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Opportunity Costs module

t3co.tco.opportunity_cost

oc = main() module-attribute

OpportunityCost

This class is used to calculate the different opportunity costs for a scenario and vehicle - Payload Capacity Cost Multiplier - Fueling Downtime Cost - Maintenance and Repair Downtime Cost

Source code in t3co/tco/opportunity_cost.py
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class OpportunityCost:
    """
    This class is used to calculate the different opportunity costs for a scenario and vehicle
    - Payload Capacity Cost Multiplier
    - Fueling Downtime Cost
    - Maintenance and Repair Downtime Cost
    """

    def __init__(
        self, scenario: run_scenario.Scenario, range_dict: dict = None, **kwargs
    ) -> None:
        """
        Initializes OpportunityCost object using Scenario object, range_dict (from fueleconomy module), and other arguments

        Args:
            scenario (run_scenario.Scenario): Scenario object
            range_dict (dict, optional): dictionary containing primary_fuel_range_mi from fueleconomy.get_range_mi function. Defaults to None.
        """

        self.payload_cap_cost_multiplier = None

        if range_dict:
            self.total_range_mi = range_dict["primary_fuel_range_mi"]
        else:
            self.total_range_mi = scenario.target_range_mi

        if scenario.activate_tco_fueling_dwell_time_cost:
            self.frac_of_fullcharge_bounds = list(
                np.float_(scenario.fdt_frac_full_charge_bounds.strip(" ][").split(","))
            )
            if (
                "0" in str(scenario.shifts_per_year)
                or np.isnan(scenario.shifts_per_year)
            ) and scenario.constant_trip_distance_mi:
                self.shifts_per_year = [
                    round(scenario.vmt[i] / scenario.constant_trip_distance_mi)
                    for i in range(scenario.vehicle_life_yr)
                ]
            else:
                self.shifts_per_year = ast.literal_eval(scenario.shifts_per_year)

        self.payload_cap_cost_multiplier = 0

        if len(kwargs) > 0:
            warnings.warn(f"Invalid kwargs: {list(kwargs.keys())}")

        # weight distribution file to load
        self.wt_dist_file = kwargs.pop(
            "wt_dist_file",
            Path(os.path.abspath(__file__)).parents[1]
            / "resources"
            / "auxiliary"
            / "tractorweightvars.csv",
        )
        self.df_veh_wt = pd.read_csv(self.wt_dist_file, index_col=0)

    def set_kdes(
        self,
        scenario: run_scenario.Scenario,
        bw_method: float = 0.15,
        verbose: bool = False,
    ) -> None:
        """
        This method sets tje kde kernel. This is time-consuming, only call this once, if possible.

        Args:
            scenario (run_scenario.Scenario): Scenario object
            bw_method (float, optional):  kernel bandwidth method used by guassian_kde. Defaults to .15.
            verbose (bool, optional): if True, prints process sets. Defaults to False.
        """

        if verbose:
            print("Initializing kernels.")

        self.df_veh_wt = self.df_veh_wt[~self.df_veh_wt["WEIGHTAVG"].isnull()]
        self.df_veh_wt = self.df_veh_wt[~self.df_veh_wt["WEIGHTEMPTY"].isnull()]
        self.df_veh_wt = self.df_veh_wt[self.df_veh_wt["WEIGHTAVG"] < 120000]

        weights = self.df_veh_wt["TAB_MILES"] / np.nansum(self.df_veh_wt["TAB_MILES"])
        kernel = gaussian_kde(
            self.df_veh_wt["WEIGHTAVG"], weights=weights, bw_method=bw_method
        )
        self.vehicle_weights_bins_lb = np.linspace(
            self.df_veh_wt["WEIGHTAVG"].min(), self.df_veh_wt["WEIGHTAVG"].max(), 1000
        )
        self.vehicle_weights_bins_kg = gl.lbs_to_kgs(self.vehicle_weights_bins_lb)

        # get probability of each vehicle weight
        self.p_of_weights = kernel(self.vehicle_weights_bins_lb)

        probability_payload = pd.DataFrame(
            [self.vehicle_weights_bins_kg, self.p_of_weights],
            index=["vehicle_weights_bins_kg", "p_of_weights"],
        ).T
        probability_payload.to_csv(
            Path(self.wt_dist_file).parents[0] / "payload_pdf.csv"
        )
        normalization_factor = probability_payload[
            probability_payload["vehicle_weights_bins_kg"].between(
                scenario.plf_ref_veh_empty_mass_kg, scenario.gvwr_kg
            )
        ]["p_of_weights"].sum()
        self.p_of_weights_normalized = self.p_of_weights / normalization_factor

    def set_payload_loss_factor(
        self,
        a_vehicle: fastsim.vehicle.Vehicle,
        scenario: run_scenario.Scenario,
        plots: bool = False,
        plots_dir: str = None,
    ) -> None:
        """
        This method runs teh kernel density estimation function set_kdes and calculates the payload capacity loss factor (payload_cap_cost_multiplier) \
            of the new vehicle compared to a conventional vehicle's reference empty weight.

