Merge branch 'f3/thermal-cal-bev' of github.com:NREL/fastsim into f3/…

…thermal

cal_and_val/thermal/cal_bev.py

@@ -0,0 +1,153 @@
+"""
+Calibration script for 2020 Chevrolet Bolt EV    
+"""
+
+# critical import
+from pathlib import Path
+
+# anticipated cricital imports
+import numpy as np  # noqa: F401
+import matplotlib.pyplot as plt  # noqa: F401
+import seaborn as sns
+import pandas as pd  # noqa: F401
+import polars as pl  # noqa: F401
+
+import fastsim as fsim
+
+# Initialize seaborn plot configuration
+sns.set_style()
+
+veh = fsim.Vehicle.from_file(Path(__file__).parent / "f3-vehicles/2020 Chevrolet Bolt EV.yaml")
+
+# Obtain the data from
+# https://nrel.sharepoint.com/:f:/r/sites/EEMSCoreModelingandDecisionSupport2022-2024/Shared%20Documents/FASTSim/DynoTestData?csf=1&web=1&e=F4FEBp
+# and then copy it to the local folder below
+cyc_folder_path = Path(__file__) / "dyno_test_data/2020 Chevrolet Bolt EV/Extended Datasets"
+assert cyc_folder_path.exists()
+
+# See 2020_Chevrolet_Bolt_TestSummary_201005.xlsm for cycle-level data
+cyc_files = [
+    # TODO: check for seat heater usage in cold cycles and account for that in model!
+    # 20F (heater maybe on? Col R in test summary), UDDS + HWY + UDDS + US06
+    "62009051 Test Data.txt"
+    # 20F (heater maybe on? Col R in test summary), US06 + UDDS + HWY + UDDS
+    "62009053 Test Data.txt"
+
+    # room temperature (no HVAC), UDDS + HWY + UDDS + US06
+    "62009019 Test Data.txt",
+    # room temperature (no HVAC), US06 + UDDS + HWY + UDDS
+    "62009021 Test Data.txt",
+
+    # TODO: check for solar load (should be around 1 kW / m^2) and implement or this somewhere (`drive_cycle`???)
+    # 95F (HVAC on), UDDS + HWY + UDDS
+    "62009040 Test Data.txt"
+    # 95F (HVAC on), US06
+    "62009041 Test Data.txt"
+]
+assert len(cyc_files) > 0
+cyc_files = [cyc_folder_path / cyc_file for cyc_file in cyc_files]
+
+# TODO: use random selection to retain ~70% of cycles for calibration, and
+# reserve the remaining for validation
+cyc_files_for_cal = [
+    # TOOD: populate this somehow -- e.g. random split of `cyc_files`
+]
+assert len(cyc_files_for_cal) > 0
+dfs_for_cal = {}
+for cyc_file in cyc_files_for_cal:
+    cyc_file: Path
+    # `delimiter="\t"` should work for tab separated variables
+    dfs_for_cal[cyc_file.stem] = pd.read_csv(cyc_file, delimiter="\t")
+cycs_for_cal = {}
+for (cyc_file_stem, df) in dfs_for_cal.items():
+    cyc_file_stem: str
+    df: pd.DataFrame
+    cyc_dict = df.to_dict()
+    # TODO: be ready to do some massaging of `cyc_dict`, like making sure that
+    # keys match expected, purging invalid keys, and massaging data types
+
+    # TODO: make sure this catches ambient temperature
+    cycs_for_cal[cyc_file_stem] = fsim.Cycle.from_pydict(cyc_dict)
+sds_for_cal = {}
+for (cyc_file_stem, cyc) in cycs_for_cal.items():
+    cyc_file_stem: str
+    cyc: fsim.Cycle
+    # TODO: clone veh and set up initial conditions for:
+    # - SOC
+    # - cabin temp
+    # - battery temp if available, else use cabin temp
+    # - engine temp for HEV
+    # NOTE: maybe change `save_interval` to 5
+    sds_for_cal[cyc_file_stem] = fsim.SimDrive(veh, cyc).to_pydict()
+
+# TODO: flesh this out for validation stuff
+# cyc_files_for_val = []
+
+# Setup model objectives
+## Parameter Functions
+def new_em_eff_max(sd_dict, new_eff_peak):
+    """
+    Set `new_eff_max` in `ElectricMachine`
+    """
+    em = fsim.ElectricMachine.from_pydict(sd_dict['veh']['pt_type']['BatteryElectricVehicle']['em'])
+    em.set_eff_peak(new_eff_peak)
+    sd_dict['veh']['pt_type']['BatteryElectricVehicle']['em'] = em.to_pydict()
+    # TODO: check that `sd_dict` is mutably modified outside the scope of this function, e.g. with a debugger
+
+def new_em_eff_range(sd_dict, new_eff_range):
+    """
+    Set `new_eff_range` in `ElectricMachine`
+    """
+    em = fsim.ElectricMachine.from_pydict(sd_dict['veh']['pt_type']['BatteryElectricVehicle']['em'])
+    em.set_eff_range(new_eff_range)
+    sd_dict['veh']['pt_type']['BatteryElectricVehicle']['em'] = em.to_pydict()
+    # TODO: check that `sd_dict` is mutably modified outside the scope of this function, e.g. with a debugger
+
+## Model Objectives
+cal_mod_obj = fsim.pymoo_api.ModelObjectives(
+    models = sds_for_cal,
+    dfs = dfs_for_cal,
+    obj_fns=(
+        (
+            lambda sd_dict: np.array(sd_dict['veh']['pt_type']['BatteryElectricVehicle']['res']['history']['soc']),
+            lambda df: df['HVBatt_SOC_CAN4__per']
+        ),
+        # TODO: add objectives for:
+        # - battery temperature
+        (
+            lambda sd_dict: np.array(sd_dict['veh']['pt_type']['BatteryElectricVehicle']['res']['thermal']['RESLumpedThermal']['history']['temperature_kelvin']),
+            # HVBatt_cell_temp_1_CAN3__C (or average of temps?) or HVBatt_pack_average_temp_HPCM2__C?
+            lambda df: df['HVBatt_pack_average_temp_HPCM2__C']
+        ),
+        # - cabin temperature
+        # - HVAC power, if available
+    ),
+    param_fns=(
+        new_em_eff_max,
+        new_em_eff_range,
+        # TODO: make sure this has functions for modifying
+        # - HVAC PID controls for cabin (not for battery because Sonata has
+        #   passive thermal management, but make sure to do battery thermal
+        #   controls for BEV)
+        # - battery thermal
+        #     - thermal mass
+        #     - convection to ambient
+        #     - convection to cabin
+        # - cabin thermal
+        #     - thermal mass
+        #     - length
+        #     - htc to amb when stopped
+        #     - set width from vehicle specs -- no need to calibrate
+    ),
+    # must match order and length of `params_fns`
+    bounds=(
+        (0.80, 0.99),
+        (0.1, 0.6),
+    ),
+
+)
+
+# Setup calibration problem
+cal_prob = fsim.pymoo_api.CalibrationProblem(
+    mod_obj=cal_mod_obj,
+)

