DCH, apply_floor=True) + elif fit == "rampinelli": + eff_ch = self._sample_rampinelli(self._RAMPINELLI_CH, apply_floor=False) + eff_dch = self._sample_rampinelli(self._RAMPINELLI_DCH, apply_floor=True) + elif fit == "rational": + eff_ch = self._sample_rational(self._RATIONAL_CH) + eff_dch = self._sample_rational(self._RATIONAL_DCH) + else: + raise ValueError(f"Unknown fit '{fit}'; expected 'notton', 'rampinelli', or 'rational'.") + + p = np.linspace(0, 1, 101) + input_ch = p + output_ch = input_ch * eff_ch + + input_dch = -p + output_dch = np.zeros_like(p) + output_dch[1:] = input_dch[1:] / eff_dch[1:] + + self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() + self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + + @staticmethod + def _sample_notton(coeffs: tuple[float, float], apply_floor: bool) -> np.ndarray: + P0, K = coeffs + p = np.linspace(0, 1, 101) + eff = np.zeros_like(p) + eff[1:] = p[1:] / (p[1:] + P0 + K * p[1:] ** 2) + if apply_floor: + eff[1:] = np.maximum(Sungrow._MIN_EFF_DCH, eff[1:]) + return eff + + @staticmethod + def _sample_rampinelli(coeffs: tuple[float, float, float], apply_floor: bool) -> np.ndarray: + K0, K1, K2 = coeffs + p = np.linspace(0, 1, 101) + eff = np.zeros_like(p) + eff[1:] = p[1:] / (p[1:] + K0 + K1 * p[1:] + K2 * p[1:] ** 2) + if apply_floor: + eff[1:] = np.maximum(Sungrow._MIN_EFF_DCH, eff[1:]) + return eff + + @staticmethod + def _sample_rational(coeffs: tuple[float, float, float, float]) -> np.ndarray: + a1, a0, b1, b0 = coeffs + p = np.linspace(0, 1, 101) + eff = np.zeros_like(p) + eff[1:] = (a1 * p[1:] + a0) / (p[1:] ** 2 + b1 * p[1:] + b0) + return eff + + def ac_to_dc(self, power_ac: float) -> float: + return interp1d_scalar(power_ac, self._inp, self._out) + + def dc_to_ac(self, power_dc: float) -> float: + return interp1d_scalar(power_dc, self._out, self._inp) diff --git a/tests/test_converter_models.py b/tests/test_converter_models.py index 87fd5be..74fa20a 100644 --- a/tests/test_converter_models.py +++ b/tests/test_converter_models.py @@ -14,6 +14,7 @@ from simses.model.converter.fix_efficiency import FixedEfficiency from simses.model.converter.notton import Notton from simses.model.converter.sinamics import SinamicsS120, SinamicsS120Fit +from simses.model.converter.sungrow import Sungrow # --------------------------------------------------------------------------- @@ -52,6 +53,18 @@ class ConverterModelSpec: name="Bonfiglioli_FieldData", factory=lambda: Bonfiglioli(Bonfiglioli.FIELD_DATA), ), + ConverterModelSpec( + name="Sungrow_Notton", + factory=Sungrow, + ), + ConverterModelSpec( + name="Sungrow_Rampinelli", + factory=lambda: Sungrow(fit="rampinelli"), + ), + ConverterModelSpec( + name="Sungrow_Rational", + factory=lambda: Sungrow(fit="rational"), + ), ConverterModelSpec( name="SinamicsS120", factory=SinamicsS120, From ceb634688959ff9a7af9e2e6fa1b55539a4b2024 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 14:01:30 +0200 Subject: [PATCH 08/14] Document new cell, converter, and degradation models MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Update the cell-models, converter-models, and API-reference pages for the ported models (MolicelNMC, PanasonicNCA, Notton, Bonfiglioli, Sungrow, and the MolicelNMC degradation pair). Existing chooser-table structure preserved — one row per instance, per-instance description and usage merged into a single section. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/api/models.md | 28 ++++++++++ docs/guides/cell-models.md | 47 +++++++++++++++-- docs/guides/converter-models.md | 91 +++++++++++++++++++++++++++++++-- docs/guides/extending-cells.md | 2 +- 4 files changed, 160 insertions(+), 8 deletions(-) diff --git a/docs/api/models.md b/docs/api/models.md index b01d26e..8872d2e 100644 --- a/docs/api/models.md +++ b/docs/api/models.md @@ -8,6 +8,14 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.cell.sony_lfp.SonyLFP +### MolicelNMC + +::: simses.model.cell.molicel_nmc.MolicelNMC + +### PanasonicNCA + +::: simses.model.cell.panasonic_nca.PanasonicNCA + ### Samsung94AhNMC ::: simses.model.cell.samsung94Ah_nmc.Samsung94AhNMC @@ -18,6 +26,18 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.converter.fix_efficiency.FixedEfficiency +### Notton + +::: simses.model.converter.notton.Notton + +### Bonfiglioli + +::: simses.model.converter.bonfiglioli.Bonfiglioli + +### Sungrow + +::: simses.model.converter.sungrow.Sungrow + ### SinamicsS120 ::: simses.model.converter.sinamics.SinamicsS120 @@ -36,6 +56,14 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.degradation.sony_lfp_cyclic.SonyLFPCyclicDegradation +### MolicelNMC Calendar Degradation + +::: simses.model.degradation.molicel_nmc_calendar.MolicelNMCCalendarDegradation + +### MolicelNMC Cyclic Degradation + +::: simses.model.degradation.molicel_nmc_cyclic.MolicelNMCCyclicDegradation + ## Thermal Container Presets ::: simses.model.thermal.containers diff --git a/docs/guides/cell-models.md b/docs/guides/cell-models.md index 8c72de1..d1fe1d1 100644 --- a/docs/guides/cell-models.md +++ b/docs/guides/cell-models.md @@ -1,19 +1,21 @@ # Choosing a Cell Model -simses ships two built-in cell models. Both implement [`CellType`][simses.battery.cell.CellType] and slot interchangeably into `Battery`. The table below is the quick chooser; each cell is then documented in its own section with source and a runnable snippet. +simses ships four built-in cell models. All implement [`CellType`][simses.battery.cell.CellType] and slot interchangeably into `Battery`. The table below is the quick chooser; each cell is then documented in its own section with source and a runnable snippet. ## Comparison | Model | Chemistry | Format | Nominal capacity | Nominal voltage | Voltage window | Max C-rate (ch / dis) | Rint model | Default aging | |---|---|---|---|---|---|---|---|---| | [`SonyLFP`](#sonylfp) | LFP | Cylindrical 26650 (26 × 65 mm) | 3.0 Ah | 3.2 V | 2.0 – 3.6 V | 1.0 / 6.6 | 2-D lookup in (SOC, T), separate charge/discharge curves | Yes (Naumann) | +| [`MolicelNMC`](#molicelnmc) | NMC | Cylindrical 18650 (18 × 65 mm) | 1.9 Ah | 3.7 V | 3.0 – 4.25 V | 1.05 / 2.1 | 1-D lookup in SOC (symmetric, T-independent) | Yes (Molicel) | +| [`PanasonicNCA`](#panasonicnca) | NCA | Cylindrical 18650 (18 × 65 mm) | 2.73 Ah | 3.6 V | 2.5 – 4.2 V | 0.5 / 3.5 | 1-D lookup in SOC, separate charge/discharge curves | No | | [`Samsung94AhNMC`](#samsung94ahnmc) | NMC | Prismatic (125 × 45 × 173 mm) | 94.0 Ah | 3.68 V | 2.7 – 4.15 V | 2.0 / 2.0 | Constant 0.819 mΩ | No | All values are per cell, taken directly from the model constructors. ## `SonyLFP` -A small-format cylindrical LFP cell (Sony/Murata US26650FTC1) with a flat OCV plateau, strong cycle life, and a notably asymmetric C-rate (1.0 charge, 6.6 discharge). OCV, hysteresis, and the entropic coefficient are 1-D lookups in SOC; internal resistance is a 2-D lookup over (SOC, T) with separate charge and discharge tables. This is the only cell in the library that ships a default degradation pair — [Naumann 2018 calendar](https://doi.org/10.1016/j.est.2018.01.019) and [Naumann 2020 cyclic](https://doi.org/10.1016/j.jpowsour.2019.227666) — so multi-year stationary-storage runs with aging work out of the box. +A small-format cylindrical LFP cell (Sony/Murata US26650FTC1) with a flat OCV plateau, strong cycle life, and a notably asymmetric C-rate (1.0 charge, 6.6 discharge). OCV, hysteresis, and the entropic coefficient are 1-D lookups in SOC; internal resistance is a 2-D lookup over (SOC, T) with separate charge and discharge tables. Ships a default degradation pair — [Naumann 2018 calendar](https://doi.org/10.1016/j.est.2018.01.019) and [Naumann 2020 cyclic](https://doi.org/10.1016/j.jpowsour.2019.227666) — so multi-year stationary-storage runs with aging work out of the box. Additional source: Naumann, M. *Techno-economic evaluation of stationary lithium-ion energy storage systems with special consideration of aging*. PhD Thesis, Technical University Munich, 2018. @@ -31,6 +33,45 @@ battery = Battery( The `degradation=True` shortcut only works with cells that declare a `default_degradation_model()` — `SonyLFP` does. For warm-starting from a prior aging state, see [Degradation](../concepts/degradation.md). +## `MolicelNMC` + +An 18650 cylindrical NMC cell (Molicel INR-18650-NMC) with moderate power capability (1.05 C charge, 2.1 C discharge). OCV is analytical — a sum of four sigmoid terms plus a linear term. Internal resistance is a 1-D lookup in SOC; the source characterisation is symmetric for charge and discharge and temperature-independent in the tested range. Ships a default degradation pair adapted to the Naumann structure: `t^0.75` calendar capacity fade with `√t` resistance rise, and a `Q^0.5562` power-law cyclic law in charge throughput. Both legs track capacity fade *and* resistance rise, making it a good reference cell for NMC aging studies. + +Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., Sextl, G., Jossen, A. *Nonlinear aging characteristics of lithium-ion cells under different operational conditions*, [Journal of Energy Storage 1 (2015) 44–53](https://doi.org/10.1016/j.est.2015.05.003). + +```python +from simses.battery import Battery +from simses.model.cell.molicel_nmc import MolicelNMC + +battery = Battery( + cell=MolicelNMC(), + circuit=(14, 4), + initial_states={"start_soc": 0.5, "start_T": 25.0}, + degradation=True, # picks up the default Molicel pair +) +``` + +A 14-series × 4-parallel arrangement gives ≈ 52 V nominal with 7.6 Ah, about 380 Wh — suited to small mobile applications. The cyclic law omits the asymmetric C-rate branching of the legacy reference: it requires charge/discharge direction on the `HalfCycle`, which the detector does not expose. DoD is the dominant stress factor and is preserved. + +## `PanasonicNCA` + +A conservative-charge 18650 NCA cell (Panasonic NCR18650) with a 0.5 C charge / 3.5 C discharge envelope — the low charge rate reflects the published limit for long calendar life. OCV is analytical (sum of four sigmoids plus a linear term); internal resistance is a 1-D lookup in SOC with separate charge and discharge curves (charge and discharge differ at every SOC in the source data). No default degradation model ships — the legacy reference has a solid calendar model (Arrhenius with voltage-cubic stress) but no matching cyclic pair, so combining them would be misleading. Attach a model explicitly, or run without aging. + +Source: Keil, P., Schuster, S. F., Wilhelm, J., Travi, J., Hauser, A., Karl, R. C., Jossen, A. *Calendar aging of lithium-ion batteries*, [Journal of The Electrochemical Society 163(9) (2016) A1872–A1880](https://doi.org/10.1149/2.0411609jes). + +```python +from simses.battery import Battery +from simses.model.cell.panasonic_nca import PanasonicNCA + +battery = Battery( + cell=PanasonicNCA(), + circuit=(14, 4), + initial_states={"start_soc": 0.5, "start_T": 25.0}, +) +``` + +A 14-series × 4-parallel arrangement gives ≈ 50 V nominal with 10.9 Ah, about 550 Wh. + ## `Samsung94AhNMC` A large-format prismatic NMC cell typical of modern stationary-storage installations. 94 Ah is in the range used by grid-scale container systems where large-format prismatic cells dominate today. OCV is analytical — a sum of four sigmoid terms plus a linear term, steeper than LFP in the working range and useful for SOC estimation. Internal resistance is constant (0.819 mΩ); hysteresis and entropic coefficient are zero. No default degradation model ships with this cell — attach one explicitly, or run without aging. @@ -58,4 +99,4 @@ Writing a new cell model means subclassing `CellType` and implementing `open_cir - [Battery concept](../concepts/battery.md) — how `CellType` composes into `Battery` and scales to pack level. - [`CellType` API reference](../api/battery.md#cell-interface). -- [Models API reference](../api/models.md) — the `SonyLFP` and `Samsung94AhNMC` classes. +- [Models API reference](../api/models.md) — the four shipped cell classes. diff --git a/docs/guides/converter-models.md b/docs/guides/converter-models.md index 8e3fe95..2abcb49 100644 --- a/docs/guides/converter-models.md +++ b/docs/guides/converter-models.md @@ -1,16 +1,19 @@ # Choosing a Converter Model -simses ships three built-in AC/DC converter loss models. All three implement the [`ConverterLossModel`][simses.converter.converter.ConverterLossModel] protocol and operate on normalised power (p.u. of the converter's rated `max_power`). +simses ships six built-in AC/DC converter loss models. All implement the [`ConverterLossModel`][simses.converter.converter.ConverterLossModel] protocol and operate on normalised power (p.u. of the converter's rated `max_power`). ## Comparison | Model | Loss shape | Data source | Direction symmetry | Constructor args | |---|---|---|---|---| | [`FixedEfficiency`](#fixedefficiency) | Constant η per direction | User-supplied | Symmetric by default; asymmetric via `(charge, discharge)` tuple | `eff: float \| tuple[float, float]` | +| [`Notton`](#notton) | Generic fit `η(p) = p / (p + P0 + K·p²)` | Notton et al. 2010 (three published inverter types) | Symmetric | `coefficients: (P0, K) = TYPE_2` | +| [`Bonfiglioli`](#bonfiglioli) | Notton form with per-direction coefficients and minimum-efficiency floor | F. Müller thesis — datasheet or FCR field data | Datasheet symmetric; field-data asymmetric | `coefficients: 6-tuple = DATASHEET` | +| [`Sungrow`](#sungrow) | Three