def lhs_dimension(): # Dynamic dimension of LHS - depending on no. of Vars saved as "dimension"
cc_bev = gin.changed_compact().reindex(['FE_batt', 'E_batt', 'E_battPHEV', 'P_battEmpty', 'P_fcEmpty',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'],
axis='rows') # for LHS-Dimension / s_ren!!!
cg_bev = gin.changed_general().reindex(['C3_batt', 'C5_empty', 'Em_elFC', 'cd_empty', 'cd', 'Em_elBatt', 'Em_elVC',
'L', 'D', 'r', 'C_fuelEl', 'C_batt', 'C_fcEmpty'], axis='rows') # 22 (18)
range_length = pd.concat([cc_bev, cg_bev]) # concatinate all needed lhs-variables
dim = (len(range_length)) # Length of Variable list
#print("dim: ", dim)
return dim
def result_calc(var, class_sel, dimension):
all_para_keys = [
'FE', 'E_batt', 'E_battEmptyPHEV', 'P_batt', 'P_fc', 'c_main_bev',
'c_main_fcev', 'c_main_phev', 'c_main_icev', 'C3', 'C5', 'Em_elFC',
'Em_elVC', 'cd', 'cd_phev', 'Em_elBatt', 'L', 'D', 'r', 'C_fuel',
'C_batt', 'C_fc', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8', 'X9',
'X10', 'X11', 'X12', 'X13', 'X14', 'm_curb', 'C_msrp', 'P_battSet',
'E_battSet', 'P_fcSet', 'C_battSet', 'C_fcSet', 'CF', 'w_h2',
'w_synth', 's_ren'
]
result = np.zeros(shape=(0, 2))
result_all = np.zeros(shape=(0, 6))
result_tax = np.zeros(shape=(0, 2)) # Partial TCO: CAPEX, OPEX
result_tax_total = [] # Total TCO!
# Array of all Values
all_values = []
vehicle_type = 0
# Counter for No. of Calculations
vehicle_name_list = []
# Array for PHEV FE's
fe_phev_cd_array = []
fe_phev_cs_array = []
c_phev_el_array = []
c_phev_synth_array = []
if class_sel == 1: # Compact
E_battPHEV = gin.changed_compact().reindex(['E_battPHEV'], axis='rows')
count_type = 0
elif class_sel == 2: # SUV
E_battPHEV = gin.changed_suv().reindex(['E_battPHEV'], axis='rows')
count_type = 1
elif class_sel == 3: # LDV
E_battPHEV = gin.changed_ldv().reindex(['E_battPHEV'], axis='rows')
count_type = 2
while count_type < len(gin.x_vals(
)): # count through fix vals of class (bev, fcev, phev, icev)
lhs_lists = var[vehicle_type] # all result lists of one propType
# Array for PHEV FE's
fe_phev_cd_list = []
fe_phev_cs_list = []
c_phev_el_list = []
c_phev_synth_list = []
x_vals = list(gin.x_vals().iloc[count_type])
spec_vals = list(gin.spec_vals().iloc[count_type])
x_vals.extend(spec_vals) # all fix vals saved here
single_res = np.zeros(shape=(n, 2))
single_all_res = np.zeros(shape=(n, 6))
single_res_tax = np.zeros(shape=(n, 2))
drive_res_tax_total = []
for list_num in range(n):
tco_res, lce_res, tco_capex, tco_opex, e_fc, e_vc, lhs_dict, fe_phev_cd, fe_phev_cs = 0, 0, 0, 0, 0, \
0, 0, 0, 0
if vehicle_type == 2: # PHEV
all_para_phev = [
'FE_bev', 'E_batt_bev', 'E_battPHEV', 'P_batt_bev',
'P_fc_bev', 'c_main_bev', 'c_main_fcev', 'c_main_phev',
'c_main_icev', 'C3_bev', 'C5_bev', 'Em_elFC', 'Em_elVC',
'cd_empty', 'cd_phev', 'Em_elBatt', 'L', 'D', 'r',
'C_fuel_bev', 'C_batt_bev', 'C_fc_bev', 'FE_icev',
'E_batt', 'E_battEmptyPHEV', 'P_batt', 'P_fc',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev',
'C3_icev', 'C5_icev', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuel_icev',
'C_batt', 'C_fc', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7',
'X8', 'X9', 'X10', 'X11', 'X12', 'X13', 'X14', 'm_curb',
