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model-vtcpfm.py
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# %%
# Running Virginia Tech's Comprehensive Power-based Fuel Model
# to Compare its output with our best Cascaded ANN results
# Ehsan Moradi, Ph.D. Candidate
# %%
# Load required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# %%
# Load sample data from Excel to a pandas dataframe
def load_from_Excel(vehicle, order, sheet, settings):
directory = (
"../../Google Drive/Academia/PhD Thesis/Field Experiments/Veepeak/"
+ vehicle
+ "/Processed/"
+ settings["INPUT_" + order + "_TYPE"]
+ "/"
)
input_file = vehicle + " - {0} - {1}.xlsx".format(
settings["INPUT_" + order + "_TYPE"], settings["INPUT_" + order + "_INDEX"]
)
input_path = directory + input_file
df = pd.read_excel(input_path, sheet_name=sheet, header=0)
return df
# %%
# Save the predicted field back to Excel file
def save_to_excel(df, vehicle, order, settings):
directory = (
"../../Google Drive/Academia/PhD Thesis/Field Experiments/Veepeak/"
+ vehicle
+ "/Processed/"
+ settings["OUTPUT_" + order + "_TYPE"]
+ "/"
)
output_file = vehicle + " - {0} - {1}.xlsx".format(
settings["OUTPUT_" + order + "_TYPE"], settings["OUTPUT_" + order + "_INDEX"]
)
output_path = directory + output_file
with pd.ExcelWriter(output_path, engine="openpyxl", mode="w") as writer:
df.to_excel(writer, header=True, index=None)
print("{0} -> Data is saved to Excel successfully!".format(vehicle))
return None
# %%
# Caclulate the resistance force (sum of aerodynamic, rolling, and grade resistance forces)
def calculate_resistance_force(df, specs):
df["RESISTANCE_FORCE"] = (
(specs["RHO"] / 25.92)
* specs["C_D"]
* (1 - 0.085 * df["ALT_M"] / 1000)
* specs["A_F"]
* (df["SPD_KH"] ** 2)
+ 9.8066
* specs["WEIGHT"]
* (specs["C_R"] / 1000)
* (specs["C_1"] * df["SPD_KH"] + specs["C_2"])
+ 9.8066 * specs["WEIGHT"] * np.sin(np.radians(df["NO_OUTLIER_GRADE_DEG"]))
)
return df
# %%
# Calculate power
def calculate_power(df, specs):
df["POWER"] = (
(df["RESISTANCE_FORCE"] + 1.04 * specs["WEIGHT"] * df["ACC_MS2"])
/ (3600 * 0.9)
) * df["SPD_KH"]
df["POWER2"] = df["POWER"] ** 2
return df
# %%
# Caculate other required variables
def calculate_variables(df, specs):
variables = {}
T = df["ROAD_TYPE"].groupby(df["ROAD_TYPE"]).agg(["count"]).reset_index()
P = df["POWER"].groupby(df["ROAD_TYPE"]).agg(["sum"]).reset_index()
P2 = df["POWER2"].groupby(df["ROAD_TYPE"]).agg(["sum"]).reset_index()
df["FCR_LS"] = df["FCR_LH"] / 3600
F = df["FCR_LS"].groupby(df["ROAD_TYPE"]).agg(["sum"]).reset_index()
W = df["RPM"].groupby(df["ROAD_TYPE"]).agg(["sum"]).reset_index()
T_CITY = T.loc[T["ROAD_TYPE"] == "City", "count"].iloc[0]
T_HWY = T.loc[T["ROAD_TYPE"] == "Highway", "count"].iloc[0]
P_CITY = P.loc[P["ROAD_TYPE"] == "City", "sum"].iloc[0]
P_HWY = P.loc[P["ROAD_TYPE"] == "Highway", "sum"].iloc[0]
P2_CITY = P2.loc[P2["ROAD_TYPE"] == "City", "sum"].iloc[0]
P2_HWY = P2.loc[P2["ROAD_TYPE"] == "Highway", "sum"].iloc[0]
F_CITY = F.loc[F["ROAD_TYPE"] == "City", "sum"].iloc[0]
F_HWY = F.loc[F["ROAD_TYPE"] == "Highway", "sum"].iloc[0]
W_CITY = W.loc[F["ROAD_TYPE"] == "City", "sum"].iloc[0]
W_HWY = W.loc[F["ROAD_TYPE"] == "Highway", "sum"].iloc[0]
ALPHA0 = max(
[
(specs["P_MFO"] * specs["W_IDLE"] * specs["D"])
/ (22164 * specs["Q_FINAL"] * specs["N"]),
(