        Args:
            a_vehicle (fastsim.vehicle): FASTSim vehicle object of the analysis vehicle
            scenario (run_scenario.Scenario): Scenario object of current selection
            plots (bool, optional): if True, creates histogram of KDE weight bins. Defaults to False.
            plots_dir (str, optional): output diretory for saving plot figure. Defaults to None.
        """
        self.set_kdes(scenario, verbose=False)

        new_empty_weight_kg = a_vehicle.veh_kg - a_vehicle.cargo_kg
        empty_increase_kg = new_empty_weight_kg - scenario.plf_ref_veh_empty_mass_kg
        new_cargo_cieling_kg = (
            scenario.gvwr_kg - empty_increase_kg + scenario.gvwr_credit_kg
        )

        # determine indices where lost cargo capacity is bounded in vehicle_weights
        # and get the corresponding indices for p_of_weights
        # based on current vehicle's new_cargo_cieling_lb and base_vehicle_gvwr_lb

        if empty_increase_kg >= scenario.gvwr_credit_kg:
            a = self.vehicle_weights_bins_kg - new_cargo_cieling_kg
            # print(a)
            minidx = (
                np.where(
                    self.vehicle_weights_bins_kg == a[a > 0][0] + new_cargo_cieling_kg
                )
            )[0][0]
            a = self.vehicle_weights_bins_kg - scenario.gvwr_kg
            maxidx = (
                np.where(self.vehicle_weights_bins_kg == a[a > 0][0] + scenario.gvwr_kg)
            )[0][0]

            estimated_lost_payload_per_bin_kg = self.p_of_weights_normalized[
                minidx:maxidx
            ] * (self.vehicle_weights_bins_kg[minidx:maxidx] - new_cargo_cieling_kg)
            estimated_lost_payload_kg = np.trapz(estimated_lost_payload_per_bin_kg)

            # payload cost multiplier
            self.payload_cap_cost_multiplier = 1 + estimated_lost_payload_kg / (
                scenario.gvwr_kg - new_empty_weight_kg + scenario.gvwr_credit_kg
            )

            scenario.estimated_lost_payload_kg = estimated_lost_payload_kg
        else:
            self.payload_cap_cost_multiplier = 1
        # recording final report data on vehicle empty weights and cargo capcities
        scenario.plf_scenario_vehicle_empty_kg = new_empty_weight_kg
        scenario.plf_reference_vehicle_cargo_capacity_kg = (
            scenario.gvwr_kg - scenario.plf_ref_veh_empty_mass_kg
        )
        scenario.plf_scenario_vehicle_cargo_capacity_kg = (
            scenario.gvwr_kg + scenario.gvwr_credit_kg - new_empty_weight_kg
        )

        def make_plots(save_dir: str = None) -> None:
            """
            This function generates a histogram of the payload KDE weight distribution

            Args:
                save_dir (str, optional): Output directory path to save plot figure. Defaults to None.
            """
            if save_dir and not Path(save_dir).exists():
                save_dir.mkdir()

            fig, ax = plt.subplots(figsize=(9, 6))
            ax.hist(
                gl.lbs_to_kgs(self.df_veh_wt["WEIGHTAVG"]),
                bins=50,
                label="WEIGHTAVG: operating weight [kg]",
            )
            ax2 = ax.twinx()
            ax2.plot(
                self.vehicle_weights_bins_kg,
                self.p_of_weights,
                color="red",
                linewidth=3,
                label="KDE",
            )
            ax2.fill_between(
                x=self.vehicle_weights_bins_kg[minidx:maxidx],
                y1=self.p_of_weights[minidx:maxidx],
                y2=[0] * len(self.p_of_weights[minidx:maxidx]),
                color="red",
                alpha=0.3,
                label=f"estimated cargo lost [kg]: {round(estimated_lost_payload_kg)}",
            )
            ax2.axvline(
                scenario.gvwr_kg,
                label=f"GVWR [kg] {round(scenario.gvwr_kg)}",
                color="orange",
            )
            ax2.axvline(
                new_cargo_cieling_kg,
                label=f"GVWR + credit - empty weight increase [kg] : {round(scenario.gvwr_kg)} + {round(scenario.gvwr_credit_kg)}  - {round(empty_increase_kg)} = {round(new_cargo_cieling_kg)}",
                color="purple",
            )
            fig.suptitle(
                f"payload cost multiplier: {round(self.payload_cap_cost_multiplier,2)}"
            )
            fig.legend()
            plt.show()

        if plots:
            make_plots(plots_dir)

    def set_fueling_dwell_time_cost(
        self, a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario
    ) -> None:
        """
        This function calculates the fueling dwell time cost for a vehicle based on fuel fill rate/charging power and shifts_per_year