selectable fit families, per-direction coefficients, discharge floor for notton/rampinelli | F. Müller thesis — FCR field data | Asymmetric | `fit: "notton" \| "rampinelli" \| "rational"` | | [`SinamicsS120`](#sinamicss120) | 101-point lookup built from measured efficiency curves | Schimpe et al. 2018 | Symmetric by default; asymmetric via `use_discharging_curve=True` | `use_discharging_curve: bool = False` | -| [`SinamicsS120Fit`](#sinamicss120fit) | Closed-form fit `loss(p) = k₀(1 − e^(−m₀|p|)) + k₁|p| + k₂|p|²` | Least-squares fit to the same Schimpe 2018 data | Symmetric | (none) | +| [`SinamicsS120Fit`](#sinamicss120fit) | Closed-form fit `loss(p) = k₀(1 − e^(−m₀|p|)) + k₁|p| + k₂|p|²` | Least-squares fit to Schimpe 2018 data | Symmetric | (none) | -At runtime all three evaluate to linear interpolation on a 101-point internal table — the distinction is how those points were generated. +At runtime all loss models except `FixedEfficiency` evaluate to linear interpolation on a 201-point internal table (101 per direction, mirrored about zero) — the distinction is how those points were generated. ## `FixedEfficiency` @@ -27,6 +30,86 @@ converter = Converter( ) ``` +## `Notton` + +A generic parametric PV-inverter loss family with efficiency `η(p) = p / (p + P0 + K·p²)`, where `p` is the magnitude of normalised power. Three published coefficient sets from the reference paper are provided as class attributes — `TYPE_1`, `TYPE_2` (default), `TYPE_3` — representing different inverter technologies. Use this when you have no manufacturer-specific data but want a physically reasonable two-parameter fit. + +Source: Notton, G., Lazarov, V., Stoyanov, L. *Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations*, [Renewable Energy 35(2) (2010) 541–554](https://doi.org/10.1016/j.renene.2009.07.013). + +```python +from simses.converter import Converter +from simses.model.converter.notton import Notton + +converter = Converter( + loss_model=Notton(), # Type 2 inverter by default + max_power=100_000, + storage=battery, +) + +# Or with custom coefficients: +converter = Converter( + loss_model=Notton(coefficients=(0.01, 0.04)), + max_power=100_000, + storage=battery, +) +``` + +## `Bonfiglioli` + +Bonfiglioli RPS TL-4Q inverter. Uses the Notton form but with asymmetric charge/discharge coefficients and a minimum-efficiency floor that clips the curve at low normalised power — important for realistic idling behaviour in high-duty applications. Two published coefficient sets: + +- `DATASHEET` (default): manufacturer datasheet measurements. Symmetric ch/dch (`P0=0.0072, K=0.034, min_eff=0.58`). +- `FIELD_DATA`: measured on FCR battery systems. Asymmetric, with lower minimum efficiencies — reflects real deployment losses including auxiliary consumption. + +Source: F. Müller (M.Sc. thesis, TUM) — Notton fit of the [Bonfiglioli RPS TL-4Q datasheet](http://www.docsbonfiglioli.com/pdf_documents/catalogue/VE_CAT_RTL-4Q_STD_ENG-ITA_R00_5_WEB.pdf) with a complementary field-measured dataset. + +```python +from simses.converter import Converter +from simses.model.converter.bonfiglioli import Bonfiglioli + +converter = Converter( + loss_model=Bonfiglioli(), # datasheet data + max_power=100_000, + storage=battery, +) + +# Field-measured data (asymmetric, lower min η): +converter = Converter( + loss_model=Bonfiglioli(Bonfiglioli.FIELD_DATA), + max_power=100_000, + storage=battery, +) +``` + +## `Sungrow` + +Sungrow SC1000TL manufacturer-specific fit, also backed by field data from a frequency containment reserve (FCR) battery system. Three fit families are selectable via the `fit` argument: + +- `"notton"` (default): classic Notton form. Discharge branch is clipped at a 0.21 minimum efficiency floor. +- `"rampinelli"`: three-parameter loss polynomial `p / (p + K0 + K1·p + K2·p²)`. Same discharge floor. +- `"rational"`: direct rational efficiency curve `(a1·p + a0) / (p² + b1·p + b0)`. No floor — the rational form stays bounded naturally. + +All three use asymmetric charge/discharge coefficients. Use the rational fit when the Notton/Rampinelli floor clipping produces a visibly flat region you'd rather not see in your efficiency curve. + +Source: F. Müller (M.Sc. thesis, TUM) — field fit on a Sungrow SC1000TL inverter. + +```python +from simses.converter import Converter +from simses.model.converter.sungrow import Sungrow + +converter = Converter( + loss_model=Sungrow(), # notton fit by default + max_power=1_000_000, # 1 MW rated + storage=battery, +) + +converter = Converter( + loss_model=Sungrow(fit="rational"), + max_power=1_000_000, + storage=battery, +) +``` + ## `SinamicsS120` Lookup-table model built from measured efficiency curves for the Siemens Sinamics S120, a common utility-scale drive. The bundled CSV carries 1001 sample points, re-sampled down to 101 at construction. The measurement splits into `Charging` and `Discharging` columns that differ by a mean of 0.23 % and a maximum of 0.40 %. By default the charging curve is mirrored onto the discharge branch so the model is symmetric about zero; set `use_discharging_curve=True` to preserve the measured asymmetry. @@ -69,4 +152,4 @@ Writing a new converter loss model means implementing `ac_to_dc(power_norm)` and - [Converter concept](../concepts/converter.md) — how `ConverterLossModel` composes into `Converter`, the two-pass resolution, and sign handling at the AC/DC boundary. - [`Converter` API reference](../api/converter.md). -- [Models API reference](../api/models.md) — the three shipped loss models. +- [Models API reference](../api/models.md) — the six shipped loss models. diff --git a/docs/guides/extending-cells.md b/docs/guides/extending-cells.md index 9195c1e..5974e87 100644 --- a/docs/guides/extending-cells.md +++ b/docs/guides/extending-cells.md @@ -3,7 +3,7 @@ How to implement a new cell chemistry as a `CellType` subclass, drop it into a `Battery`, and plug it into the existing test harness. !!! info "Who this is for" - Researchers or engineers who want to simulate a cell not covered by the shipped `SonyLFP` / `Samsung94AhNMC`. If you just need to pick between the existing models, see [Choosing a Cell Model](cell-models.md) instead. For the architectural picture of how `CellType` and `Battery` interact, see [Battery concept](../concepts/battery.md#battery-and-celltype). + Researchers or engineers who want to simulate a cell not covered by the shipped models (`SonyLFP`, `MolicelNMC`, `PanasonicNCA`, `Samsung94AhNMC`). If you just need to pick between the existing models, see [Choosing a Cell Model](cell-models.md) instead. For the architectural picture of how `CellType` and `Battery` interact, see [Battery concept](../concepts/battery.md#battery-and-celltype). ## The contract From fde1fb1addeec8bd16f4f24983fc00c29a5e79c5 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 14:52:29 +0200 Subject: [PATCH 09/14] Split converter loss models into fit families + product subclasses MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Refactor the Notton-form converter family so generic fit classes and specific product classes are cleanly separated: - Notton(P0, K): generic symmetric Notton fit. - AsymmetricNotton(charge, discharge): per-direction (P0, K) pairs. - NottonType1, NottonType2, NottonType3: published inverter presets from Notton et al. 2010 as no-arg Notton subclasses. - Rampinelli(K0, K1, K2): three-parameter generic fit family. - BonfiglioliTL4Q, BonfiglioliTL4QFieldData: datasheet (Notton subclass) and FCR field-data (AsymmetricNotton subclass) variants of the Bonfiglioli RPS TL-4Q. - SungrowSC1000TL: AsymmetricNotton subclass for the Sungrow SC1000TL, reproducing the Notton fit that was the legacy default. Drops the minimum-efficiency floor: the Notton curve is monotonic, well-behaved at low p, and round-trips cleanly through the LUT without it. Drops the internal fit-family selector on Sungrow (Rampinelli and rational-form variants from the thesis are not ported; the original Notton default is). Model names now carry the manufacturer model designation — mirrors SinamicsS120 and makes call sites self-explanatory (``BonfiglioliTL4Q()``, ``SungrowSC1000TL()``). Extract a shared ``_notton_lut`` helper so Notton and AsymmetricNotton deduplicate the sample-and-invert logic. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/api/models.md | 32 +++++- docs/guides/converter-models.md | 131 ++++++++++++++++------ src/simses/model/converter/bonfiglioli.py | 69 +++++------- src/simses/model/converter/notton.py | 125 ++++++++++++++++----- src/simses/model/converter/rampinelli.py | 50 +++++++++ src/simses/model/converter/sungrow.py | 114 +++---------------- tests/test_converter_models.py | 35 +++--- 7 files changed, 326 insertions(+), 230 deletions(-) create mode 100644 src/simses/model/converter/rampinelli.py diff --git a/docs/api/models.md b/docs/api/models.md index 8872d2e..4f1515b 100644 --- a/docs/api/models.md +++ b/docs/api/models.md @@ -30,13 +30,37 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.converter.notton.Notton -### Bonfiglioli +### AsymmetricNotton -::: simses.model.converter.bonfiglioli.Bonfiglioli +::: simses.model.converter.notton.AsymmetricNotton -### Sungrow +### NottonType1 -::: simses.model.converter.sungrow.Sungrow +::: simses.model.converter.notton.NottonType1 + +### NottonType2 + +::: simses.model.converter.notton.NottonType2 + +### NottonType3 + +::: simses.model.converter.notton.NottonType3 + +### Rampinelli + +::: simses.model.converter.rampinelli.Rampinelli + +### BonfiglioliTL4Q + +::: simses.model.converter.bonfiglioli.BonfiglioliTL4Q + +### BonfiglioliTL4QFieldData + +::: simses.model.converter.bonfiglioli.BonfiglioliTL4QFieldData + +### SungrowSC1000TL + +::: simses.model.converter.sungrow.SungrowSC1000TL ### SinamicsS120 diff --git a/docs/guides/converter-models.md b/docs/guides/converter-models.md index 2abcb49..24e7209 100644 --- a/docs/guides/converter-models.md +++ b/docs/guides/converter-models.md @@ -1,17 +1,28 @@ # Choosing a Converter Model -simses ships six built-in AC/DC converter loss models. All implement the [`ConverterLossModel`][simses.converter.converter.ConverterLossModel] protocol and operate on normalised power (p.u. of the converter's rated `max_power`). +simses ships ten built-in AC/DC converter loss models, split into two categories. **Fit families** (`Notton`, `AsymmetricNotton`, `Rampinelli`) are generic parametric forms that require explicit coefficients; the `NottonTypeN` subclasses are presets of published coefficients. **Product models** (`BonfiglioliTL4Q`, `BonfiglioliTL4QFieldData`, `SungrowSC1000TL`, `SinamicsS120`, `SinamicsS120Fit`) are specific manufacturer hardware with baked-in coefficients. All implement the [`ConverterLossModel`][simses.converter.converter.ConverterLossModel] protocol and operate on normalised power (p.u. of the converter's rated `max_power`). ## Comparison -| Model | Loss shape | Data source | Direction symmetry | Constructor args | -|---|---|---|---|---| -| [`FixedEfficiency`](#fixedefficiency) | Constant η per direction | User-supplied | Symmetric by default; asymmetric via `(charge, discharge)` tuple | `eff: float \| tuple[float, float]` | -| [`Notton`](#notton) | Generic fit `η(p) = p / (p + P0 + K·p²)` | Notton et al. 2010 (three published inverter types) | Symmetric | `coefficients: (P0, K) = TYPE_2` | -| [`Bonfiglioli`](#bonfiglioli) | Notton form with per-direction coefficients and minimum-efficiency floor | F. Müller thesis — datasheet or FCR field data | Datasheet symmetric; field-data asymmetric | `coefficients: 6-tuple = DATASHEET` | -| [`Sungrow`](#sungrow) | Three selectable fit families, per-direction coefficients, discharge floor for notton/rampinelli | F. Müller thesis — FCR field data | Asymmetric | `fit: "notton" \| "rampinelli" \| "rational"` | -| [`SinamicsS120`](#sinamicss120) | 101-point lookup built from measured efficiency curves | Schimpe et al. 2018 | Symmetric by default; asymmetric via `use_discharging_curve=True` | `use_discharging_curve: bool = False` | -| [`SinamicsS120Fit`](#sinamicss120fit) | Closed-form fit `loss(p) = k₀(1 − e^(−m₀|p|)) + k₁|p| + k₂|p|²` | Least-squares fit to Schimpe 2018 data | Symmetric | (none) | +### Fit families (parametric, require coefficients) + +| Model | Loss shape | Constructor args | +|---|---|---| +| [`Notton`](#notton) | `η(p) = p / (p + P0 + K·p²)`, symmetric | `P0: float, K: float` | +| [`AsymmetricNotton`](#asymmetricnotton) | Notton form with independent charge and discharge coefficients | `charge: (P0, K), discharge: (P0, K)` | +| [`Rampinelli`](#rampinelli) | `η(p) = p / (p + K0 + K1·p + K2·p²)`, symmetric | `K0: float, K1: float, K2: float` | +| [`NottonType1`](#nottontypen), [`NottonType2`](#nottontypen), [`NottonType3`](#nottontypen) | Three published inverter presets from Notton et al. 2010 | (none — no-arg subclasses of `Notton`) | + +### Product models (specific hardware, no-arg constructors) + +| Model | Inherits from | Data source | Direction symmetry | +|---|---|---|---| +| [`FixedEfficiency`](#fixedefficiency) | — | User-supplied | Symmetric by default; asymmetric via `(charge, discharge)` tuple | +| [`BonfiglioliTL4Q`](#bonfigliolitl4q) | `Notton` | F. Müller thesis — RPS TL-4Q datasheet | Symmetric | +| [`BonfiglioliTL4QFieldData`](#bonfigliolitl4qfielddata) | `AsymmetricNotton` | F. Müller thesis — FCR field data | Asymmetric | +| [`SungrowSC1000TL`](#sungrowsc1000tl) | `AsymmetricNotton` | F. Müller thesis — FCR field data | Asymmetric | +| [`SinamicsS120`](#sinamicss120) | — | Schimpe et al. 2018 (measured) | Symmetric by default; asymmetric via `use_discharging_curve=True` | +| [`SinamicsS120Fit`](#sinamicss120fit) | — | Schimpe et al. 2018 (parametric fit) | Symmetric | At runtime all loss models except `FixedEfficiency` evaluate to linear interpolation on a 201-point internal table (101 per direction, mirrored about zero) — the distinction is how those points were generated. @@ -32,7 +43,9 @@ converter = Converter( ## `Notton` -A generic parametric PV-inverter loss family with efficiency `η(p) = p / (p + P0 + K·p²)`, where `p` is the magnitude of normalised power. Three published coefficient sets from the reference paper are provided as class attributes — `TYPE_1`, `TYPE_2` (default), `TYPE_3` — representing different inverter technologies. Use this when you have no manufacturer-specific data but want a physically reasonable two-parameter fit. +A generic parametric PV-inverter loss family with efficiency `η(p) = p / (p + P0 + K·p²)`, where `p` is the magnitude of normalised power. Symmetric about zero. Use this when you have a Notton-form fit to measured data, or when you want a physically reasonable two-parameter baseline. + +For custom asymmetric ch/dch use [`AsymmetricNotton`](#asymmetricnotton). For the three published inverter presets see [`NottonTypeN`](#nottontypen) below. Source: Notton, G., Lazarov, V., Stoyanov, L. *Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations*, [Renewable Energy 35(2) (2010) 541–554](https://doi.org/10.1016/j.renene.2009.07.013). @@ -41,71 +54,117 @@ from simses.converter import Converter from simses.model.converter.notton import Notton converter = Converter( - loss_model=Notton(), # Type 2 inverter by default + loss_model=Notton(P0=0.0072, K=0.0345), max_power=100_000, storage=battery, ) +``` + +## `AsymmetricNotton` + +Notton-form fit with independent charge and discharge parameter sets. Each direction takes its own `(P0, K)` pair — useful for fitting converters whose measured efficiency curves differ between charging and discharging. + +```python +from simses.converter import Converter +from simses.model.converter.notton import AsymmetricNotton -# Or with custom coefficients: converter = Converter( - loss_model=Notton(coefficients=(0.01, 0.04)), + loss_model=AsymmetricNotton( + charge=(0.0072, 0.0345), + discharge=(0.005, 0.018), + ), max_power=100_000, storage=battery, ) ``` -## `Bonfiglioli` - -Bonfiglioli RPS TL-4Q inverter. Uses the Notton form but with asymmetric charge/discharge coefficients and a minimum-efficiency floor that clips the curve at low normalised power — important for realistic idling behaviour in high-duty applications. Two published coefficient sets: +## `NottonTypeN` -- `DATASHEET` (default): manufacturer datasheet measurements. Symmetric ch/dch (`P0=0.0072, K=0.034, min_eff=0.58`). -- `FIELD_DATA`: measured on FCR battery systems. Asymmetric, with lower minimum efficiencies — reflects real deployment losses including auxiliary consumption. +Three published inverter presets from Notton et al. 2010, provided as no-arg subclasses of `Notton` for convenience: -Source: F. Müller (M.Sc. thesis, TUM) — Notton fit of the [Bonfiglioli RPS TL-4Q datasheet](http://www.docsbonfiglioli.com/pdf_documents/catalogue/VE_CAT_RTL-4Q_STD_ENG-ITA_R00_5_WEB.pdf) with a complementary field-measured dataset. +- `NottonType1` — `P0 = 0.0145, K = 0.0437` +- `NottonType2` — `P0 = 0.0072, K = 0.0345` +- `NottonType3` — `P0 = 0.0088, K = 0.1149` ```python from simses.converter import Converter -from simses.model.converter.bonfiglioli import Bonfiglioli +from simses.model.converter.notton import NottonType2 converter = Converter( - loss_model=Bonfiglioli(), # datasheet data + loss_model=NottonType2(), max_power=100_000, storage=battery, ) +``` + +## `Rampinelli` + +A three-parameter generalisation of the Notton form: `η(p) = p / (p + K0 + K1·p + K2·p²)`. The extra linear term lets the fit capture a wider range of measured efficiency curves — useful when a two-parameter Notton fit leaves a visible residual at mid-power. + +Source: Rampinelli, G. A., Krenzinger, A., Chenlo Romero, F. *Mathematical models for efficiency of inverters used in grid connected photovoltaic systems*, [Renewable and Sustainable Energy Reviews 34 (2014) 578–587](https://doi.org/10.1016/j.rser.2014.03.047). + +```python +from simses.converter import Converter +from simses.model.converter.rampinelli import Rampinelli -# Field-measured data (asymmetric, lower min η): converter = Converter( - loss_model=Bonfiglioli(Bonfiglioli.FIELD_DATA), + loss_model=Rampinelli(K0=0.003, K1=0.014, K2=0.003), max_power=100_000, storage=battery, ) ``` -## `Sungrow` +## `BonfiglioliTL4Q` -Sungrow SC1000TL manufacturer-specific fit, also backed by field data from a frequency containment reserve (FCR) battery system. Three fit families are selectable via the `fit` argument: +Bonfiglioli RPS TL-4Q inverter parameterised from the manufacturer datasheet — a `Notton` subclass with symmetric coefficients `P0 = 0.0072, K = 0.034`. -- `"notton"` (default): classic Notton form. Discharge branch is clipped at a 0.21 minimum efficiency floor. -- `"rampinelli"`: three-parameter loss polynomial `p / (p + K0 + K1·p + K2·p²)`. Same discharge floor. -- `"rational"`: direct rational efficiency curve `(a1·p + a0) / (p² + b1·p + b0)`. No floor — the rational form stays bounded naturally. +See [`BonfiglioliTL4QFieldData`](#bonfigliolitl4qfielddata) for the asymmetric variant parameterised from FCR field data. -All three use asymmetric charge/discharge coefficients. Use the rational fit when the Notton/Rampinelli floor clipping produces a visibly flat region you'd rather not see in your efficiency curve. +Source: F. Müller (M.Sc. thesis, TUM) — Notton fit of the [Bonfiglioli RPS TL-4Q datasheet](http://www.docsbonfiglioli.com/pdf_documents/catalogue/VE_CAT_RTL-4Q_STD_ENG-ITA_R00_5_WEB.pdf). -Source: F. Müller (M.Sc. thesis, TUM) — field fit on a Sungrow SC1000TL inverter. +```python +from simses.converter import Converter +from simses.model.converter.bonfiglioli import BonfiglioliTL4Q + +converter = Converter( + loss_model=BonfiglioliTL4Q(), + max_power=100_000, + storage=battery, +) +``` + +## `BonfiglioliTL4QFieldData` + +Bonfiglioli RPS TL-4Q inverter parameterised from frequency containment reserve (FCR) battery-system field measurements — an `AsymmetricNotton` subclass with distinct charge and discharge coefficients. Reflects real deployment losses including auxiliary consumption that the datasheet curves do not capture. Charge: `P0 = 0.00195, K = 0.01349`. Discharge: `P0 = 0.00292, K = 0.03609`. + +Source: F. Müller (M.Sc. thesis, TUM) — field fit on FCR BESS deployments of the Bonfiglioli RPS TL-4Q. ```python from simses.converter import Converter -from simses.model.converter.sungrow import Sungrow +from simses.model.converter.bonfiglioli import BonfiglioliTL4QFieldData converter = Converter( - loss_model=Sungrow(), # notton fit by default - max_power=1_000_000, # 1 MW rated + loss_model=BonfiglioliTL4QFieldData(), + max_power=100_000, storage=battery, ) +``` + +## `SungrowSC1000TL` + +Sungrow SC1000TL inverter, an `AsymmetricNotton` subclass backed by field data from an FCR battery system. Charge: `P0 = 0.007701864, K = 0.017290859`. Discharge: `P0 = 0.005511580, K = 0.018772838`. + +The original thesis also characterised Rampinelli and rational-form fits of the same measurements; the Notton fit was the default in the legacy simses implementation and is the one ported here. + +Source: F. Müller (M.Sc. thesis, TUM) — field fit on a Sungrow SC1000TL inverter. + +```python +from simses.converter import Converter +from simses.model.converter.sungrow import SungrowSC1000TL converter = Converter( - loss_model=Sungrow(fit="rational"), - max_power=1_000_000, + loss_model=SungrowSC1000TL(), + max_power=1_000_000, # 1 MW rated storage=battery, ) ``` @@ -152,4 +211,4 @@ Writing a new converter loss model means implementing `ac_to_dc(power_norm)` and - [Converter concept](../concepts/converter.md) — how `ConverterLossModel` composes into `Converter`, the two-pass resolution, and sign handling at the AC/DC boundary. - [`Converter` API reference](../api/converter.md). -- [Models API reference](../api/models.md) — the six shipped loss models. +- [Models API reference](../api/models.md) — all ten shipped loss models. diff --git a/src/simses/model/converter/bonfiglioli.py b/src/simses/model/converter/bonfiglioli.py index 54f1d08..d92eaf3 100644 --- a/src/simses/model/converter/bonfiglioli.py +++ b/src/simses/model/converter/bonfiglioli.py @@ -1,59 +1,40 @@ -import numpy as np +from simses.model.converter.notton import AsymmetricNotton, Notton -from simses.interpolation import interp1d_scalar +class BonfiglioliTL4Q(Notton): + """Bonfiglioli RPS TL-4Q converter — datasheet parameterisation. -class Bonfiglioli: - """Bonfiglioli RPS TL-4Q converter loss model. + Symmetric Notton-form fit with ``P0 = 0.0072, K = 0.034`` measured + under manufacturer datasheet conditions. - Notton-form efficiency fit with asymmetric charge and discharge - coefficients and a minimum-efficiency floor that clips the fit at - low normalised power. Two parameter sets are published: + See :class:`BonfiglioliTL4QFieldData` for the asymmetric variant + parameterised from FCR field data. - * :attr:`DATASHEET` (default) — manufacturer datasheet measurements. - Symmetric: ``P0=0.0072, K=0.034, min_eff=0.5813`` for both - directions. - * :attr:`FIELD_DATA` — measured on FCR battery systems. Asymmetric: - charge ``P0=0.00195, K=0.01349, min_eff=0.3441``; discharge - ``P0=0.00292, K=0.03609, min_eff=0.2742``. Reflects real - deployment losses including auxiliary consumption. - - Source: field fit and datasheet reading by F. Müller (M.Sc. thesis, - TUM), from the + Source: F. Müller (M.Sc. thesis, TUM) — Notton fit of the `Bonfiglioli RPS TL-4Q datasheet `_. """ - # (P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, min_eff_dch) - DATASHEET = (0.0072, 0.034, 0.5813, 0.0072, 0.034, 0.5813) - FIELD_DATA = (0.00195, 0.01349, 0.3441, 0.00292, 0.03609, 0.2742) - - def __init__(self, coefficients: tuple[float, float, float, float, float, float] = DATASHEET) -> None: - """ - Args: - coefficients: ``(P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, - min_eff_dch)`` tuple. Defaults to :attr:`DATASHEET`. - """ - P0_ch, K_ch, min_eff_ch, P0_dch, K_dch, min_eff_dch = coefficients + def __init__(self) -> None: + super().__init__(P0=0.0072, K=0.034) - p = np.linspace(0, 1, 101) - eff_ch = np.zeros_like(p) - eff_ch[1:] = np.maximum(min_eff_ch, p[1:] / (p[1:] + P0_ch + K_ch * p[1:] ** 2)) - input_ch = p - output_ch = input_ch * eff_ch +class BonfiglioliTL4QFieldData(AsymmetricNotton): + """Bonfiglioli RPS TL-4Q converter — FCR field-data parameterisation. - eff_dch = np.zeros_like(p) - eff_dch[1:] = np.maximum(min_eff_dch, p[1:] / (p[1:] + P0_dch + K_dch * p[1:] ** 2)) - input_dch = -p - output_dch = np.zeros_like(p) - output_dch[1:] = input_dch[1:] / eff_dch[1:] + Asymmetric Notton-form fit measured on frequency containment reserve + (FCR) battery systems; reflects real deployment losses including + auxiliary consumption. Charge: ``P0 = 0.00195, K = 0.01349``. + Discharge: ``P0 = 0.00292, K = 0.03609``. - self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() - self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + See :class:`BonfiglioliTL4Q` for the symmetric datasheet variant. - def ac_to_dc(self, power_ac: float) -> float: - return interp1d_scalar(power_ac, self._inp, self._out) + Source: F. Müller (M.Sc. thesis, TUM) — field fit on FCR BESS + deployments of the Bonfiglioli RPS TL-4Q. + """ - def dc_to_ac(self, power_dc: float) -> float: - return interp1d_scalar(power_dc, self._out, self._inp) + def __init__(self) -> None: + super().