'C_msrp', 'P_battSet', 'E_battSet', 'P_fcSet', 'C_battSet',
'C_fcSet', 'CF', 'w_h2', 'w_synth', 's_ren'
]
# 58 vals - 60 with 2 x cd_phev - 68 with 2 x 4 c_maint
all_phev_lhs = list(lhs_lists[list_num])
if booleanCheckbox == 1 and (
vehicle_type == 0 or vehicle_type
== 1): # Checking if there is a subsidy
s_ren = gin.sub_big()
elif booleanCheckbox == 1 and (vehicle_type == 2):
s_ren = gin.sub_small()
else:
s_ren = 0.0
all_phev_lhs.extend(x_vals)
all_phev_lhs.append(s_ren)
lhs_dict = dict(zip(all_para_phev, all_phev_lhs))
lhs_dict['E_batt_bev'] = 0 # ********** NEW
vehicle_name = 'PHEV'
e_inst = FuelCyclePHEV(**lhs_dict)
e_fc = e_inst.fuel_cycle_phev() # e_fc of PHEV
## Here distinguish tco icev / bev - FE
lhs_dict['C_main'] = lhs_dict['c_main_phev']
tco_inst = TCO(**lhs_dict)
tco_res, tco_opex, tco_capex = tco_inst.calc_tco_phev(
) ## tco result PHEV
# IF GHG TAX TURNED ON: ADDED TO TCO HERE
if ghg_tax != 0:
tco_opex_tax = tco_opex + (e_fc * ghg_tax / 1000000
) # normalize t to gram
tco_capex_tax = tco_capex + (e_vc * ghg_tax / 1000000
) # normalize t to gram
L = lhs_lists[list_num][16]
D = lhs_lists[list_num][17]
r = lhs_lists[list_num][18]
cd_phev = lhs_lists[list_num][13]
cs = 100 - cd_phev
# --- OPEX --- #
sum_tco_tax = 0
for years in range(1, int(round(
L +
1))): # creating sum # FOR ABSOLUTE C_main NUMBERS
equation = (tco_opex_tax) / (1 + r)**(years - 1)
sum_tco_tax += equation
tco_res_tax = (tco_capex_tax / (L * D)) + sum_tco_tax
else:
tco_opex_tax = 0
tco_capex_tax = 0
tco_res_tax = 0
fe_phev_cd = lhs_dict['FE_bev']
fe_phev_cs = lhs_dict['FE_icev']
c_phev_el = lhs_dict['C_fuel_bev']
c_phev_synth = lhs_dict['C_fuel_icev']
### UPDATE PHEV VALS TO FIT LCE CALCULATION
phev_vals = list(e_inst.new_phev_vals()) # updated PHEV vals
phev_vals.extend(x_vals)
phev_vals.append(s_ren)
lhs_dict = dict(zip(all_para_keys, phev_vals))
lhs_dict['C_main'] = lhs_dict['c_main_phev']
lce_inst = LCE(**lhs_dict)
e_vc = lce_inst.vehicle_cycle() # e_vc of PHEV
lce_res = lce_inst.calc_lce(e_fc, e_vc) ## LCE
elif vehicle_type == 0 or vehicle_type == 1 or vehicle_type == 3:
all_vals = list(
lhs_lists[list_num]
) # should be one single list of lhs_variable_results
all_vals.extend(x_vals) # ALL NEEDED VARS ARE HERE NOW
if booleanCheckbox == 1 and (
vehicle_type == 0
or vehicle_type == 1): # checks if there is a subsidy
s_ren = gin.sub_big()
elif booleanCheckbox == 1 and (vehicle_type == 2):
s_ren = gin.sub_small()
else:
s_ren = 0.0
all_vals.append(s_ren)
lhs_dict = dict(zip(all_para_keys, all_vals))
lhs_dict['E_battEmptyPHEV'] = 0
lce_inst = LCE(**lhs_dict)
e_fc = lce_inst.fuel_cycle() # e_fc of rest vehicles
e_vc = lce_inst.vehicle_cycle() # e_vc of rest vehicles
# --- c_maintenance allocation --- #
if vehicle_type == 0:
lhs_dict['C_main'] = lhs_dict['c_main_bev']
vehicle_name = 'BEV'
elif vehicle_type == 1:
lhs_dict['C_main'] = lhs_dict['c_main_fcev']
vehicle_name = 'FCEV'
elif vehicle_type == 3:
lhs_dict['C_main'] = lhs_dict['c_main_icev']
vehicle_name = 'ICEV'
tco_inst = TCO(**lhs_dict)
lce_res = lce_inst.calc_lce(e_fc, e_vc) ##### LCE
tco_res, tco_opex, tco_capex = tco_inst.calc_tco(
) # tco result rest
# IF GHG TAX TURNED ON: ADDED TO TCO HERE
if ghg_tax != 0:
tco_opex_tax = tco_opex + (e_fc * ghg_tax / 1000000