        Args:
            a_vehicle (fastsim.vehicle): FASTSim vehicle object of analysis vehicle
            scenario (run_scenario.Scenario): Scenario object for current selection
        """
        self.total_fueling_dwell_time_hr = 0
        self.net_fueling_dwell_time_hr_per_yr = []
        self.fueling_downtime_oppy_cost_dol_per_yr = []
        self.fueling_dwell_labor_cost_dol_per_yr = []
        dwellparams = np.array(
            [
                scenario.fdt_dwpt_fraction_power_pct,
                scenario.fdt_frac_full_charge_bounds,
                scenario.fdt_avg_overhead_hr_per_dwell_hr,
                scenario.downtime_oppy_cost_dol_per_hr,
            ]
        )
        assert any(
            dwellparams
        ), f"Missing parameters in {str(dwellparams)}: {np.isnan(dwellparams)}"

        if a_vehicle.veh_pt_type in ["BEV"]:
            self.full_dwell_hr = (1 - scenario.fdt_dwpt_fraction_power_pct) * (
                a_vehicle.ess_max_kwh / scenario.ess_max_charging_power_kw
            )
        elif a_vehicle.veh_pt_type in ["Conv"]:
            if scenario.fuel_type in ["gasoline"]:
                self.full_dwell_hr = (
                    a_vehicle.fs_kwh
                    / (gl.kwh_per_gge)
                    / scenario.fs_fueling_rate_gasoline_gpm
                ) / 60
            else:
                self.full_dwell_hr = (
                    a_vehicle.fs_kwh
                    / (gl.kwh_per_gge / gl.DieselGalPerGasGal)
                    / scenario.fs_fueling_rate_diesel_gpm
                ) / 60
        else:
            self.full_dwell_hr = (
                (1 - scenario.fdt_dwpt_fraction_power_pct)
                * (
                    a_vehicle.fs_kwh
                    / (gl.kwh_per_gge / gl.kgH2_per_gge)
                    / scenario.fs_fueling_rate_kg_per_min
                )
                / 60
            )

        for i in range(scenario.vehicle_life_yr):
            self.d_trip_mi = scenario.vmt[i] / self.shifts_per_year[i]
            self.num_of_dwells = max(
                0,
                (
                    (self.d_trip_mi)
                    * (1 - scenario.fdt_dwpt_fraction_power_pct)
                    / self.total_range_mi
                    - scenario.fdt_num_free_dwell_trips
                ),
            )

            if self.num_of_dwells != 0:
                self.remaining_dwells = self.num_of_dwells % 1
                if self.remaining_dwells < self.frac_of_fullcharge_bounds[0]:
                    self.num_of_dwells += (
                        self.frac_of_fullcharge_bounds[0] - self.remaining_dwells
                    )
                elif (
                    self.frac_of_fullcharge_bounds[0]
                    < self.remaining_dwells
                    < self.frac_of_fullcharge_bounds[1]
                ):
                    self.num_of_dwells += 0
                else:
                    self.num_of_dwells += 1 - self.remaining_dwells

            if (self.num_of_dwells < 1 and not scenario.fdt_num_free_dwell_trips) or (
                scenario.fuel_type
            ):
                self.net_fueling_dwell_time_hr_per_yr.append(
                    scenario.vmt[i]
                    * (1 - scenario.fdt_dwpt_fraction_power_pct)
                    / self.total_range_mi
                    * (self.full_dwell_hr + scenario.fdt_avg_overhead_hr_per_dwell_hr)
                )
            else:
                self.dwell_time_hr = (
                    self.num_of_dwells * self.full_dwell_hr
                    + ceil(self.num_of_dwells)
                    * scenario.fdt_avg_overhead_hr_per_dwell_hr
                )
                self.net_fueling_dwell_time_hr_per_yr.append(
                    self.shifts_per_year[i]
                    * max(
                        0,
                        (
                            self.dwell_time_hr
                            - max(0, scenario.fdt_available_freetime_hr)
                        ),
                    )
                )