__init__( + charge=(0.00195, 0.01349), + discharge=(0.00292, 0.03609), + ) diff --git a/src/simses/model/converter/notton.py b/src/simses/model/converter/notton.py index 2f63ed6..1c49415 100644 --- a/src/simses/model/converter/notton.py +++ b/src/simses/model/converter/notton.py @@ -3,19 +3,44 @@ from simses.interpolation import interp1d_scalar +def _notton_lut(P0_ch: float, K_ch: float, P0_dch: float, K_dch: float) -> tuple[list[float], list[float]]: + """Build a 201-point input/output LUT for a Notton-form loss model. + + Efficiency follows ``η(p) = p / (p + P0 + K·p²)`` on each direction + independently. The LUT stitches charge (0 → 1) and discharge (−1 → 0) + branches into a single monotonic curve so that ``interp1d_scalar`` + can invert it exactly. + """ + p = np.linspace(0, 1, 101) + + # Charge branch (AC -> DC): efficiency reduces DC output; P_dc = P_ac · η. + eff_ch = p[1:] / (p[1:] + P0_ch + K_ch * p[1:] ** 2) + input_ch = p + output_ch = np.zeros_like(p) + output_ch[1:] = p[1:] * eff_ch + + # Discharge branch (DC -> AC): battery supplies the loss; P_dc = P_ac / η + # (i.e. |DC| > |AC|). With input_dch = −p, output_dch = input_dch / η. + eff_dch = p[1:] / (p[1:] + P0_dch + K_dch * p[1:] ** 2) + input_dch = -p + output_dch = np.zeros_like(p) + output_dch[1:] = -p[1:] / eff_dch + + inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() + out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + return inp, out + + class Notton: - """Generic parametric PV-inverter loss model. + """Generic parametric PV-inverter loss family — symmetric form. - Efficiency curve of the form ``η(p) = p / (p + P0 + K·p²)`` where ``p`` - is the magnitude of normalised power (p.u. of the converter's rated - max power). The fit is sampled at 201 points (101 per direction, - mirrored about zero) at construction and interpolated at runtime, so - ``ac_to_dc`` and ``dc_to_ac`` remain numerical inverses of each other. + Efficiency curve of the form ``η(p) = p / (p + P0 + K·p²)`` where + ``p`` is the magnitude of normalised power (p.u. of the converter's + rated max power). Same coefficients apply to charge and discharge. - Three coefficient sets are published in Notton et al. (2010): - ``TYPE_1`` (P0=0.0145, K=0.0437), ``TYPE_2`` (P0=0.0072, K=0.0345, - used by default here), ``TYPE_3`` (P0=0.0088, K=0.1149). Custom - coefficients can also be supplied directly. + For the three published inverter presets see :class:`NottonType1`, + :class:`NottonType2`, :class:`NottonType3`. For Notton-form fits + with per-direction coefficients see :class:`AsymmetricNotton`. Source: Notton, G.; Lazarov, V.; Stoyanov, L. (2010). *Optimal sizing of a grid-connected PV system for various PV module technologies and @@ -23,35 +48,77 @@ class Notton: Renewable Energy 35(2) 541–554, doi:10.1016/j.renene.2009.07.013. """ - TYPE_1 = (0.0145, 0.0437) - TYPE_2 = (0.0072, 0.0345) - TYPE_3 = (0.0088, 0.1149) - - def __init__(self, coefficients: tuple[float, float] = TYPE_2) -> None: + def __init__(self, P0: float, K: float) -> None: """ Args: - coefficients: ``(P0, K)`` tuple of Notton fit coefficients. - Defaults to the published Type-2 inverter parameters. + P0: No-load loss coefficient (p.u.). + K: Quadratic-loss coefficient (p.u.). """ - P0, K = coefficients + self._inp, self._out = _notton_lut(P0, K, P0, K) - # Evaluate at non-zero magnitudes only; index 0 (p=0) is handled as output=0. - p = np.linspace(0, 1, 101) - eff = np.zeros_like(p) - eff[1:] = p[1:] / (p[1:] + P0 + K * p[1:] ** 2) + def ac_to_dc(self, power_ac: float) -> float: + return interp1d_scalar(power_ac, self._inp, self._out) + + def dc_to_ac(self, power_dc: float) -> float: + return interp1d_scalar(power_dc, self._out, self._inp) - input_ch = p - output_ch = input_ch * eff - input_dch = -p - output_dch = np.zeros_like(p) - output_dch[1:] = input_dch[1:] / eff[1:] +class AsymmetricNotton: + """Notton-form loss family with per-direction coefficients. - self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() - self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + Same efficiency law as :class:`Notton` but with independent charge + and discharge parameter sets — each a ``(P0, K)`` pair. Used by + manufacturer product models whose measured efficiency differs + between charging and discharging (e.g. :class:`BonfiglioliTL4QFieldData`, + :class:`SungrowSC1000TL`). + """ + + def __init__( + self, + charge: tuple[float, float], + discharge: tuple[float, float], + ) -> None: + """ + Args: + charge: ``(P0, K)`` coefficients for the charge branch. + discharge: ``(P0, K)`` coefficients for the discharge branch. + """ + P0_ch, K_ch = charge + P0_dch, K_dch = discharge + self._inp, self._out = _notton_lut(P0_ch, K_ch, P0_dch, K_dch) def ac_to_dc(self, power_ac: float) -> float: return interp1d_scalar(power_ac, self._inp, self._out) def dc_to_ac(self, power_dc: float) -> float: return interp1d_scalar(power_dc, self._out, self._inp) + + +class NottonType1(Notton): + """Notton Type-1 inverter preset (``P0 = 0.0145, K = 0.0437``). + + Source: Notton et al. 2010, Renewable Energy 35(2) 541–554. + """ + + def __init__(self) -> None: + super().__init__(P0=0.0145, K=0.0437) + + +class NottonType2(Notton): + """Notton Type-2 inverter preset (``P0 = 0.0072, K = 0.0345``). + + Source: Notton et al. 2010, Renewable Energy 35(2) 541–554. + """ + + def __init__(self) -> None: + super().__init__(P0=0.0072, K=0.0345) + + +class NottonType3(Notton): + """Notton Type-3 inverter preset (``P0 = 0.0088, K = 0.1149``). + + Source: Notton et al. 2010, Renewable Energy 35(2) 541–554. + """ + + def __init__(self) -> None: + super().__init__(P0=0.0088, K=0.1149) diff --git a/src/simses/model/converter/rampinelli.py b/src/simses/model/converter/rampinelli.py new file mode 100644 index 0000000..d012204 --- /dev/null +++ b/src/simses/model/converter/rampinelli.py @@ -0,0 +1,50 @@ +import numpy as np + +from simses.interpolation import interp1d_scalar + + +class Rampinelli: + """Generic parametric PV-inverter loss family. + + Efficiency curve of the form + ``η(p) = p / (p + K0 + K1·p + K2·p²)`` + where ``p`` is the magnitude of normalised power (p.u. of the + converter's rated max power). Three-parameter extension of the + Notton form — the extra linear term lets the fit capture a wider + range of measured efficiency curves. Symmetric about zero. The fit + is sampled at 201 points (101 per direction, mirrored about zero) + at construction and interpolated at runtime, so ``ac_to_dc`` and + ``dc_to_ac`` remain numerical inverses of each other. + + Source: Rampinelli, G. A.; Krenzinger, A.; Chenlo Romero, F. (2014). + *Mathematical models for efficiency of inverters used in grid + connected photovoltaic systems.* Renewable and Sustainable Energy + Reviews 34, 578–587, doi:10.1016/j.rser.2014.03.047. + """ + + def __init__(self, K0: float, K1: float, K2: float) -> None: + """ + Args: + K0: No-load loss coefficient (p.u.). + K1: Linear loss coefficient (p.u.). + K2: Quadratic loss coefficient (p.u.). + """ + p = np.linspace(0, 1, 101) + eff = np.zeros_like(p) + eff[1:] = p[1:] / (p[1:] + K0 + K1 * p[1:] + K2 * p[1:] ** 2) + + input_ch = p + output_ch = input_ch * eff + + input_dch = -p + output_dch = np.zeros_like(p) + output_dch[1:] = input_dch[1:] / eff[1:] + + self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() + self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() + + def ac_to_dc(self, power_ac: float) -> float: + return interp1d_scalar(power_ac, self._inp, self._out) + + def dc_to_ac(self, power_dc: float) -> float: + return interp1d_scalar(power_dc, self._out, self._inp) diff --git a/src/simses/model/converter/sungrow.py b/src/simses/model/converter/sungrow.py index fdc4f07..f4c6d2a 100644 --- a/src/simses/model/converter/sungrow.py +++ b/src/simses/model/converter/sungrow.py @@ -1,104 +1,22 @@ -import numpy as np +from simses.model.converter.notton import AsymmetricNotton -from simses.interpolation import interp1d_scalar +class SungrowSC1000TL(AsymmetricNotton): + """Sungrow SC1000TL converter — FCR field-data parameterisation. -class Sungrow: - """Sungrow SC1000TL converter loss model. + Asymmetric Notton-form fit, backed by field data from a frequency + containment reserve storage system. Charge: ``P0 = 0.007701864, + K = 0.017290859``. Discharge: ``P0 = 0.005511580, K = 0.018772838``. - Manufacturer-specific fit with asymmetric charge and discharge - coefficients, backed by field data from a frequency containment - reserve storage system. Three fit families are available via the - ``fit`` argument: - - * ``"notton"`` (default) — ``η(p) = p / (p + P0 + K·p²)``. Minimum - efficiency floor of 0.2092 applied to the discharge branch. - * ``"rampinelli"`` — ``η(p) = p / (p + K0 + K1·p + K2·p²)``. Same - discharge floor. - * ``"rational"`` — ``η(p) = (a1·p + a0) / (p² + b1·p + b0)``. No - minimum floor (the rational form stays bounded naturally). - - The fit is sampled at 201 points (101 per direction) at construction - and interpolated at runtime, so ``ac_to_dc`` and ``dc_to_ac`` remain - numerical inverses of each other. - - Source: field fit by F. Müller (M.Sc. thesis, TUM) on a - Sungrow SC1000TL inverter deployed in an FCR BESS. + Source: field fit by F. Müller (M.Sc. thesis, TUM) on a Sungrow + SC1000TL inverter deployed in an FCR BESS. The thesis also provides + Rampinelli and rational-form fits of the same dataset; the Notton + fit was the configured default in the legacy simses implementation + and is the one reproduced here. """ - _MIN_EFF_DCH = 0.2092 - - # (P0, K) for each direction - _NOTTON_CH = (0.007701864, 0.017290859) - _NOTTON_DCH = (0.005511580, 0.018772838) - - # (K0, K1, K2) for each direction - _RAMPINELLI_CH = (0.007421847, 0.003452202, 0.011994448) - _RAMPINELLI_DCH = (0.003407887, 0.013809826, 0.003155305) - - # (a1, a0, b1, b0) for each direction - _RATIONAL_CH = (47.773200770, 0.210333852, 47.572383928, 0.630988885) - _RATIONAL_DCH = (57.341420538, 0.092381040, 57.318868901, 0.441493908) - - def __init__(self, fit: str = "notton") -> None: - """ - Args: - fit: Fit family, one of ``"notton"``, ``"rampinelli"``, - ``"rational"``. Defaults to ``"notton"``. - """ - if fit == "notton": - eff_ch = self._sample_notton(self._NOTTON_CH, apply_floor=False) - eff_dch = self._sample_notton(self._NOTTON_DCH, apply_floor=True) - elif fit == "rampinelli": - eff_ch = self._sample_rampinelli(self._RAMPINELLI_CH, apply_floor=False) - eff_dch = self._sample_rampinelli(self._RAMPINELLI_DCH, apply_floor=True) - elif fit == "rational": - eff_ch = self._sample_rational(self._RATIONAL_CH) - eff_dch = self._sample_rational(self._RATIONAL_DCH) - else: - raise ValueError(f"Unknown fit '{fit}'; expected 'notton', 'rampinelli', or 'rational'.") - - p = np.linspace(0, 1, 101) - input_ch = p - output_ch = input_ch * eff_ch - - input_dch = -p - output_dch = np.zeros_like(p) - output_dch[1:] = input_dch[1:] / eff_dch[1:] - - self._inp = np.hstack((input_dch[1:][::-1], 0.0, input_ch[1:])).tolist() - self._out = np.hstack((output_dch[1:][::-1], 0.0, output_ch[1:])).tolist() - - @staticmethod - def _sample_notton(coeffs: tuple[float, float], apply_floor: bool) -> np.ndarray: - P0, K = coeffs - p = np.linspace(0, 1, 101) - eff = np.zeros_like(p) - eff[1:] = p[1:] / (p[1:] + P0 + K * p[1:] ** 2) - if apply_floor: - eff[1:] = np.maximum(Sungrow._MIN_EFF_DCH, eff[1:]) - return eff - - @staticmethod - def _sample_rampinelli(coeffs: tuple[float, float, float], apply_floor: bool) -> np.ndarray: - K0, K1, K2 = coeffs - p = np.linspace(0, 1, 101) - eff = np.zeros_like(p) - eff[1:] = p[1:] / (p[1:] + K0 + K1 * p[1:] + K2 * p[1:] ** 2) - if apply_floor: - eff[1:] = np.maximum(Sungrow._MIN_EFF_DCH, eff[1:]) - return eff - - @staticmethod - def _sample_rational(coeffs: tuple[float, float, float, float]) -> np.ndarray: - a1, a0, b1, b0 = coeffs - p = np.linspace(0, 1, 101) - eff = np.zeros_like(p) - eff[1:] = (a1 * p[1:] + a0) / (p[1:] ** 2 + b1 * p[1:] + b0) - return eff - - def ac_to_dc(self, power_ac: float) -> float: - return interp1d_scalar(power_ac, self._inp, self._out) - - def dc_to_ac(self, power_dc: float) -> float: - return interp1d_scalar(power_dc, self._out, self._inp) + def __init__(self) -> None: + super().__init__( + charge=(0.007701864, 0.017290859), + discharge=(0.005511580, 0.018772838), + ) diff --git a/tests/test_converter_models.py b/tests/test_converter_models.py index 74fa20a..834c0e2 100644 --- a/tests/test_converter_models.py +++ b/tests/test_converter_models.py @@ -10,11 +10,12 @@ import pytest -from simses.model.converter.bonfiglioli import Bonfiglioli +from simses.model.converter.bonfiglioli import BonfiglioliTL4Q, BonfiglioliTL4QFieldData from simses.model.converter.fix_efficiency import FixedEfficiency -from simses.model.converter.notton import Notton +from simses.model.converter.notton import NottonType1, NottonType2 +from simses.model.converter.rampinelli import Rampinelli from simses.model.converter.sinamics import SinamicsS120, SinamicsS120Fit -from simses.model.converter.sungrow import Sungrow +from simses.model.converter.sungrow import SungrowSC1000TL # --------------------------------------------------------------------------- @@ -38,32 +39,28 @@ class ConverterModelSpec: factory=lambda: FixedEfficiency((0.96, 0.94)), ), ConverterModelSpec( - name="Notton_Type2", - factory=Notton, + name="NottonType2", + factory=NottonType2, ), ConverterModelSpec( - name="Notton_Type1", - factory=lambda: Notton(Notton.TYPE_1), + name="NottonType1", + factory=NottonType1, ), ConverterModelSpec( - name="Bonfiglioli_Datasheet", - factory=Bonfiglioli, + name="Rampinelli", + factory=lambda: Rampinelli(0.003407887, 0.013809826, 0.003155305), ), ConverterModelSpec( - name="Bonfiglioli_FieldData", - factory=lambda: Bonfiglioli(Bonfiglioli.FIELD_DATA), + name="BonfiglioliTL4Q", + factory=BonfiglioliTL4Q, ), ConverterModelSpec( - name="Sungrow_Notton", - factory=Sungrow, + name="BonfiglioliTL4QFieldData", + factory=BonfiglioliTL4QFieldData, ), ConverterModelSpec( - name="Sungrow_Rampinelli", - factory=lambda: Sungrow(fit="rampinelli"), - ), - ConverterModelSpec( - name="Sungrow_Rational", - factory=lambda: Sungrow(fit="rational"), + name="SungrowSC1000TL", + factory=SungrowSC1000TL, ), ConverterModelSpec( name="SinamicsS120", From 2b4eba7e48677f79839814479ef73ebbeca724bb Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 15:01:48 +0200 Subject: [PATCH 10/14] Revert "Add MolicelNMC default degradation model" This reverts commit 63b43d2a7afc13353994a94c11919e08dca591db. --- src/simses/model/cell/molicel_nmc.py | 24 +- .../data/NMC_Molicel_capacity_cal.csv | 102 -- .../data/NMC_Molicel_capacity_cyc.csv | 1002 ----------------- .../degradation/data/NMC_Molicel_ri_cal.csv | 102 -- .../degradation/data/NMC_Molicel_ri_cyc.csv | 1002 ----------------- .../model/degradation/molicel_nmc_calendar.py | 69 -- .../model/degradation/molicel_nmc_cyclic.py | 82 -- tests/test_degradation_models.py | 4 - 8 files changed, 1 insertion(+), 2386 deletions(-) delete mode 100644 src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv delete mode 100644 src/simses/model/degradation/data/NMC_Molicel_capacity_cyc.csv delete mode 100644 src/simses/model/degradation/data/NMC_Molicel_ri_cal.csv delete mode 100644 src/simses/model/degradation/data/NMC_Molicel_ri_cyc.csv delete mode 100644 src/simses/model/degradation/molicel_nmc_calendar.py delete mode 100644 src/simses/model/degradation/molicel_nmc_cyclic.py diff --git a/src/simses/model/cell/molicel_nmc.py b/src/simses/model/cell/molicel_nmc.py index 8f7bdad..e45d5a7 100644 --- a/src/simses/model/cell/molicel_nmc.py +++ b/src/simses/model/cell/molicel_nmc.py @@ -6,11 +6,7 @@ from simses.battery.battery import BatteryState, CellType from simses.battery.format import RoundCell from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties -from simses.degradation import DegradationModel -from simses.degradation.state import DegradationState from simses.interpolation import interp1d_scalar -from simses.model.degradation.molicel_nmc_calendar import MolicelNMCCalendarDegradation -from simses.model.degradation.molicel_nmc_cyclic import MolicelNMCCyclicDegradation class MolicelNMC(CellType): @@ -20,12 +16,7 @@ class MolicelNMC(CellType): nominal voltage. Analytical ``OCV(SOC)`` as a sum of sigmoids and a linear term; internal resistance is a 1-D lookup in SOC (the source characterisation is symmetric for charge and discharge and - temperature-independent in the tested range). Ships a default - degradation model - (:class:`~simses.model.degradation.molicel_nmc_calendar.MolicelNMCCalendarDegradation` - + :class:`~simses.model.degradation.molicel_nmc_cyclic.MolicelNMCCyclicDegradation`) - that :class:`~simses.battery.battery.Battery` picks up when constructed - with ``degradation=True``. + temperature-independent in the tested range). Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., Sextl, G., & Jossen, A. (2015). *Nonlinear aging characteristics of @@ -87,16 +78,3 @@ def open_circuit_voltage(self, state: BatteryState) -> float: def internal_resistance(self, state: BatteryState) -> float: return interp1d_scalar(state.soc, self._rint_lut_soc, self._rint_lut_rint) - - @classmethod - def default_degradation_model( - cls, - initial_soc: float, - initial_state: DegradationState | None = None, - ) -> DegradationModel: - return DegradationModel( - calendar=MolicelNMCCalendarDegradation(), - cyclic=MolicelNMCCyclicDegradation(), - initial_soc=initial_soc, - initial_state=initial_state, - ) diff --git a/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv b/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv deleted file mode 100644 index 5ae884e..0000000 --- a/src/simses/model/degradation/data/NMC_Molicel_capacity_cal.csv +++ /dev/null @@ -1,102 +0,0 @@ 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-0.974,0.0011952 -0.975,0.0011960769 -0.976,0.0011969538 -0.977,0.0011978308 -0.978,0.0011987077 -0.979,0.0011995846 -0.98,0.0012004614999999 -0.981,0.0012013385 -0.982,0.0012022153999999 -0.983,0.0012030923 -0.984,0.0012039692 -0.985,0.0012048462 -0.986,0.0012057231 -0.987,0.0012066 -0.988,0.0012074769 -0.989,0.0012083538 -0.99,0.0012092308 -0.991,0.0012101077 -0.992,0.0012109846 -0.993,0.0012118615 -0.994,0.0012127385 -0.995,0.0012136153999999 -0.996,0.0012144923 -0.997,0.0012153692 -0.998,0.0012162461999999 -0.999,0.0012171231 -1.0,0.001218 diff --git a/src/simses/model/degradation/molicel_nmc_calendar.py b/src/simses/model/degradation/molicel_nmc_calendar.py deleted file mode 100644 index 639a769..0000000 --- a/src/simses/model/degradation/molicel_nmc_calendar.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Calendar degradation model for Molicel INR-18650-NMC cells. - -Source: parameterised from accelerated-aging measurements adapted to the -same structure as the Naumann SonyLFP calendar law, with stress factors -bundled as 2-D lookup tables over (SOC, T). Ni Chuanqin (EES, TUM). -""" - -import os - -import pandas as pd - -from simses.battery.state import BatteryState -from simses.degradation.calendar import CalendarDegradation -from simses.interpolation import interp2d_scalar - -_SEC_PER_WEEK = 86400.0 * 7.0 - - -def _load_stress_matrix(filename: str) -> tuple[list[float], list[float], list[list[float]]]: - path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", filename) - df = pd.read_csv(path) - soc_lut = df["SOC"].tolist() - T_lut = df["Temp"].dropna().tolist() - mat = df.iloc[:, 2 : 2 + len(T_lut)].values.tolist() - return soc_lut, T_lut, mat - - -class MolicelNMCCalendarDegradation(CalendarDegradation): - """Calendar aging for Molicel INR-18650-NMC cells. - - Capacity loss follows a ``t^0.75`` power law with virtual-time - continuation; resistance rise follows a ``sqrt(t)`` law with - virtual-time continuation. Both stress factors are 2-D lookups over - ``(SOC, T)`` valid in the range T ∈ [10, 50] °C and SOC ∈ [0, 1]. - Out-of-range inputs raise ``ValueError`` from the interpolation - helper. - - This model is **stateless**: accumulated values are owned by the - :class:`~simses.degradation.degradation.DegradationModel` and passed - in on every call. - """ - - def __init__(self) -> None: - self._soc_lut, self._T_lut, self._cap_mat = _load_stress_matrix("NMC_Molicel_capacity_cal.csv") - _, _, self._ri_mat = _load_stress_matrix("NMC_Molicel_ri_cal.csv") - - def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: - if dt == 0.0: - return 0.0 - - k_q = interp2d_scalar(state.soc, state.T, self._soc_lut, self._T_lut, self._cap_mat) - if k_q <= 0.0: - return 0.0 - - dt_weeks = dt / _SEC_PER_WEEK - virtual_weeks = (accumulated_qloss / k_q) ** (4.0 / 3.0) - return k_q * (virtual_weeks + dt_weeks) ** 0.75 - accumulated_qloss - - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: - if dt == 0.0: - return 0.0 - - k_r = interp2d_scalar(state.soc, state.T, self._soc_lut, self._T_lut, self._ri_mat) - if k_r <= 0.0: - return 0.0 - - dt_weeks = dt / _SEC_PER_WEEK - virtual_weeks = (accumulated_rinc / k_r) ** 2 - return k_r * (virtual_weeks + dt_weeks) ** 0.5 - accumulated_rinc diff --git a/src/simses/model/degradation/molicel_nmc_cyclic.py b/src/simses/model/degradation/molicel_nmc_cyclic.py deleted file mode 100644 index 7776263..0000000 --- a/src/simses/model/degradation/molicel_nmc_cyclic.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Cyclic degradation model for Molicel INR-18650-NMC cells. - -Source: parameterised from accelerated-aging measurements adapted to the -same structure as the Naumann SonyLFP cyclic law, with stress factors -bundled as 1-D lookup tables over DoD. Ni Chuanqin (EES, TUM). -""" - -import os - -import pandas as pd - -from simses.battery.state import BatteryState -from simses.degradation.cycle_detector import HalfCycle -from simses.degradation.cyclic import CyclicDegradation -from simses.interpolation import interp1d_scalar - -# Nominal single-cell capacity (Ah). The legacy cyclic law uses charge -# throughput in Ah as its independent variable; we reconstruct it from -# the half-cycle's depth-of-discharge. -_NOMINAL_CAPACITY_AH = 1.9 - -# Power-law exponents (dimensionless). -_EXPONENT_QLOSS = 0.5562 -_EXPONENT_RINC = 0.5562 - - -def _load_1d_stress(filename: str, column: str) -> tuple[list[float], list[float]]: - path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", filename) - df = pd.read_csv(path) - return df["DOD"].tolist(), df[column].tolist() - - -class MolicelNMCCyclicDegradation(CyclicDegradation): - """Cyclic aging for Molicel INR-18650-NMC cells. - - Capacity loss follows a power law in charge throughput ``Q^0.5562`` - with virtual-throughput continuation; resistance rise follows the - same power law. The stress factors are 1-D lookups over DoD. Charge - throughput per half-cycle is reconstructed as ``DoD * 1.9 Ah`` using - the Molicel's nominal cell capacity. - - The legacy model also includes an asymmetric C-rate scaling - (separate coefficients above 0.5 C for charge vs discharge). That - branching requires charge/discharge direction on the - :class:`HalfCycle`, which the simses-lite detector does not expose; - the scaling is therefore omitted here. DoD is the dominant stress - factor and is preserved. - - This model is **stateless**: accumulated values are owned by the - :class:`~simses.degradation.degradation.DegradationModel` and passed - in on every call. - """ - - def __init__(self) -> None: - self._dod_lut_cap, self._cap_stress = _load_1d_stress("NMC_Molicel_capacity_cyc.csv", "f_capacity_cyc") - self._dod_lut_ri, self._ri_stress = _load_1d_stress("NMC_Molicel_ri_cyc.csv", "f_ri_cyc") - - def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: - dod = half_cycle.depth_of_discharge - if dod == 0.0: - return 0.0 - - k_q = interp1d_scalar(dod, self._dod_lut_cap, self._cap_stress) - if k_q <= 0.0: - return 0.0 - - throughput_ah = dod * _NOMINAL_CAPACITY_AH - virtual_q = (accumulated_qloss / k_q) ** (1.0 / _EXPONENT_QLOSS) - return max(0.0, k_q * (virtual_q + throughput_ah) ** _EXPONENT_QLOSS - accumulated_qloss) - - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: - dod = half_cycle.depth_of_discharge - if dod == 0.0: - return 0.0 - - k_r = interp1d_scalar(dod, self._dod_lut_ri, self._ri_stress) - if k_r <= 0.0: - return 0.0 - - throughput_ah = dod * _NOMINAL_CAPACITY_AH - virtual_q = (accumulated_rinc / k_r) ** (1.0 / _EXPONENT_RINC) - return max(0.0, k_r * (virtual_q + throughput_ah) ** _EXPONENT_RINC - accumulated_rinc) diff --git a/tests/test_degradation_models.py b/tests/test_degradation_models.py index c3afbcf..1be6ff8 100644 --- a/tests/test_degradation_models.py +++ b/tests/test_degradation_models.py @@ -11,8 +11,6 @@ from simses.battery.state import BatteryState from simses.degradation.cycle_detector import HalfCycle -from simses.model.degradation.molicel_nmc_calendar import MolicelNMCCalendarDegradation -from simses.model.degradation.molicel_nmc_cyclic import MolicelNMCCyclicDegradation from simses.model.degradation.sony_lfp_calendar import SonyLFPCalendarDegradation from simses.model.degradation.sony_lfp_cyclic import SonyLFPCyclicDegradation @@ -61,7 +59,6 @@ class CalendarModelSpec: CALENDAR_SPECS = [ - CalendarModelSpec(name="MolicelNMCCalendar", factory=MolicelNMCCalendarDegradation), CalendarModelSpec(name="SonyLFPCalendar", factory=SonyLFPCalendarDegradation), ] @@ -73,7 +70,6 @@ class CyclicModelSpec: CYCLIC_SPECS = [ - CyclicModelSpec(name="MolicelNMCCyclic", factory=MolicelNMCCyclicDegradation), CyclicModelSpec(name="SonyLFPCyclic", factory=SonyLFPCyclicDegradation), ] From 639510f6ae3dbe69933d652c92b965f83307ee37 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 15:01:48 +0200 Subject: [PATCH 11/14] Revert "Add PanasonicNCA cell model" This reverts commit c0147fec3d15bb66c927e5e64451e55b4704a151. --- .../model/cell/data/NCA_PanasonicNCR_Rint.csv | 102 ------------------ src/simses/model/cell/panasonic_nca.py | 81 -------------- tests/test_cell_models.py | 7 -- 3 files changed, 190 deletions(-) delete mode 100644 src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv delete mode 100644 src/simses/model/cell/panasonic_nca.py diff --git a/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv b/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv deleted file mode 100644 index db6e828..0000000 --- a/src/simses/model/cell/data/NCA_PanasonicNCR_Rint.csv +++ /dev/null @@ -1,102 +0,0 @@ -SOC,R_ch,R_dch -0.0,0.12,0.541090909090909 -0.01,0.117611366120219,0.4805202865013779 -0.02,0.1152227322404369,0.4199496639118459 -0.03,0.112834098360656,0.359379041322314 -0.04,0.110445464480874,0.298808418732783 -0.05,0.108056830601093,0.238237796143251 -0.06,0.105166338797814,0.2157546681118189 -0.07,0.102275846994535,0.193271540080387 -0.08,0.0993853551912568,0.170788412048955 -0.09,0.0964948633879782,0.148305284017523 -0.1,0.0936043715846995,0.125822155986091 -0.11,0.0906043817886442,0.1187407287414419 -0.12,0.0876043919925888,0.111659301496793 -0.13,0.0846044021965335,0.104577874252144 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-0.58,0.0632873038294565,0.0757891431527947 -0.59,0.0618673673297481,0.0775166360220554 -0.6,0.0604474308300397,0.0792441288913162 -0.61,0.0612114911573659,0.0790175313651162 -0.62,0.061975551484692,0.0787909338389162 -0.63,0.0627396118120182,0.0785643363127162 -0.64,0.0635036721393443,0.0783377387865163 -0.65,0.0642677324666705,0.0781111412603163 -0.66,0.0650317927939966,0.0778845437341163 -0.67,0.0657958531213227,0.0776579462079163 -0.68,0.0665599134486489,0.0774313486817163 -0.69,0.067323973775975,0.0772047511555163 -0.7,0.0680880341033012,0.0769781536293163 -0.71,0.0696310830958873,0.0765470346905279 -0.72,0.0711741320884734,0.0761159157517395 -0.73,0.0727171810810595,0.075684796812951 -0.74,0.0742602300736456,0.0752536778741626 -0.75,0.0758032790662318,0.0748225589353742 -0.76,0.0773463280588179,0.0743914399965858 -0.77,0.078889377051404,0.0739603210577974 -0.78,0.0804324260439901,0.0735292021190089 -0.79,0.0819754750365763,0.0730980831802205 -0.8,0.0835185240291624,0.0726669642414321 -0.81,0.0845644132530404,0.0723820859991071 -0.82,0.0856103024769184,0.072097207756782 -0.83,0.0866561917007964,0.071812329514457 -0.84,0.0877020809246745,0.071527451272132 -0.85,0.0887479701485525,0.0712425730298069 -0.86,0.0897938593724305,0.0709576947874819 -0.87,0.0908397485963086,0.0706728165451568 -0.88,0.0918856378201866,0.0703879383028318 -0.89,0.0929315270440647,0.0701030600605068 -0.9,0.0939774162679427,0.0698181818181817 -0.91,0.0938887655502393,0.0698181818181817 -0.92,0.093800114832536,0.0698181818181817 -0.93,0.0937114641148326,0.0698181818181817 -0.94,0.0936228133971293,0.0698181818181817 -0.95,0.0935341626794259,0.0698181818181816 -0.96,0.0934455119617226,0.0698181818181816 -0.97,0.0933568612440192,0.0698181818181816 -0.98,0.0932682105263159,0.0698181818181816 -0.99,0.0931795598086125,0.0698181818181816 -1.0,0.0930909090909092,0.0698181818181816 diff --git a/src/simses/model/cell/panasonic_nca.py b/src/simses/model/cell/panasonic_nca.py deleted file mode 100644 index e728f5c..0000000 --- a/src/simses/model/cell/panasonic_nca.py +++ /dev/null @@ -1,81 +0,0 @@ -import math -import os - -import pandas as pd - -from simses.battery.battery import BatteryState, CellType -from simses.battery.format import RoundCell -from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties -from simses.interpolation import interp1d_scalar - - -class PanasonicNCA(CellType): - """Panasonic NCR18650 cylindrical NCA cell. - - Nickel-cobalt-aluminum oxide cell, 2.73 Ah nominal capacity, 3.6 V - nominal voltage, conservative 0.5 C charge / 3.5 C discharge. Analytical - ``OCV(SOC)`` as a sum of sigmoids and a linear term; internal resistance - is a 1-D lookup in SOC with separate charge and discharge curves. - - Source: P. Keil, S. F. Schuster, J. Wilhelm, J. Travi, A. Hauser, - R. C. Karl, A. Jossen. *Calendar aging of lithium-ion batteries.