added files that should have already been tracked

python/fastsim/demos/demo_hev_thrml_ws_ca.py

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+# %%
+from plot_utils import *
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.figure import Figure
+from matplotlib.axes import Axes
+import seaborn as sns
+from pathlib import Path
+import time
+import json
+import os
+from typing import Tuple
+import fastsim as fsim
+
+sns.set_theme()
+
+
+# if environment var `SHOW_PLOTS=false` is set, no plots are shown
+SHOW_PLOTS = os.environ.get("SHOW_PLOTS", "true").lower() == "true"
+# if environment var `SAVE_FIGS=true` is set, save plots
+SAVE_FIGS = os.environ.get("SAVE_FIGS", "false").lower() == "true"
+
+celsius_to_kelvin = 273.15
+temp_amb = -6.7 + celsius_to_kelvin
+temp_init_bat_and_cab = 22.0 + celsius_to_kelvin
+temp_init_eng = 70.0 + celsius_to_kelvin
+# `fastsim3` -- load vehicle and cycle, build simulation, and run
+# %%
+
+# load 2021 Hyundai Sonata HEV from file
+veh_dict = fsim.Vehicle.from_file(
+    fsim.package_root() /
+    "../../cal_and_val/thermal/f3-vehicles/2021_Hyundai_Sonata_Hybrid_Blue.yaml"
+).to_pydict()
+veh_dict['cabin']['LumpedCabin']['state']['temperature_kelvin'] = temp_init_bat_and_cab
+veh_dict['pt_type']['HybridElectricVehicle']['res']['thrml']['RESLumpedThermal']['state']['temperature_kelvin'] = temp_init_bat_and_cab
+veh_dict['pt_type']['HybridElectricVehicle']['fc']['thrml']['FuelConverterThermal']['state']['temperature_kelvin'] = temp_init_eng
+veh = fsim.Vehicle.from_pydict(veh_dict)
+
+# Set `save_interval` at vehicle level -- cascades to all sub-components with time-varying states
+fsim.set_param_from_path(veh, "save_interval", 1)
+
+# load cycle from file
+cyc_dict = fsim.Cycle.from_resource("udds.csv").to_pydict()
+cyc_dict['temp_amb_air_kelvin'] = [temp_amb] * len(cyc_dict['time_seconds'])
+cyc = fsim.Cycle.from_pydict(cyc_dict)
+
+# instantiate `SimDrive` simulation object
+sd = fsim.SimDrive(veh, cyc)
+
+# simulation start time
+t0 = time.perf_counter()
+# run simulation
+sd.walk()
+# simulation end time
+t1 = time.perf_counter()
+t_fsim3_si1 = t1 - t0
+print(
+    f"fastsim-3 `sd.walk()` elapsed time with `save_interval` of 1:\n{t_fsim3_si1:.2e} s")
+
+# %%
+df = sd.to_dataframe(allow_partial=True)
+sd_dict = sd.to_pydict(flatten=True)
+# # Visualize results
+
+
+def plot_temperatures() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(2, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Component Temperatures")
+
+    ax[0].set_prop_cycle(get_uni_cycler())
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["cyc.temp_amb_air_kelvin"] - 273.15,
+        label="amb",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["veh.cabin.LumpedCabin.history.temperature_kelvin"] - 273.15,
+        label="cabin",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.res.thrml." +
+            "RESLumpedThermal.history.temperature_kelvin"] - 273.15,
+        label="res",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.fc.thrml." +
+            "FuelConverterThermal.history.temperature_kelvin"] - 273.15,
+        label="fc",
+    )
+    ax[0].set_ylabel("Temperatures [°C]")
+    ax[0].legend()
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df["cyc.time_seconds"],
+        df["veh.history.speed_ach_meters_per_second"],
+        label="ach",
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Ach Speed [m/s]")
+    x_min, x_max = ax[-1].get_xlim()[0], ax[-1].get_xlim()[1]
+    x_max = (x_max - x_min) * 1.15
+    ax[-1].set_xlim([x_min, x_max])
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path("./plots/temps.svg"))
+
+    return fig, ax
+
+
+def plot_fc_pwr() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(3, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Fuel Converter Power")
+
+    ax[0].set_prop_cycle(get_paired_cycler())
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        (df["veh.pt_type.HybridElectricVehicle.fc.history.pwr_prop_watts"] +
+            df["veh.pt_type.HybridElectricVehicle.fc.history.pwr_aux_watts"]) / 1e3,
+        label="shaft",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.fc.history.pwr_fuel_watts"] / 1e3,
+        label="fuel",
+    )
+    ax[0].set_ylabel("FC Power [kW]")
+    ax[0].legend()
+
+    ax[1].set_prop_cycle(get_paired_cycler())
+    ax[1].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.res.history.soc"],
+        label="soc",
+    )
+    ax[1].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.res.history.soc_disch_buffer"],
+        label='accel buffer',
+        alpha=0.5,
+    )
+    # ax[1].plot(
+    #     df["cyc.time_seconds"],
+    #     df["veh.pt_type.HybridElectricVehicle.res.history.soc_regen_buffer"],
+    #     label='regen buffer',
+    #     alpha=0.5,
+    # )
+    # ax[1].plot(
+    #     df["cyc.time_seconds"],
+    #     df['veh.pt_type.HybridElectricVehicle.fc.history.eff'],
+    #     label='FC eff',
+    # )
+    ax[1].set_ylabel("[-]")
+    ax[1].legend(loc="center right")
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df["cyc.time_seconds"],
+        df["veh.history.speed_ach_meters_per_second"],
+        label="ach",
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Ach Speed [m/s]")
+    x_min, x_max = ax[-1].get_xlim()[0], ax[-1].get_xlim()[1]