            self.fueling_dwell_labor_cost_dol_per_yr.append(
                self.net_fueling_dwell_time_hr_per_yr[i]
                * scenario.labor_rate_dol_per_hr
            )
            self.fueling_downtime_oppy_cost_dol_per_yr.append(
                self.net_fueling_dwell_time_hr_per_yr[i]
                * scenario.downtime_oppy_cost_dol_per_hr
            )

            self.total_fueling_dwell_time_hr += self.net_fueling_dwell_time_hr_per_yr[i]

    def set_M_R_downtime_cost(
        self, a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario
    ) -> None:
        """
        This function calculates the Maintenance and Repair (M&R) downtime cost based on planned, unplanned, and tire replacement downtime inputs

        Args:
            a_vehicle (fastsim.vehicle): FASTSim object of the analysis vehicle
            scenario (run_scenario.Scenario): Scenario object for the current selection
        """
        self.planned_downtime_hr = [
            scenario.mr_planned_downtime_hr_per_yr
            for _ in range(scenario.vehicle_life_yr)
        ]  # regular maintenance and inspections
        self.unplanned_downtime_hr = [
            scenario.mr_unplanned_downtime_hr_per_mi[i] * scenario.vmt[i]
            for i in range(scenario.vehicle_life_yr)
        ]  # increases with age
        self.tire_replacement_downtime_hr = [
            (scenario.vmt[i])
            / scenario.mr_avg_tire_life_mi
            * scenario.mr_tire_replace_downtime_hr_per_event
            for i in range(scenario.vehicle_life_yr)
        ]
        self.net_net_mr_downtime_hr_per_yr_per_yr = np.array(
            [
                self.planned_downtime_hr[i]
                + self.unplanned_downtime_hr[i]
                + self.tire_replacement_downtime_hr[i]
                for i in range(scenario.vehicle_life_yr)
            ]
        )
        self.mr_downtime_oppy_cost_dol_per_yr = (
            self.net_net_mr_downtime_hr_per_yr_per_yr
            * scenario.downtime_oppy_cost_dol_per_hr
        )

df_veh_wt = pd.read_csv(self.wt_dist_file, index_col=0) instance-attribute

frac_of_fullcharge_bounds = list(np.float_(scenario.fdt_frac_full_charge_bounds.strip(' ][').split(','))) instance-attribute

payload_cap_cost_multiplier = 0 instance-attribute

shifts_per_year = [round(scenario.vmt[i] / scenario.constant_trip_distance_mi) for i in range(scenario.vehicle_life_yr)] instance-attribute

total_range_mi = range_dict['primary_fuel_range_mi'] instance-attribute

wt_dist_file = kwargs.pop('wt_dist_file', Path(os.path.abspath(__file__)).parents[1] / 'resources' / 'auxiliary' / 'tractorweightvars.csv') instance-attribute

__init__(scenario: run_scenario.Scenario, range_dict: dict = None, **kwargs) -> None

Initializes OpportunityCost object using Scenario object, range_dict (from fueleconomy module), and other arguments

Parameters:

Name Type Description Default
scenario run_scenario.Scenario

Scenario object

 required
range_dict dict

dictionary containing primary_fuel_range_mi from fueleconomy.get_range_mi function. Defaults to None.

 None
Source code in t3co/tco/opportunity_cost.py
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def __init__(
    self, scenario: run_scenario.Scenario, range_dict: dict = None, **kwargs
) -> None:
    """
    Initializes OpportunityCost object using Scenario object, range_dict (from fueleconomy module), and other arguments

    Args:
        scenario (run_scenario.Scenario): Scenario object
        range_dict (dict, optional): dictionary containing primary_fuel_range_mi from fueleconomy.get_range_mi function. Defaults to None.
    """

    self.payload_cap_cost_multiplier = None

    if range_dict:
        self.total_range_mi = range_dict["primary_fuel_range_mi"]
    else:
        self.total_range_mi = scenario.target_range_mi

    if scenario.activate_tco_fueling_dwell_time_cost:
        self.frac_of_fullcharge_bounds = list(
            np.float_(scenario.fdt_frac_full_charge_bounds.strip(" ][").split(","))
        )
        if (
            "0" in str(scenario.shifts_per_year)
            or np.isnan(scenario.shifts_per_year)
        ) and scenario.constant_trip_distance_mi:
            self.shifts_per_year = [
                round(scenario.vmt[i] / scenario.constant_trip_distance_mi)
                for i in range(scenario.vehicle_life_yr)
            ]
        else:
            self.shifts_per_year = ast.literal_eval(scenario.shifts_per_year)

    self.payload_cap_cost_multiplier = 0

    if len(kwargs) > 0:
        warnings.warn(f"Invalid kwargs: {list(kwargs.keys())}")