* - Journal of The Electrochemical Society 163(9) (2016) A1872-A1880, - doi:10.1149/2.0411609jes. - """ - - def __init__(self) -> None: - super().__init__( - electrical=ElectricalCellProperties( - nominal_capacity=2.73, # Ah - nominal_voltage=3.6, # V - max_voltage=4.2, # V - min_voltage=2.5, # V - max_charge_rate=0.5, # 1/h - max_discharge_rate=3.5, # 1/h - self_discharge_rate=0.0, - coulomb_efficiency=1.0, # p.u. - ), - thermal=ThermalCellProperties( - min_temperature=0.0, # °C - max_temperature=45.0, # °C - mass=0.044, # kg per cell - specific_heat=1048, # J/kgK - convection_coefficient=15, # W/m2K - ), - cell_format=RoundCell(diameter=18, length=65), - ) - path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") - df_rint = pd.read_csv(os.path.join(path, "NCA_PanasonicNCR_Rint.csv")) - self._rint_lut_soc = df_rint["SOC"].tolist() - self._rint_lut_ch = df_rint["R_ch"].tolist() - self._rint_lut_dch = df_rint["R_dch"].tolist() - - def open_circuit_voltage(self, state: BatteryState) -> float: - a1 = -0.3777 - a2 = 10.2859 - a3 = 17.0608 - a4 = -3.7820 - b1 = -5.6272 - b2 = 0.2907 - k0 = 4.9852 - k1 = -2.86523 - k2 = 0.3852 - k3 = -0.1599 - k4 = 1.2256 - k5 = 0.7412 - - soc = state.soc - - ocv = ( - k0 - + k1 / (1 + math.exp(a1 * (soc - b1))) - + k2 / (1 + math.exp(a2 * (soc - b2))) - + k3 / (1 + math.exp(a3 * (soc - 1))) - + k4 / (1 + math.exp(a4 * soc)) - + k5 * soc - ) - return ocv - - def internal_resistance(self, state: BatteryState) -> float: - lut = self._rint_lut_ch if state.is_charge else self._rint_lut_dch - return interp1d_scalar(state.soc, self._rint_lut_soc, lut) diff --git a/tests/test_cell_models.py b/tests/test_cell_models.py index 931e8dc..7fd38c2 100644 --- a/tests/test_cell_models.py +++ b/tests/test_cell_models.py @@ -13,7 +13,6 @@ from simses.battery.cell import CellType from simses.battery.state import BatteryState from simses.model.cell.molicel_nmc import MolicelNMC -from simses.model.cell.panasonic_nca import PanasonicNCA from simses.model.cell.samsung94Ah_nmc import Samsung94AhNMC from simses.model.cell.sony_lfp import SonyLFP @@ -44,12 +43,6 @@ class CellModelSpec: factory=MolicelNMC, rint_varies_with_soc=True, ), - CellModelSpec( - name="PanasonicNCA", - factory=PanasonicNCA, - rint_varies_with_soc=True, - rint_differs_charge_discharge=True, - ), CellModelSpec( name="Samsung94AhNMC", factory=Samsung94AhNMC, From 50e862e033b7ceb7a1acd8545383214e2dda21d3 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 15:01:48 +0200 Subject: [PATCH 12/14] Revert "Add MolicelNMC cell model" This reverts commit 082969750f056a5695c5299cfe946a81a5146138. --- .../model/cell/data/NMC_Molicel_Rint.csv | 102 ------------------ src/simses/model/cell/molicel_nmc.py | 80 -------------- tests/test_cell_models.py | 6 -- 3 files changed, 188 deletions(-) delete mode 100644 src/simses/model/cell/data/NMC_Molicel_Rint.csv delete mode 100644 src/simses/model/cell/molicel_nmc.py diff --git a/src/simses/model/cell/data/NMC_Molicel_Rint.csv b/src/simses/model/cell/data/NMC_Molicel_Rint.csv deleted file mode 100644 index e9e316c..0000000 --- a/src/simses/model/cell/data/NMC_Molicel_Rint.csv +++ /dev/null @@ -1,102 +0,0 @@ -SOC,Rint -0.0,0.1917073789999999 -0.01,0.1881221 -0.02,0.184454034 -0.03,0.180723681 -0.04,0.176951537 -0.05,0.1731581 -0.06,0.1693638689999999 -0.07,0.165589341 -0.08,0.161855014 -0.09,0.158181385 -0.1,0.154588954 -0.11,0.1510982169999999 -0.12,0.147729672 -0.13,0.144503818 -0.14,0.141439021 -0.15,0.138539519 -0.16,0.135803796 -0.17,0.1332303209999999 -0.18,0.130816151 -0.19,0.128553571 -0.2,0.126433863 -0.21,0.1244483109999999 -0.22,0.122588613 -0.23,0.120847198 -0.24,0.11921657 -0.25,0.117689259 -0.26,0.116258344 -0.27,0.11491745 -0.28,0.113660224 -0.29,0.112480399 -0.3,0.111372525 -0.31,0.110331599 -0.32,0.109352619 -0.33,0.108430789 -0.34,0.107562245 -0.35,0.1067433909999999 -0.36,0.10597063 -0.37,0.105240459 -0.38,0.104549591 -0.39,0.103894773 -0.4,0.103272763 -0.41,0.102680862 -0.42,0.102117065 -0.43,0.101579411 -0.44,0.101065978 -0.45,0.1005753319999999 -0.46,0.100106393 -0.47,0.0996580859999999 -0.48,0.099229347 -0.49,0.09881915 -0.5,0.098426488 -0.51,0.098050353 -0.52,0.097689902 -0.53,0.097344814 -0.54,0.097014876 -0.55,0.096699874 -0.56,0.096399712 -0.57,0.096114486 -0.58,0.0958443119999999 -0.59,0.095589304 -0.6,0.095349492 -0.61,0.095124834 -0.62,0.094915283 -0.63,0.094720779 -0.64,0.094541143 -0.65,0.094376136 -0.66,0.094225519 -0.67,0.094089023 -0.68,0.0939662469999999 -0.69,0.093856758 -0.7,0.093760123 -0.71,0.09367589 -0.72,0.093603572 -0.73,0.0935426769999999 -0.74,0.09349271 -0.75,0.093453134 -0.76,0.093423353 -0.77,0.093402772 -0.78,0.093390791 -0.79,0.093386758 -0.8,0.0933899919999999 -0.81,0.093399808 -0.82,0.093415519 -0.83,0.093436423 -0.84,0.093461809 -0.85,0.093490968 -0.86,0.093523281 -0.87,0.093558395 -0.88,0.093596009 -0.89,0.09363582 -0.9,0.0936775359999999 -0.91,0.093720875 -0.92,0.09376556 -0.93,0.09381131 -0.94,0.093857847 -0.95,0.0939048919999999 -0.96,0.093952165 -0.97,0.093999388 -0.98,0.094046281 -0.99,0.094092566 -1.0,0.094137964 diff --git a/src/simses/model/cell/molicel_nmc.py b/src/simses/model/cell/molicel_nmc.py deleted file mode 100644 index e45d5a7..0000000 --- a/src/simses/model/cell/molicel_nmc.py +++ /dev/null @@ -1,80 +0,0 @@ -import math -import os - -import pandas as pd - -from simses.battery.battery import BatteryState, CellType -from simses.battery.format import RoundCell -from simses.battery.properties import ElectricalCellProperties, ThermalCellProperties -from simses.interpolation import interp1d_scalar - - -class MolicelNMC(CellType): - """Molicel INR-18650-NMC cylindrical NMC cell. - - Nickel-manganese-cobalt oxide cell, 1.9 Ah nominal capacity, 3.7 V - nominal voltage. Analytical ``OCV(SOC)`` as a sum of sigmoids and a - linear term; internal resistance is a 1-D lookup in SOC (the source - characterisation is symmetric for charge and discharge and - temperature-independent in the tested range). - - Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., - Sextl, G., & Jossen, A. (2015). *Nonlinear aging characteristics of - lithium-ion cells under different operational conditions.* Journal of - Energy Storage, 1, 44–53, doi:10.1016/j.est.2015.05.003. - """ - - def __init__(self) -> None: - super().__init__( - electrical=ElectricalCellProperties( - nominal_capacity=1.9, # Ah - nominal_voltage=3.7, # V - max_voltage=4.25, # V - min_voltage=3.0, # V - max_charge_rate=1.05, # 1/h - max_discharge_rate=2.1, # 1/h - self_discharge_rate=0.0, - coulomb_efficiency=1.0, # p.u. - ), - thermal=ThermalCellProperties( - min_temperature=0.0, # °C - max_temperature=45.0, # °C - mass=0.045, # kg per cell - specific_heat=965, # J/kgK - convection_coefficient=15, # W/m2K - ), - cell_format=RoundCell(diameter=18, length=65), - ) - path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") - df_rint = pd.read_csv(os.path.join(path, "NMC_Molicel_Rint.csv")) - self._rint_lut_soc = df_rint["SOC"].tolist() - self._rint_lut_rint = df_rint["Rint"].tolist() - - def open_circuit_voltage(self, state: BatteryState) -> float: - a1 = -1.6206 - a2 = -6.9895 - a3 = 1.4458 - a4 = 1.9530 - b1 = 3.4206 - b2 = 0.8759 - k0 = 2.0127 - k1 = 2.7684 - k2 = 1.0698 - k3 = 4.1431 - k4 = -3.8417 - k5 = -0.1856 - - soc = state.soc - - ocv = ( - k0 - + k1 / (1 + math.exp(a1 * (soc - b1))) - + k2 / (1 + math.exp(a2 * (soc - b2))) - + k3 / (1 + math.exp(a3 * (soc - 1))) - + k4 / (1 + math.exp(a4 * soc)) - + k5 * soc - ) - return ocv - - def internal_resistance(self, state: BatteryState) -> float: - return interp1d_scalar(state.soc, self._rint_lut_soc, self._rint_lut_rint) diff --git a/tests/test_cell_models.py b/tests/test_cell_models.py index 7fd38c2..ec1365e 100644 --- a/tests/test_cell_models.py +++ b/tests/test_cell_models.py @@ -12,7 +12,6 @@ from simses.battery.cell import CellType from simses.battery.state import BatteryState -from simses.model.cell.molicel_nmc import MolicelNMC from simses.model.cell.samsung94Ah_nmc import Samsung94AhNMC from simses.model.cell.sony_lfp import SonyLFP @@ -38,11 +37,6 @@ class CellModelSpec: CELL_SPECS: list[CellModelSpec] = [ - CellModelSpec( - name="MolicelNMC", - factory=MolicelNMC, - rint_varies_with_soc=True, - ), CellModelSpec( name="Samsung94AhNMC", factory=Samsung94AhNMC, From 2053c6c59ee7f3b240c8391d074230c111eaa4f5 Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 15:04:08 +0200 Subject: [PATCH 13/14] Drop MolicelNMC and PanasonicNCA from the docs Follow-up to the MolicelNMC + PanasonicNCA cell reverts: remove the associated sections and registry entries from the cell-models guide, API reference, and extending-cells guide. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/api/models.md | 16 ------------ docs/guides/cell-models.md | 45 ++-------------------------------- docs/guides/extending-cells.md | 2 +- 3 files changed, 3 insertions(+), 60 deletions(-) diff --git a/docs/api/models.md b/docs/api/models.md index 4f1515b..687621b 100644 --- a/docs/api/models.md +++ b/docs/api/models.md @@ -8,14 +8,6 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.cell.sony_lfp.SonyLFP -### MolicelNMC - -::: simses.model.cell.molicel_nmc.MolicelNMC - -### PanasonicNCA - -::: simses.model.cell.panasonic_nca.PanasonicNCA - ### Samsung94AhNMC ::: simses.model.cell.samsung94Ah_nmc.Samsung94AhNMC @@ -80,14 +72,6 @@ Concrete implementations of cell, converter, degradation, and thermal models. ::: simses.model.degradation.sony_lfp_cyclic.SonyLFPCyclicDegradation -### MolicelNMC Calendar Degradation - -::: simses.model.degradation.molicel_nmc_calendar.MolicelNMCCalendarDegradation - -### MolicelNMC Cyclic Degradation - -::: simses.model.degradation.molicel_nmc_cyclic.MolicelNMCCyclicDegradation - ## Thermal Container Presets ::: simses.model.thermal.containers diff --git a/docs/guides/cell-models.md b/docs/guides/cell-models.md index d1fe1d1..2cc9749 100644 --- a/docs/guides/cell-models.md +++ b/docs/guides/cell-models.md @@ -1,14 +1,12 @@ # Choosing a Cell Model -simses ships four built-in cell models. All implement [`CellType`][simses.battery.cell.CellType] and slot interchangeably into `Battery`. The table below is the quick chooser; each cell is then documented in its own section with source and a runnable snippet. +simses ships two built-in cell models. Both implement [`CellType`][simses.battery.cell.CellType] and slot interchangeably into `Battery`. The table below is the quick chooser; each cell is then documented in its own section with source and a runnable snippet. ## Comparison | Model | Chemistry | Format | Nominal capacity | Nominal voltage | Voltage window | Max C-rate (ch / dis) | Rint model | Default aging | |---|---|---|---|---|---|---|---|---| | [`SonyLFP`](#sonylfp) | LFP | Cylindrical 26650 (26 × 65 mm) | 3.0 Ah | 3.2 V | 2.0 – 3.6 V | 1.0 / 6.6 | 2-D lookup in (SOC, T), separate charge/discharge curves | Yes (Naumann) | -| [`MolicelNMC`](#molicelnmc) | NMC | Cylindrical 18650 (18 × 65 mm) | 1.9 Ah | 3.7 V | 3.0 – 4.25 V | 1.05 / 2.1 | 1-D lookup in SOC (symmetric, T-independent) | Yes (Molicel) | -| [`PanasonicNCA`](#panasonicnca) | NCA | Cylindrical 18650 (18 × 65 mm) | 2.73 Ah | 3.6 V | 2.5 – 4.2 V | 0.5 / 3.5 | 1-D lookup in SOC, separate charge/discharge curves | No | | [`Samsung94AhNMC`](#samsung94ahnmc) | NMC | Prismatic (125 × 45 × 173 mm) | 94.0 Ah | 3.68 V | 2.7 – 4.15 V | 2.0 / 2.0 | Constant 0.819 mΩ | No | All values are per cell, taken directly from the model constructors. @@ -33,45 +31,6 @@ battery = Battery( The `degradation=True` shortcut only works with cells that declare a `default_degradation_model()` — `SonyLFP` does. For warm-starting from a prior aging state, see [Degradation](../concepts/degradation.md). -## `MolicelNMC` - -An 18650 cylindrical NMC cell (Molicel INR-18650-NMC) with moderate power capability (1.05 