(F_CITY - F_HWY * (P_CITY / P_HWY))
- (10 ** -6) * (P2_CITY - P2_HWY * (P_CITY / P_HWY))
)
/ (T_CITY - T_HWY * (P_CITY / P_HWY)),
]
)
ALPHA2 = max(
(
(F_CITY - F_HWY * (P_CITY / P_HWY))
- (T_CITY - T_HWY * (P_CITY / P_HWY)) * ALPHA0
)
/ (P2_CITY - P2_HWY * (P_CITY / P_HWY)),
10 ** -6,
)
ALPHA1 = (F_HWY - T_HWY * ALPHA0 - P2_HWY * ALPHA2) / P_HWY
BETA0 = max(
[
(specs["P_MFO"] * specs["D"]) / (22164 * specs["Q_FINAL"] * specs["N"]),
(
(F_CITY - F_HWY * (P_CITY / P_HWY))
- (10 ** -6) * (P2_CITY - P2_HWY * (P_CITY / P_HWY))
)
/ (W_CITY - W_HWY * (P_CITY / P_HWY)),
]
)
BETA2 = max(
(
(F_CITY - F_HWY * (P_CITY / P_HWY))
- (W_CITY - W_HWY * (P_CITY / P_HWY)) * BETA0
)
/ (P2_CITY - P2_HWY * (P_CITY / P_HWY)),
10 ** -6,
)
BETA1 = (
((F_CITY - W_CITY * BETA0 - P2_CITY * BETA2) / P_CITY)
+ ((F_HWY - W_HWY * BETA0 - P2_HWY * BETA2) / P_HWY)
) / 2
variables = {
"T_CITY": T_CITY,
"T_HWY": T_HWY,
"P_CITY": P_CITY,
"P_HWY": P_HWY,
"P2_CITY": P2_CITY,
"P2_HWY": P2_HWY,
"F_CITY": F_CITY,
"F_HWY": F_HWY,
"W_CITY": W_CITY,
"W_HWY": W_HWY,
"ALPHA0": ALPHA0,
"ALPHA1": ALPHA1,
"ALPHA2": ALPHA2,
"BETA0": BETA0,
"BETA1": BETA1,
"BETA2": BETA2,
}
return pd.DataFrame(variables, index=[0,])
# %%
# Apply piecewise condition of VTCPFM model (type I)
def power_condition_1(row, alpha0, alpha1, alpha2):
if row["POWER"] >= 0:
val = (
variables["ALPHA0"]
+ variables["ALPHA1"] * row["POWER"]
+ variables["ALPHA2"] * row["POWER2"]
)
else:
val = variables["ALPHA0"]
val *= 3600
return val
# %%
# Apply piecewise condition of VTCPFM model (type II)
def power_condition_2(row, beta0, beta1, beta2, specs):
if row["POWER"] >= 0:
val = (
variables["BETA0"] * row["RPM"]
+ variables["BETA1"] * row["POWER"]
+ variables["BETA2"] * row["POWER2"]
)
else:
val = variables["BETA0"] * specs["W_IDLE"]
val *= 3600
return val
# %%
# Cacluate fuel consumption rate
def calculate_fuel_consumption_rate(df, variables, specs):
df["VTCPFM_I_FCR_LH"] = df.apply(
power_condition_1,
args=(variables["ALPHA0"], variables["ALPHA1"], variables["ALPHA2"]),
axis=1,
)
df["VTCPFM_II_FCR_LH"] = df.apply(
power_condition_2,
args=(variables["BETA0"], variables["BETA1"], variables["BETA2"], specs),
axis=1,
)
return df
# %%
# General settings
pd.options.mode.chained_assignment = None
EXPERIMENTS = (
"019 Hyundai Elantra GT 2019 (2.0L Auto)",
"025 Chevrolet Captiva 2010 (2.4L Auto)",
"027 Chevrolet Cruze 2011 (1.8L Manual)",
)
# %%
# Model execution settings
SETTINGS = {
"INPUT_01_TYPE": "NONE",
"INPUT_01_INDEX": "10",
"INPUT_02_TYPE": "VTCPFM",
"INPUT_02_INDEX": "SPECS",
"INPUT_03_TYPE": "VTCPFM",
"INPUT_03_INDEX": "VARIABLES",
# "OUTPUT_01_TYPE": "VTCPFM",
# "OUTPUT_01_INDEX": "VARIABLES",
"OUTPUT_02_TYPE": "VTCPFM",
"OUTPUT_02_INDEX": "COMPARE",
}
# %%
# Batch execution on all vehicles and their trips
for vehicle in EXPERIMENTS:
# Load data from Excel to a pandas dataframe
df = load_from_Excel(vehicle, "01", "Sheet1", SETTINGS)
specs = load_from_Excel(vehicle, "02", "Sheet1", SETTINGS).iloc[0].to_dict()
variables = load_from_Excel(vehicle, "03", "Sheet1", SETTINGS).iloc[0].to_dict()
df = calculate_resistance_force(df, specs)
df = calculate_power(df, specs)
# variables = calculate_variables(df, specs)
df = calculate_fuel_consumption_rate(df, variables, specs)
# save_to_excel(variables, vehicle, "01", SETTINGS)
save_to_excel(df, vehicle, "02", SETTINGS)
# %%
# %%