    # weight distribution file to load
    self.wt_dist_file = kwargs.pop(
        "wt_dist_file",
        Path(os.path.abspath(__file__)).parents[1]
        / "resources"
        / "auxiliary"
        / "tractorweightvars.csv",
    )
    self.df_veh_wt = pd.read_csv(self.wt_dist_file, index_col=0)

set_M_R_downtime_cost(a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario) -> None

This function calculates the Maintenance and Repair (M&R) downtime cost based on planned, unplanned, and tire replacement downtime inputs

Parameters:

Name Type Description Default
a_vehicle fastsim.vehicle

FASTSim object of the analysis vehicle

 required
scenario run_scenario.Scenario

Scenario object for the current selection

 required
Source code in t3co/tco/opportunity_cost.py
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def set_M_R_downtime_cost(
    self, a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario
) -> None:
    """
    This function calculates the Maintenance and Repair (M&R) downtime cost based on planned, unplanned, and tire replacement downtime inputs

    Args:
        a_vehicle (fastsim.vehicle): FASTSim object of the analysis vehicle
        scenario (run_scenario.Scenario): Scenario object for the current selection
    """
    self.planned_downtime_hr = [
        scenario.mr_planned_downtime_hr_per_yr
        for _ in range(scenario.vehicle_life_yr)
    ]  # regular maintenance and inspections
    self.unplanned_downtime_hr = [
        scenario.mr_unplanned_downtime_hr_per_mi[i] * scenario.vmt[i]
        for i in range(scenario.vehicle_life_yr)
    ]  # increases with age
    self.tire_replacement_downtime_hr = [
        (scenario.vmt[i])
        / scenario.mr_avg_tire_life_mi
        * scenario.mr_tire_replace_downtime_hr_per_event
        for i in range(scenario.vehicle_life_yr)
    ]
    self.net_net_mr_downtime_hr_per_yr_per_yr = np.array(
        [
            self.planned_downtime_hr[i]
            + self.unplanned_downtime_hr[i]
            + self.tire_replacement_downtime_hr[i]
            for i in range(scenario.vehicle_life_yr)
        ]
    )
    self.mr_downtime_oppy_cost_dol_per_yr = (
        self.net_net_mr_downtime_hr_per_yr_per_yr
        * scenario.downtime_oppy_cost_dol_per_hr
    )

set_fueling_dwell_time_cost(a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario) -> None

This function calculates the fueling dwell time cost for a vehicle based on fuel fill rate/charging power and shifts_per_year

Parameters:

Name Type Description Default
a_vehicle fastsim.vehicle

FASTSim vehicle object of analysis vehicle

 required
scenario run_scenario.Scenario

Scenario object for current selection

 required
Source code in t3co/tco/opportunity_cost.py
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def set_fueling_dwell_time_cost(
    self, a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario
) -> None:
    """
    This function calculates the fueling dwell time cost for a vehicle based on fuel fill rate/charging power and shifts_per_year

    Args:
        a_vehicle (fastsim.vehicle): FASTSim vehicle object of analysis vehicle
        scenario (run_scenario.Scenario): Scenario object for current selection
    """
    self.total_fueling_dwell_time_hr = 0
    self.net_fueling_dwell_time_hr_per_yr = []
    self.fueling_downtime_oppy_cost_dol_per_yr = []
    self.fueling_dwell_labor_cost_dol_per_yr = []
    dwellparams = np.array(
        [
            scenario.fdt_dwpt_fraction_power_pct,
            scenario.fdt_frac_full_charge_bounds,
            scenario.fdt_avg_overhead_hr_per_dwell_hr,
            scenario.downtime_oppy_cost_dol_per_hr,
        ]
    )
    assert any(
        dwellparams
    ), f"Missing parameters in {str(dwellparams)}: {np.isnan(dwellparams)}"