C charge, 2.1 C discharge). OCV is analytical — a sum of four sigmoid terms plus a linear term. Internal resistance is a 1-D lookup in SOC; the source characterisation is symmetric for charge and discharge and temperature-independent in the tested range. Ships a default degradation pair adapted to the Naumann structure: `t^0.75` calendar capacity fade with `√t` resistance rise, and a `Q^0.5562` power-law cyclic law in charge throughput. Both legs track capacity fade *and* resistance rise, making it a good reference cell for NMC aging studies. - -Source: Schuster, S. F., Bach, T., Fleder, E., Müller, J., Brand, M., Sextl, G., Jossen, A. *Nonlinear aging characteristics of lithium-ion cells under different operational conditions*, [Journal of Energy Storage 1 (2015) 44–53](https://doi.org/10.1016/j.est.2015.05.003). - -```python -from simses.battery import Battery -from simses.model.cell.molicel_nmc import MolicelNMC - -battery = Battery( - cell=MolicelNMC(), - circuit=(14, 4), - initial_states={"start_soc": 0.5, "start_T": 25.0}, - degradation=True, # picks up the default Molicel pair -) -``` - -A 14-series × 4-parallel arrangement gives ≈ 52 V nominal with 7.6 Ah, about 380 Wh — suited to small mobile applications. The cyclic law omits the asymmetric C-rate branching of the legacy reference: it requires charge/discharge direction on the `HalfCycle`, which the detector does not expose. DoD is the dominant stress factor and is preserved. - -## `PanasonicNCA` - -A conservative-charge 18650 NCA cell (Panasonic NCR18650) with a 0.5 C charge / 3.5 C discharge envelope — the low charge rate reflects the published limit for long calendar life. OCV is analytical (sum of four sigmoids plus a linear term); internal resistance is a 1-D lookup in SOC with separate charge and discharge curves (charge and discharge differ at every SOC in the source data). No default degradation model ships — the legacy reference has a solid calendar model (Arrhenius with voltage-cubic stress) but no matching cyclic pair, so combining them would be misleading. Attach a model explicitly, or run without aging. - -Source: Keil, P., Schuster, S. F., Wilhelm, J., Travi, J., Hauser, A., Karl, R. C., Jossen, A. *Calendar aging of lithium-ion batteries*, [Journal of The Electrochemical Society 163(9) (2016) A1872–A1880](https://doi.org/10.1149/2.0411609jes). - -```python -from simses.battery import Battery -from simses.model.cell.panasonic_nca import PanasonicNCA - -battery = Battery( - cell=PanasonicNCA(), - circuit=(14, 4), - initial_states={"start_soc": 0.5, "start_T": 25.0}, -) -``` - -A 14-series × 4-parallel arrangement gives ≈ 50 V nominal with 10.9 Ah, about 550 Wh. - ## `Samsung94AhNMC` A large-format prismatic NMC cell typical of modern stationary-storage installations. 94 Ah is in the range used by grid-scale container systems where large-format prismatic cells dominate today. OCV is analytical — a sum of four sigmoid terms plus a linear term, steeper than LFP in the working range and useful for SOC estimation. Internal resistance is constant (0.819 mΩ); hysteresis and entropic coefficient are zero. No default degradation model ships with this cell — attach one explicitly, or run without aging. @@ -99,4 +58,4 @@ Writing a new cell model means subclassing `CellType` and implementing `open_cir - [Battery concept](../concepts/battery.md) — how `CellType` composes into `Battery` and scales to pack level. - [`CellType` API reference](../api/battery.md#cell-interface). -- [Models API reference](../api/models.md) — the four shipped cell classes. +- [Models API reference](../api/models.md) — the shipped cell classes. diff --git a/docs/guides/extending-cells.md b/docs/guides/extending-cells.md index 5974e87..8e40954 100644 --- a/docs/guides/extending-cells.md +++ b/docs/guides/extending-cells.md @@ -3,7 +3,7 @@ How to implement a new cell chemistry as a `CellType` subclass, drop it into a `Battery`, and plug it into the existing test harness. !!! info "Who this is for" - Researchers or engineers who want to simulate a cell not covered by the shipped models (`SonyLFP`, `MolicelNMC`, `PanasonicNCA`, `Samsung94AhNMC`). If you just need to pick between the existing models, see [Choosing a Cell Model](cell-models.md) instead. For the architectural picture of how `CellType` and `Battery` interact, see [Battery concept](../concepts/battery.md#battery-and-celltype). + Researchers or engineers who want to simulate a cell not covered by the shipped models (`SonyLFP`, `Samsung94AhNMC`). If you just need to pick between the existing models, see [Choosing a Cell Model](cell-models.md) instead. For the architectural picture of how `CellType` and `Battery` interact, see [Battery concept](../concepts/battery.md#battery-and-celltype). ## The contract From c0c45939fba7fbe2d26b88781de43a3c17949abc Mon Sep 17 00:00:00 2001 From: Martin Cornejo Date: Mon, 20 Apr 2026 15:13:48 +0200 Subject: [PATCH 14/14] Revert "Pass accumulated_rinc to update_resistance" This reverts commit 8ccdfc3ea65802443821200a27cd06f622c443a7. --- docs/concepts/degradation.md | 8 +++--- docs/guides/extending-degradation.md | 16 ++++++------ examples/extending/custom_degradation.py | 6 ++--- src/simses/degradation/calendar.py | 5 +--- src/simses/degradation/cyclic.py | 5 +--- src/simses/degradation/degradation.py | 8 +++--- .../model/degradation/sony_lfp_calendar.py | 3 +-- .../model/degradation/sony_lfp_cyclic.py | 3 +-- tests/test_degradation.py | 4 +-- tests/test_degradation_models.py | 26 +++++++------------ 10 files changed, 35 insertions(+), 49 deletions(-) diff --git a/docs/concepts/degradation.md b/docs/concepts/degradation.md index 3771a96..e518760 100644 --- a/docs/concepts/degradation.md +++ b/docs/concepts/degradation.md @@ -11,7 +11,7 @@ A simses degradation setup is three things composed together. [`DegradationModel`][simses.degradation.degradation.DegradationModel] is the **composer**. It owns the mutable [`DegradationState`][simses.degradation.state.DegradationState] (the running ledger of accumulated damage), holds a [`HalfCycleDetector`][simses.degradation.cycle_detector.HalfCycleDetector], and delegates the actual aging laws to two stateless sub-models. At each call to `Battery.step()` the battery hands the current `BatteryState` to `DegradationModel.step(state, dt)`, which applies calendar aging and — if a half-cycle has just completed — cyclic aging, then writes the updated `soh_Q` and `soh_R` back onto `BatteryState`. -[`CalendarDegradation`][simses.degradation.calendar.CalendarDegradation] and [`CyclicDegradation`][simses.degradation.cyclic.CyclicDegradation] are **protocols** — the aging-law equivalents of `CellType`: stateless, chemistry-specific descriptions of how damage accumulates under given stress. Concrete implementations are typically **(semi-)empirical fits** to accelerated-aging measurements on a specific cell — polynomial, Arrhenius, power-law, or lookup forms calibrated to observed fade curves, not first-principles electrochemistry. A `CalendarDegradation` exposes `update_capacity(state, dt, accumulated_qloss)` and `update_resistance(state, dt, accumulated_rinc)`; a `CyclicDegradation` exposes the same two methods but takes a `HalfCycle` instead of `dt`. Each call returns a *delta* (a non-negative increment), never an absolute value. +[`CalendarDegradation`][simses.degradation.calendar.CalendarDegradation] and [`CyclicDegradation`][simses.degradation.cyclic.CyclicDegradation] are **protocols** — the aging-law equivalents of `CellType`: stateless, chemistry-specific descriptions of how damage accumulates under given stress. Concrete implementations are typically **(semi-)empirical fits** to accelerated-aging measurements on a specific cell — polynomial, Arrhenius, power-law, or lookup forms calibrated to observed fade curves, not first-principles electrochemistry. A `CalendarDegradation` exposes `update_capacity(state, dt, accumulated_qloss)` and `update_resistance(state, dt)`; a `CyclicDegradation` exposes the same two methods but takes a `HalfCycle` instead of `dt`. Each call returns a *delta* (a non-negative increment), never an absolute value. [`HalfCycleDetector`][simses.degradation.cycle_detector.HalfCycleDetector] is the **trigger**. It watches SOC across timesteps and raises a completed [`HalfCycle`][simses.degradation.cycle_detector.HalfCycle] whenever the SOC reverses direction. The `HalfCycle` carries the stress factors — depth of discharge, mean SOC, average C-rate, and full-equivalent-cycle contribution — that the cyclic model needs. @@ -59,15 +59,15 @@ At each step, `DegradationModel` sums the calendar contribution and — when the One call to `DegradationModel.step(state, dt)` runs two passes. -**Calendar pass (every step).** The calendar sub-model is asked for the capacity loss and resistance rise that accumulate over this timestep, given the current temperature and SOC. `DegradationModel` also hands it the current values of `qloss_cal` and `rinc_cal` — the calendar damage already accumulated — as `accumulated_qloss` and `accumulated_rinc`. The sub-model returns non-negative deltas, which are added to the accumulators and reflected on `state.soh_Q` and `state.soh_R`. +**Calendar pass (every step).** The calendar sub-model is asked for the capacity loss and resistance rise that accumulate over this timestep, given the current temperature and SOC. `DegradationModel` also hands it the current value of `qloss_cal` — the calendar damage already accumulated — as `accumulated_qloss`. The sub-model returns a non-negative delta, which is added to `qloss_cal` and subtracted from `state.soh_Q`. Resistance follows the same pattern through `rinc_cal` and `soh_R`. -**Cyclic pass (on direction reversal).** The cycle detector is advanced with the new SOC. If it signals a completed half-cycle, the cyclic sub-model is called with the `HalfCycle` object and the current `qloss_cyc` / `rinc_cyc` accumulators. Again the sub-model returns deltas, which are added to the accumulators and reflected on `state.soh_Q` / `state.soh_R`. If no half-cycle completes this step, the cyclic pass is skipped entirely. +**Cyclic pass (on direction reversal).** The cycle detector is advanced with the new SOC. If it signals a completed half-cycle, the cyclic sub-model is called with the `HalfCycle` object and the current `qloss_cyc` accumulator. Again the sub-model returns a delta, which is added to `qloss_cyc` and subtracted from `state.soh_Q`. If no half-cycle completes this step, the cyclic pass is skipped entirely. ### Why the accumulator is passed in Aging laws are typically nonlinear in their independent variable — calendar damage often grows as $\sqrt{t}$, $t^{0.75}$, or a double-exponential SEI form, and cyclic damage grows as $\sqrt{\text{FEC}}$ or a power law in charge throughput. Under *constant* stress these laws are straightforward to integrate. But in a real simulation, stress varies every timestep — temperature drifts, SOC swings, C-rate changes with operating profile — and a nonlinear law needs to know how much damage has already accumulated to compute the next increment correctly. -Passing `accumulated_qloss` / `accumulated_rinc` in as arguments lets the sub-model do this reconstruction on the fly without maintaining its own internal state. The `DegradationState` on `DegradationModel` is the *only* place aging state lives, which means checkpointing, warm-starting from a prior aging history, or swapping sub-models between runs all work without any coordination between the framework and the laws. Memoryless laws (e.g. linear-in-time calendar) are free to ignore the accumulators entirely. +Passing `accumulated_qloss` in as an argument lets the sub-model do this reconstruction on the fly without maintaining its own internal state. The `DegradationState` on `DegradationModel` is the *only* place aging state lives, which means checkpointing, warm-starting from a prior aging history, or swapping sub-models between runs all work without any coordination between the framework and the laws. Memoryless laws (e.g. linear-in-time calendar) are free to ignore the accumulator entirely. The concrete example below walks through one common continuation technique — virtual-time reconstruction — as used by the Sony LFP calendar model. diff --git a/docs/guides/extending-degradation.md b/docs/guides/extending-degradation.md index 9e312a7..80077e8 100644 --- a/docs/guides/extending-degradation.md +++ b/docs/guides/extending-degradation.md @@ -15,12 +15,12 @@ Fires **every timestep** — even when the battery is idle. ```python def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: ... -def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: ... +def update_resistance(self, state: BatteryState, dt: float) -> float: ... ``` - `state` — current battery state (read SOC, T, etc.). - `dt` — timestep in seconds. -- `accumulated_qloss` / `accumulated_rinc` — calendar capacity loss and resistance increase accumulated so far (p.u., ≥ 0). Your model reads these to continue a non-linear aging law under varying stress; memoryless laws (linear in time) can ignore them. +- `accumulated_qloss` — calendar capacity loss accumulated so far (p.u., ≥ 0). Your model reads this to continue a non-linear aging law under varying stress; memoryless laws can ignore it. - Returns a **non-negative delta** — never an absolute value. ### `CyclicDegradation` @@ -29,16 +29,16 @@ Fires **only on completed half-cycles** — `DegradationModel` delegates to the ```python def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: ... -def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: ... +def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: ... ``` - `half_cycle` — a [`HalfCycle`][simses.degradation.cycle_detector.HalfCycle] carrying `depth_of_discharge`, `mean_soc`, `c_rate`, and `full_equivalent_cycles`. -- Same accumulator pattern on both sides — virtual-FEC continuation available when the law is non-linear in throughput. +- Same `accumulated_qloss` pattern on the capacity side. - Same delta-only return convention. ### The statelessness rule -Both sub-models must be **stateless**. All accumulators live on the [`DegradationState`][simses.degradation.state.DegradationState] that `DegradationModel` owns. The framework passes `accumulated_qloss` into `update_capacity` and `accumulated_rinc` into `update_resistance` so your model can reconstruct history without storing anything internally. Memoryless laws (linear-in-time calendar R rise, linear-in-FEC cyclic R rise) are free to ignore the accumulator. +Both sub-models must be **stateless**. All accumulators live on the [`DegradationState`][simses.degradation.state.DegradationState] that `DegradationModel` owns. The framework passes `accumulated_qloss` into `update_capacity` so your model can reconstruct history without storing anything internally; resistance rise doesn't accumulate the same way (most rise laws are memoryless in their independent variable). This rule keeps checkpointing, warm-starts, and sub-model swapping trivial — the only state lives in one place. @@ -68,11 +68,11 @@ class SqrtTimeCalendar: t_virt = (accumulated_qloss / stress) ** 2 return stress * math.sqrt(t_virt + dt) - accumulated_qloss - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, dt: float) -> float: return 1e-8 * self._stress(state.T) / self.K_REF * dt ``` -The capacity method inverts the √t law at each call to find the *virtual* time that would have produced `accumulated_qloss` under the *current* stress, then steps forward — so T can change between steps without double-counting. If your law is linear in time (`dq = k · dt`), just ignore the accumulator and return `k(state) · dt`. If it follows a different exponent (`t^0.75`, SEI double-exponential, etc.), apply the same inversion principle with the right formula. The same principle applies to `update_resistance` via `accumulated_rinc` when the R-rise law is non-linear in time. +The capacity method inverts the √t law at each call to find the *virtual* time that would have produced `accumulated_qloss` under the *current* stress, then steps forward — so T can change between steps without double-counting. If your law is linear in time (`dq = k · dt`), just ignore `accumulated_qloss` and return `k(state) · dt`. If it follows a different exponent (`t^0.75`, SEI double-exponential, etc.), apply the same inversion principle with the right formula. **Cyclic** — `Δq_cyc = K_CYC · DoD² · ΔFEC` per completed half-cycle, no memory across cycles: @@ -87,7 +87,7 @@ class DodSquaredCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return self.K_CYC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: return self.K_RINC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles ``` diff --git a/examples/extending/custom_degradation.py b/examples/extending/custom_degradation.py index 8144283..e7d257f 100644 --- a/examples/extending/custom_degradation.py +++ b/examples/extending/custom_degradation.py @@ -61,8 +61,8 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo t_virt = (accumulated_qloss / stress) ** 2 return stress * math.sqrt(t_virt + dt) - accumulated_qloss - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: - """Resistance rise — linear in time, simple memoryless model (``accumulated_rinc`` unused).""" + def update_resistance(self, state: BatteryState, dt: float) -> float: + """Resistance rise — linear in time, simple memoryless model.""" return 1e-8 * self._stress(state.T) / self.K_REF * dt @@ -83,7 +83,7 @@ class DodSquaredCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return self.K_CYC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: return self.K_RINC * half_cycle.depth_of_discharge**2 * half_cycle.full_equivalent_cycles diff --git a/src/simses/degradation/calendar.py b/src/simses/degradation/calendar.py index ee3a075..851ecd1 100644 --- a/src/simses/degradation/calendar.py +++ b/src/simses/degradation/calendar.py @@ -28,15 +28,12 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo """ ... - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, dt: float) -> float: """Compute incremental calendar resistance increase. Args: state: Current battery state. dt: Timestep in seconds. - accumulated_rinc: Calendar resistance increase accumulated so far - (p.u., positive), used to seed virtual-time continuation. - Memoryless laws (e.g. linear-in-time) can ignore it. Returns: delta_soh_R — positive increment in p.u. (resistance increases). diff --git a/src/simses/degradation/cyclic.py b/src/simses/degradation/cyclic.py index 15138aa..47fb09f 100644 --- a/src/simses/degradation/cyclic.py +++ b/src/simses/degradation/cyclic.py @@ -29,15 +29,12 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate """ ... - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: """Compute incremental cyclic resistance increase for a completed half-cycle. Args: state: Current battery state. half_cycle: Stress factors of the completed half-cycle. - accumulated_rinc: Cyclic resistance increase accumulated so far - (p.u., positive), used to seed virtual-FEC continuation. - Memoryless laws (e.g. linear-in-FEC) can ignore it. Returns: delta_soh_R — positive increment in p.u. (resistance increases). diff --git a/src/simses/degradation/degradation.py b/src/simses/degradation/degradation.py index 5c8ad3a..4942b46 100644 --- a/src/simses/degradation/degradation.py +++ b/src/simses/degradation/degradation.py @@ -11,7 +11,7 @@ class _NoOpCalendar: def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: float) -> float: return 0.0 - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, dt: float) -> float: return 0.0 @@ -21,7 +21,7 @@ class _NoOpCyclic: def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulated_qloss: float) -> float: return 0.0 - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: return 0.0 @@ -86,7 +86,7 @@ def step(self, state: BatteryState, dt: float) -> None: """ # Calendar aging dq_cal = self.calendar.update_capacity(state, dt, self.state.qloss_cal) - dr_cal = self.calendar.update_resistance(state, dt, self.state.rinc_cal) + dr_cal = self.calendar.update_resistance(state, dt) self.state.qloss_cal += dq_cal self.state.rinc_cal += dr_cal state.soh_Q -= dq_cal @@ -96,7 +96,7 @@ def step(self, state: BatteryState, dt: float) -> None: if self.cycle_detector.step(state.soc, dt): half_cycle = self.cycle_detector.last_cycle dq_cyc = self.cyclic.update_capacity(state, half_cycle, self.state.qloss_cyc) - dr_cyc = self.cyclic.update_resistance(state, half_cycle, self.state.rinc_cyc) + dr_cyc = self.cyclic.update_resistance(state, half_cycle) self.state.qloss_cyc += dq_cyc self.state.rinc_cyc += dr_cyc state.soh_Q -= dq_cyc diff --git a/src/simses/model/degradation/sony_lfp_calendar.py b/src/simses/model/degradation/sony_lfp_calendar.py index 62860ab..34e2bcc 100644 --- a/src/simses/model/degradation/sony_lfp_calendar.py +++ b/src/simses/model/degradation/sony_lfp_calendar.py @@ -56,8 +56,7 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo return delta_q - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: - # accumulated_rinc is unused: this model is linear-in-time, no virtual-time needed. + def update_resistance(self, state: BatteryState, dt: float) -> float: if dt == 0.0: return 0.0 diff --git a/src/simses/model/degradation/sony_lfp_cyclic.py b/src/simses/model/degradation/sony_lfp_cyclic.py index 65cedb9..4e04648 100644 --- a/src/simses/model/degradation/sony_lfp_cyclic.py +++ b/src/simses/model/degradation/sony_lfp_cyclic.py @@ -51,8 +51,7 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate return delta_q - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: - # accumulated_rinc is unused: this model is linear-in-FEC, no virtual-FEC needed. + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: delta_fec = half_cycle.full_equivalent_cycles if delta_fec == 0.0: return 0.0 diff --git a/tests/test_degradation.py b/tests/test_degradation.py index 108988b..fbb02b5 100644 --- a/tests/test_degradation.py +++ b/tests/test_degradation.py @@ -26,7 +26,7 @@ def update_capacity(self, state: BatteryState, dt: float, accumulated_qloss: flo self.call_count += 1 return self.dq - def update_resistance(self, state: BatteryState, dt: float, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, dt: float) -> float: return self.dr @@ -42,7 +42,7 @@ def update_capacity(self, state: BatteryState, half_cycle: HalfCycle, accumulate self.call_count += 1 return self.dq - def update_resistance(self, state: BatteryState, half_cycle: HalfCycle, accumulated_rinc: float) -> float: + def update_resistance(self, state: BatteryState, half_cycle: HalfCycle) -> float: return self.dr diff --git a/tests/test_degradation_models.py b/tests/test_degradation_models.py index 1be6ff8..f800076 100644 --- a/tests/test_degradation_models.py +++ b/tests/test_degradation_models.py @@ -110,14 +110,14 @@ def test_capacity_loss_positive(self, cal_model): def test_delta_soh_r_positive(self, cal_model): """Calendar aging should increase resistance (delta_soh_R > 0).""" state = _make_state() - dr = cal_model.update_resistance(state, dt=3600.0, accumulated_rinc=0.0) + dr = cal_model.update_resistance(state, dt=3600.0) assert dr > 0 def test_zero_dt_zero_change(self, cal_model): """Zero timestep should produce zero degradation.""" state = _make_state() assert cal_model.update_capacity(state, dt=0.0, accumulated_qloss=0.0) == 0.0 - assert cal_model.update_resistance(state, dt=0.0, accumulated_rinc=0.0) == 0.0 + assert cal_model.update_resistance(state, dt=0.0) == 0.0 def test_longer_time_more_degradation(self, cal_model): """More time should produce more capacity loss.""" @@ -145,7 +145,7 @@ def test_delta_soh_r_positive(self, cyc_model): """Cyclic aging should increase resistance (delta_soh_R > 0).""" state = _make_state() hc = _make_half_cycle() - dr = cyc_model.update_resistance(state, hc, accumulated_rinc=0.0) + dr = cyc_model.update_resistance(state, hc) assert dr > 0 def test_zero_fec_zero_change(self, cyc_model): @@ -153,7 +153,7 @@ def test_zero_fec_zero_change(self, cyc_model): state = _make_state() hc = HalfCycle(depth_of_discharge=0.0, mean_soc=0.5, c_rate=0.5, full_equivalent_cycles=0.0) assert cyc_model.update_capacity(state, hc, accumulated_qloss=0.0) == 0.0 - assert cyc_model.update_resistance(state, hc, accumulated_rinc=0.0) == 0.0 + assert cyc_model.update_resistance(state, hc) == 0.0 # =================================================================== @@ -176,8 +176,8 @@ def test_higher_temperature_more_rinc(self): model_hot = SonyLFPCalendarDegradation() state_cold = _make_state(T=5.0) state_hot = _make_state(T=45.0) - dr_cold = model_cold.update_resistance(state_cold, dt=86400.0, accumulated_rinc=0.0) - dr_hot = model_hot.update_resistance(state_hot, dt=86400.0, accumulated_rinc=0.0) + dr_cold = model_cold.update_resistance(state_cold, dt=86400.0) + dr_hot = model_hot.update_resistance(state_hot, dt=86400.0) assert dr_hot > dr_cold def test_sqrt_time_behavior(self): @@ -199,20 +199,17 @@ def test_accumulated_loss_continuity(self): total_time = 86400.0 # 1 day dq_single = model.update_capacity(state, dt=total_time, accumulated_qloss=0.0) - dr_single = model.update_resistance(state, dt=total_time, accumulated_rinc=0.0) + dr_single = model.update_resistance(state, dt=total_time) n_steps = 100 accumulated_qloss = 0.0 - accumulated_rinc = 0.0 dq_total = 0.0 dr_total = 0.0 for _ in range(n_steps): dq = model.update_capacity(state, dt=total_time / n_steps, accumulated_qloss=accumulated_qloss) accumulated_qloss += dq dq_total += dq - dr = model.update_resistance(state, dt=total_time / n_steps, accumulated_rinc=accumulated_rinc) - accumulated_rinc += dr - dr_total += dr + dr_total += model.update_resistance(state, dt=total_time / n_steps) assert dq_total == pytest.approx(dq_single, rel=0.02) assert dr_total == pytest.approx(dr_single, rel=0.02) @@ -261,11 +258,10 @@ def test_accumulated_loss_continuity(self): total_fec = 1.0 hc_single = HalfCycle(depth_of_discharge=0.5, mean_soc=0.5, c_rate=0.5, full_equivalent_cycles=total_fec) dq_single = model.update_capacity(state, hc_single, accumulated_qloss=0.0) - dr_single = model.update_resistance(state, hc_single, accumulated_rinc=0.0) + dr_single = model.update_resistance(state, hc_single) n_steps = 100 accumulated_qloss = 0.0 - accumulated_rinc = 0.0 dq_total = 0.0 dr_total = 0.0 for _ in range(n_steps): @@ -278,9 +274,7 @@ def test_accumulated_loss_continuity(self): dq = model.update_capacity(state, hc, accumulated_qloss=accumulated_qloss) accumulated_qloss += dq dq_total += dq - dr = model.update_resistance(state, hc, accumulated_rinc=accumulated_rinc) - accumulated_rinc += dr - dr_total += dr + dr_total += model.update_resistance(state, hc) assert dq_total == pytest.approx(dq_single, rel=0.02) assert dr_total == pytest.approx(dr_single, rel=0.02)