cal_and_val/thermal/f3-vehicles/2020 Chevrolet Bolt EV.yaml

@@ -5,9 +5,9 @@ year: 2020
 cabin: 
   LumpedCabin:
     # 0.05 is Chad's uncalibrated estimate
-    cab_shell_htc_to_amb_watts_per_square_meter_kelvin: 0.05
+    cab_shell_htc_to_amb_watts_per_square_meter_degree_celsius: 0.05
     # 0.05 is Chad's uncalibrated estimate
-    cab_htc_to_amb_stop_watts_per_square_meter_kelvin: 0.05
+    cab_htc_to_amb_stop_watts_per_square_meter_degree_celsius: 0.05
     # 200,000 is Chad's uncalibrated estimate
     heat_capacitance_joules_per_kelvin: 200000
     # TODO: get actual vehicle length, but calibrate this later

cal_and_val/thermal/f3-vehicles/2021_Hyundai_Sonata_Hybrid_Blue.yaml

@@ -94,7 +94,7 @@ pt_type:
         FuelConverterThermal:
           heat_capacitance_joules_per_kelvin: 200000.0
           length_for_convection_meters: 1.0
-          htc_to_amb_stop_watts_per_square_meter_kelvin: 50.0
+          htc_to_amb_stop_watts_per_square_meter_degree_celsius: 50.0
           conductance_from_comb_watts_per_kelvin: 5.0
           max_frac_from_comb: 0.5
           tstat_te_sto_kelvin: 358.15
@@ -226,8 +226,8 @@ chassis:
   cargo_mass_kilograms: ~
 cabin:
   LumpedCabin:
-    cab_shell_htc_to_amb_watts_per_square_meter_kelvin: 0.05
-    cab_htc_to_amb_stop_watts_per_square_meter_kelvin: 0.05
+    cab_shell_htc_to_amb_watts_per_square_meter_degree_celsius: 0.05
+    cab_htc_to_amb_stop_watts_per_square_meter_degree_celsius: 0.05
     heat_capacitance_joules_per_kelvin: 200000.0
     length_meters: 2.5
     width_meters: 2.0