    if a_vehicle.veh_pt_type in ["BEV"]:
        self.full_dwell_hr = (1 - scenario.fdt_dwpt_fraction_power_pct) * (
            a_vehicle.ess_max_kwh / scenario.ess_max_charging_power_kw
        )
    elif a_vehicle.veh_pt_type in ["Conv"]:
        if scenario.fuel_type in ["gasoline"]:
            self.full_dwell_hr = (
                a_vehicle.fs_kwh
                / (gl.kwh_per_gge)
                / scenario.fs_fueling_rate_gasoline_gpm
            ) / 60
        else:
            self.full_dwell_hr = (
                a_vehicle.fs_kwh
                / (gl.kwh_per_gge / gl.DieselGalPerGasGal)
                / scenario.fs_fueling_rate_diesel_gpm
            ) / 60
    else:
        self.full_dwell_hr = (
            (1 - scenario.fdt_dwpt_fraction_power_pct)
            * (
                a_vehicle.fs_kwh
                / (gl.kwh_per_gge / gl.kgH2_per_gge)
                / scenario.fs_fueling_rate_kg_per_min
            )
            / 60
        )

    for i in range(scenario.vehicle_life_yr):
        self.d_trip_mi = scenario.vmt[i] / self.shifts_per_year[i]
        self.num_of_dwells = max(
            0,
            (
                (self.d_trip_mi)
                * (1 - scenario.fdt_dwpt_fraction_power_pct)
                / self.total_range_mi
                - scenario.fdt_num_free_dwell_trips
            ),
        )

        if self.num_of_dwells != 0:
            self.remaining_dwells = self.num_of_dwells % 1
            if self.remaining_dwells < self.frac_of_fullcharge_bounds[0]:
                self.num_of_dwells += (
                    self.frac_of_fullcharge_bounds[0] - self.remaining_dwells
                )
            elif (
                self.frac_of_fullcharge_bounds[0]
                < self.remaining_dwells
                < self.frac_of_fullcharge_bounds[1]
            ):
                self.num_of_dwells += 0
            else:
                self.num_of_dwells += 1 - self.remaining_dwells

        if (self.num_of_dwells < 1 and not scenario.fdt_num_free_dwell_trips) or (
            scenario.fuel_type
        ):
            self.net_fueling_dwell_time_hr_per_yr.append(
                scenario.vmt[i]
                * (1 - scenario.fdt_dwpt_fraction_power_pct)
                / self.total_range_mi
                * (self.full_dwell_hr + scenario.fdt_avg_overhead_hr_per_dwell_hr)
            )
        else:
            self.dwell_time_hr = (
                self.num_of_dwells * self.full_dwell_hr
                + ceil(self.num_of_dwells)
                * scenario.fdt_avg_overhead_hr_per_dwell_hr
            )
            self.net_fueling_dwell_time_hr_per_yr.append(
                self.shifts_per_year[i]
                * max(
                    0,
                    (
                        self.dwell_time_hr
                        - max(0, scenario.fdt_available_freetime_hr)
                    ),
                )
            )

        self.fueling_dwell_labor_cost_dol_per_yr.append(
            self.net_fueling_dwell_time_hr_per_yr[i]
            * scenario.labor_rate_dol_per_hr
        )
        self.fueling_downtime_oppy_cost_dol_per_yr.append(
            self.net_fueling_dwell_time_hr_per_yr[i]
            * scenario.downtime_oppy_cost_dol_per_hr
        )

        self.total_fueling_dwell_time_hr += self.net_fueling_dwell_time_hr_per_yr[i]

set_kdes(scenario: run_scenario.Scenario, bw_method: float = 0.15, verbose: bool = False) -> None

This method sets tje kde kernel. This is time-consuming, only call this once, if possible.

Parameters:

Name Type Description Default
scenario run_scenario.Scenario

Scenario object

 required
bw_method float

kernel bandwidth method used by guassian_kde. Defaults to .15.

 0.15
verbose bool

if True, prints process sets. Defaults to False.

 False
Source code in t3co/tco/opportunity_cost.py
Python
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def set_kdes(
    self,
    scenario: run_scenario.Scenario,
    bw_method: float = 0.15,
    verbose: bool = False,
) -> None:
    """
    This method sets tje kde kernel. This is time-consuming, only call this once, if possible.

    Args:
        scenario (run_scenario.Scenario): Scenario object
        bw_method (float, optional):  kernel bandwidth method used by guassian_kde. Defaults to .15.
        verbose (bool, optional): if True, prints process sets. Defaults to False.
    """

    if verbose:
        print("Initializing kernels.")

    self.df_veh_wt = self.df_veh_wt[~self.df_veh_wt["WEIGHTAVG"].isnull()]
    self.df_veh_wt = self.df_veh_wt[~self.df_veh_wt["WEIGHTEMPTY"].isnull()]
    self.df_veh_wt = self.df_veh_wt[self.df_veh_wt["WEIGHTAVG"] < 120000]

    weights = self.df_veh_wt["TAB_MILES"] / np.nansum(self.df_veh_wt["TAB_MILES"])
    kernel = gaussian_kde(
        self.df_veh_wt["WEIGHTAVG"], weights=weights, bw_method=bw_method
    )
    self.vehicle_weights_bins_lb = np.linspace(
        self.df_veh_wt["WEIGHTAVG"].min(), self.df_veh_wt["WEIGHTAVG"].max(), 1000
    )
    self.vehicle_weights_bins_kg = gl.lbs_to_kgs(self.vehicle_weights_bins_lb)