+    x_max = (x_max - x_min) * 1.15
+    ax[-1].set_xlim([x_min, x_max])
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path("./plots/fc_pwr.svg"))
+
+    return fig, ax
+
+
+def plot_fc_energy() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(2, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Fuel Converter Energy")
+
+    ax[0].set_prop_cycle(get_paired_cycler())
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        (df["veh.pt_type.HybridElectricVehicle.fc.history.energy_prop_joules"] +
+            df["veh.pt_type.HybridElectricVehicle.fc.history.energy_aux_joules"]) / 1e6,
+        label="shaft",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"],
+        df["veh.pt_type.HybridElectricVehicle.fc.history.energy_fuel_joules"] / 1e6,
+        label="fuel",
+    )
+    ax[0].set_ylabel("FC Energy [MJ]")
+    ax[0].legend()
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df["cyc.time_seconds"],
+        df["veh.history.speed_ach_meters_per_second"],
+        label="ach",
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Ach Speed [m/s]")
+    x_min, x_max = ax[-1].get_xlim()[0], ax[-1].get_xlim()[1]
+    x_max = (x_max - x_min) * 1.15
+    ax[-1].set_xlim([x_min, x_max])
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path(f"./plots/fc_energy.svg"))
+
+    return fig, ax
+
+
+def plot_res_pwr() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(3, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Reversible Energy Storage Power")
+
+    ax[0].set_prop_cycle(get_paired_cycler())
+    ax[0].plot(
+        df['cyc.time_seconds'],
+        df["veh.pt_type.HybridElectricVehicle.res.history.pwr_out_electrical_watts"] / 1e3,
+        label="electrical out",
+    )
+    ax[0].set_ylabel("RES Power [kW]")
+    ax[0].legend()
+
+    ax[1].set_prop_cycle(get_paired_cycler())
+    ax[1].plot(
+        df['cyc.time_seconds'],
+        df["veh.pt_type.HybridElectricVehicle.res.history.soc"],
+        label="soc",
+    )
+    ax[1].set_ylabel("SOC")
+    ax[1].legend()
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df['cyc.time_seconds'],
+        df["veh.history.speed_ach_meters_per_second"],
+        label='ach',
+    )
+    ax[-1].plot(
+        df['cyc.time_seconds'],
+        df["cyc.speed_meters_per_second"],
+        label='cyc',
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Speed [m/s]")
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path("./plots/res_pwr.svg"))
+
+    return fig, ax
+
+
+def plot_res_energy() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(3, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Reversible Energy Storage Energy")
+
+    ax[0].set_prop_cycle(get_paired_cycler())
+    ax[0].plot(
+        df['cyc.time_seconds'],
+        df["veh.pt_type.HybridElectricVehicle.res.history.energy_out_electrical_joules"] / 1e3,
+        label="electrical out",
+    )
+    ax[0].set_ylabel("RES Energy [kW]")
+    ax[0].legend()
+
+    ax[1].set_prop_cycle(get_paired_cycler())
+    ax[1].plot(
+        df['cyc.time_seconds'],
+        df["veh.pt_type.HybridElectricVehicle.res.history.soc"],
+        label="soc",
+    )
+    ax[1].set_ylabel("SOC")
+    ax[1].legend()
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df['cyc.time_seconds'],
+        df["veh.history.speed_ach_meters_per_second"],
+        label="ach",
+    )
+    ax[-1].plot(
+        df['cyc.time_seconds'],
+        df["cyc.speed_meters_per_second"],
+        label='cyc',
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Speed [m/s]")
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path("./plots/res_energy.svg"))
+
+    return fig, ax
+
+
+def plot_road_loads() -> Tuple[Figure, Axes]:
+    fig, ax = plt.subplots(2, 1, sharex=True, figsize=figsize_3_stacked)
+    plt.suptitle("Road Loads")
+
+    ax[0].set_prop_cycle(get_uni_cycler())
+    ax[0].plot(
+        df["cyc.time_seconds"][::veh.save_interval],
+        df["veh.history.pwr_drag_watts"] / 1e3,
+        label="drag",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"][::veh.save_interval],
+        df["veh.history.pwr_rr_watts"] / 1e3,
+        label="rr",
+    )
+    ax[0].plot(
+        df["cyc.time_seconds"][::veh.save_interval],
+        df["veh.history.pwr_tractive_watts"] / 1e3,
+        label="total",
+    )
+    ax[0].set_ylabel("Power [kW]")
+    ax[0].legend()
+
+    ax[-1].set_prop_cycle(get_paired_cycler())
+    ax[-1].plot(
+        df["cyc.time_seconds"][::veh.save_interval],
+        df["veh.history.speed_ach_meters_per_second"],
+        label="ach",
+    )
+    ax[-1].legend()
+    ax[-1].set_xlabel("Time [s]")
+    ax[-1].set_ylabel("Ach. Speed [m/s]")
+
+    plt.tight_layout()
+    if SAVE_FIGS:
+        plt.savefig(Path("./plots/road_loads.svg"))
+
+    return fig, ax
+
+
+if SHOW_PLOTS:
+    fig_fc_pwr, ax_fc_pwr = plot_fc_pwr()
+    fig_fc_energy, ax_fc_energy = plot_fc_energy()
+    fig_res_pwr, ax_res_pwr = plot_res_pwr()
+    fig_res_energy, ax_res_energy = plot_res_energy()
+    fig_temps, ax_temps = plot_temperatures()
+    fig, ax = plot_road_loads()
+    plt.show()
+# %%
+
+# %%
+# example for how to use set_default_pwr_interp() method for veh.res
+res = fsim.ReversibleEnergyStorage.from_pydict(
+    sd.to_pydict()['veh']['pt_type']['HybridElectricVehicle']['res'])
+res.set_default_pwr_interp()
+
+# %%