    # get probability of each vehicle weight
    self.p_of_weights = kernel(self.vehicle_weights_bins_lb)

    probability_payload = pd.DataFrame(
        [self.vehicle_weights_bins_kg, self.p_of_weights],
        index=["vehicle_weights_bins_kg", "p_of_weights"],
    ).T
    probability_payload.to_csv(
        Path(self.wt_dist_file).parents[0] / "payload_pdf.csv"
    )
    normalization_factor = probability_payload[
        probability_payload["vehicle_weights_bins_kg"].between(
            scenario.plf_ref_veh_empty_mass_kg, scenario.gvwr_kg
        )
    ]["p_of_weights"].sum()
    self.p_of_weights_normalized = self.p_of_weights / normalization_factor

set_payload_loss_factor(a_vehicle: fastsim.vehicle.Vehicle, scenario: run_scenario.Scenario, plots: bool = False, plots_dir: str = None) -> None

This method runs teh kernel density estimation function set_kdes and calculates the payload capacity loss factor (payload_cap_cost_multiplier) of the new vehicle compared to a conventional vehicle's reference empty weight.

Parameters:

Name Type Description Default
a_vehicle fastsim.vehicle

FASTSim vehicle object of the analysis vehicle

 required
scenario run_scenario.Scenario

Scenario object of current selection

 required
plots bool

if True, creates histogram of KDE weight bins. Defaults to False.

 False
plots_dir str

output diretory for saving plot figure. Defaults to None.

 None
Source code in t3co/tco/opportunity_cost.py
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def set_payload_loss_factor(
    self,
    a_vehicle: fastsim.vehicle.Vehicle,
    scenario: run_scenario.Scenario,
    plots: bool = False,
    plots_dir: str = None,
) -> None:
    """
    This method runs teh kernel density estimation function set_kdes and calculates the payload capacity loss factor (payload_cap_cost_multiplier) \
        of the new vehicle compared to a conventional vehicle's reference empty weight.

    Args:
        a_vehicle (fastsim.vehicle): FASTSim vehicle object of the analysis vehicle
        scenario (run_scenario.Scenario): Scenario object of current selection
        plots (bool, optional): if True, creates histogram of KDE weight bins. Defaults to False.
        plots_dir (str, optional): output diretory for saving plot figure. Defaults to None.
    """
    self.set_kdes(scenario, verbose=False)

    new_empty_weight_kg = a_vehicle.veh_kg - a_vehicle.cargo_kg
    empty_increase_kg = new_empty_weight_kg - scenario.plf_ref_veh_empty_mass_kg
    new_cargo_cieling_kg = (
        scenario.gvwr_kg - empty_increase_kg + scenario.gvwr_credit_kg
    )

    # determine indices where lost cargo capacity is bounded in vehicle_weights
    # and get the corresponding indices for p_of_weights
    # based on current vehicle's new_cargo_cieling_lb and base_vehicle_gvwr_lb

    if empty_increase_kg >= scenario.gvwr_credit_kg:
        a = self.vehicle_weights_bins_kg - new_cargo_cieling_kg
        # print(a)
        minidx = (
            np.where(
                self.vehicle_weights_bins_kg == a[a > 0][0] + new_cargo_cieling_kg
            )
        )[0][0]
        a = self.vehicle_weights_bins_kg - scenario.gvwr_kg
        maxidx = (
            np.where(self.vehicle_weights_bins_kg == a[a > 0][0] + scenario.gvwr_kg)
        )[0][0]

        estimated_lost_payload_per_bin_kg = self.p_of_weights_normalized[
            minidx:maxidx
        ] * (self.vehicle_weights_bins_kg[minidx:maxidx] - new_cargo_cieling_kg)
        estimated_lost_payload_kg = np.trapz(estimated_lost_payload_per_bin_kg)

        # payload cost multiplier
        self.payload_cap_cost_multiplier = 1 + estimated_lost_payload_kg / (
            scenario.gvwr_kg - new_empty_weight_kg + scenario.gvwr_credit_kg
        )

        scenario.estimated_lost_payload_kg = estimated_lost_payload_kg
    else:
        self.payload_cap_cost_multiplier = 1
    # recording final report data on vehicle empty weights and cargo capcities
    scenario.plf_scenario_vehicle_empty_kg = new_empty_weight_kg
    scenario.plf_reference_vehicle_cargo_capacity_kg = (
        scenario.gvwr_kg - scenario.plf_ref_veh_empty_mass_kg
    )
    scenario.plf_scenario_vehicle_cargo_capacity_kg = (
        scenario.gvwr_kg + scenario.gvwr_credit_kg - new_empty_weight_kg
    )

    def make_plots(save_dir: str = None) -> None:
        """
        This function generates a histogram of the payload KDE weight distribution

        Args:
            save_dir (str, optional): Output directory path to save plot figure. Defaults to None.
        """
        if save_dir and not Path(save_dir).exists():
            save_dir.mkdir()

        fig, ax = plt.subplots(figsize=(9, 6))
        ax.hist(
            gl.lbs_to_kgs(self.df_veh_wt["WEIGHTAVG"]),
            bins=50,
            label="WEIGHTAVG: operating weight [kg]",
        )
        ax2 = ax.twinx()
        ax2.plot(
            self.vehicle_weights_bins_kg,
            self.p_of_weights,
            color="red",
            linewidth=3,
            label="KDE",
        )
        ax2.fill_between(
            x=self.vehicle_weights_bins_kg[minidx:maxidx],
            y1=self.p_of_weights[minidx:maxidx],
            y2=[0] * len(self.p_of_weights[minidx:maxidx]),
            color="red",
            alpha=0.3,
            label=f"estimated cargo lost [kg]: {round(estimated_lost_payload_kg)}",
        )
        ax2.axvline(
            scenario.gvwr_kg,
            label=f"GVWR [kg] {round(scenario.gvwr_kg)}",
            color="orange",
        )
        ax2.axvline(
            new_cargo_cieling_kg,
            label=f"GVWR + credit - empty weight increase [kg] : {round(scenario.gvwr_kg)} + {round(scenario.gvwr_credit_kg)}  - {round(empty_increase_kg)} = {round(new_cargo_cieling_kg)}",
            color="purple",
        )
        fig.suptitle(
            f"payload cost multiplier: {round(self.payload_cap_cost_multiplier,2)}"
        )
        fig.legend()
        plt.show()

    if plots:
        make_plots(plots_dir)

main()

Runs the opportunity cost module as a standalone code based on input vehicles and scenarios

Source code in t3co/tco/opportunity_cost.py
Python
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def main():
    """
    Runs the opportunity cost module as a standalone code based on input vehicles and scenarios
    """
    print("opportunity cost main()")
    vehicles_file = Path(
        "./resources/inputs/tda_example/TDA_FY22_vehicle_model_assumptions.csv"
    )
    scenarios_file = Path(
        "./resources/inputs/tda_example/TDA_FY22_scenario_assumptions.csv"
    )
    s, c = run_scenario.get_scenario_and_cycle(33, scenarios_file)
    v = run_scenario.get_vehicle(33, vehicles_file)
    oc = OpportunityCost(v, s)

    print("GVWRlb", gl.kg_to_lbs(s.gvwr_kg))
    print("GvwrCreditlb", gl.kg_to_lbs(s.gvwr_credit_kg))
    print("veh_lb", gl.kg_to_lbs(v.veh_kg))
    print("ess_mass_lb", gl.kg_to_lbs(v.ess_mass_kg))
    print("original empty lb", gl.kg_to_lbs(v.veh_kg - v.cargo_kg))
    run_scenario.set_max_battery_kwh(v, v.ess_max_kwh * 2)
    print("next ess_mass_lb", gl.kg_to_lbs(v.ess_mass_kg))
    print("next veh_lb", gl.kg_to_lbs(v.veh_kg))

    print("GVWRlb", oc.GVWRlb)
    print("gvwr_credit_lb", oc.gvwr_credit_lb)
    print("oc.base_vehicle_veh_lb", gl.kg_to_lbs(oc.base_vehicle_veh_kg))
    print("oc.base_vehicle_cargo_lb", gl.kg_to_lbs(oc.base_vehicle_cargo_kg))
    print("oc.original_empty_lb", gl.kg_to_lbs(oc.reference_vehicle_empty_kg))

    plf = oc.set_payload_loss_factor(v, plots=True)
    print(plf)
    oc.set_fueling_dwell_time_cost(v, s)
    print(oc.dwell_time_cost_Dol)
    print(oc.net_fueling_dwell_time_hr_per_yr)
    print(oc.__dict__["payload_cap_cost_multiplier"])
    oc.set_M_R_downtime_cost(v, s)