) # normalize t to gram
tco_capex_tax = tco_capex + (e_vc * ghg_tax / 1000000
) # normalize t to gram
L = lhs_lists[list_num][16]
D = lhs_lists[list_num][17]
r = lhs_lists[list_num][18]
sum_tco_tax = 0
for years in range(1, int(round(L + 1))): # creating sum
equation = (tco_opex_tax) / ((1 + r)**(years - 1))
equation = np.around(equation, decimals=4)
sum_tco_tax += equation
tco_res_tax = (tco_capex_tax / (L * D)) + sum_tco_tax
else:
tco_opex_tax = 0
tco_capex_tax = 0
tco_res_tax = 0
# Filling Zeros to PHEV specific FE Columns
fe_phev_cd = 0
fe_phev_cs = 0
c_phev_el = 0
c_phev_synth = 0
all_values.append(lhs_dict)
# all results of BEV, FCEV etc
single_res[list_num] = [
np.around(tco_res, decimals=4),
np.around(lce_res, decimals=4)
]
single_all_res[list_num] = [
tco_res, lce_res, tco_capex, tco_opex, e_fc, e_vc
]
single_all_res = np.around(single_all_res, decimals=4)
# TAX
drive_res_tax_total.append(
tco_res_tax) # Here total TCO for Tax Scenario per Drive
single_res_tax[list_num] = [
np.around(tco_capex_tax, decimals=4),
np.around(tco_opex_tax, decimals=4)
] # Partial TCO Results
fe_phev_cd_list.append(fe_phev_cd)
fe_phev_cs_list.append(fe_phev_cs)
c_phev_el_list.append(c_phev_el)
c_phev_synth_list.append(c_phev_synth)
vehicle_name_list.append(vehicle_name)
result = np.append(result, single_res, axis=0)
result_all = np.append(result_all, single_all_res,
axis=0) # --- TOTAL RESULT ---
result_tax = np.append(result_tax, single_res_tax,
axis=0) # PARTIAL TCOs
result_tax_total.extend(drive_res_tax_total) # WHOLE TCO!
# Additional Values for csv
fe_phev_cd_array.extend(fe_phev_cd_list)
fe_phev_cs_array.extend(fe_phev_cs_list)
c_phev_el_array.extend(c_phev_el_list)
c_phev_synth_array.extend(c_phev_synth_list)
# append to a longer list
count_type += 3 # Jump from compact_bev to compact_fcev to compact_phev ...
vehicle_type += 1 # increasing -> lhs_lists bev -> fcev
all_values = pd.DataFrame(all_values) # create dataframe
all_values = round(all_values, 4)
# Copy all_values to customly save as csv // Adding No. of Calculation, fe_phev_cd, fe_phev_cs,
all_values_csv = all_values
all_values_csv['Vehicle'] = vehicle_name_list
all_values_csv['fe_phev_cd'] = fe_phev_cd_array
all_values_csv['fe_phev_cs'] = fe_phev_cs_array
all_values_csv['c_phev_el'] = c_phev_el_array
all_values_csv['c_phev_synth'] = c_phev_synth_array
all_values_csv.to_csv("results/input_values.csv", sep=";")
all_values_csv.to_json("results/json/input_values.json", orient='index')
result = np.around(result, decimals=4)
columns = [
'TCO (€/km)', "LCE (gGHG/km)", "TCO_Capex (€)", "TCO_Opex (€/km)",
"Em_fc (gGHG/km)", "Em_vc (gGHG)"
]
result_extend = np.around(result_all, decimals=4)
result_extend = pd.DataFrame(data=result_extend, columns=columns)
if ghg_tax != 0:
result[:, 0] = result_tax_total
result = np.around(result, decimals=4)
return result, result_extend, all_values, vehicle_name, result_tax
def get_variables(class_sel, vehicle):
global lhs_vals
if class_sel == 1: # compact car # cc = changed compact
if vehicle == 0: # BEV
cc_bev = gin.changed_compact().reindex([
'FE_batt', 'E_batt', 'E_battPHEV', 'P_battEmpty', 'P_fcEmpty',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_bev = gin.changed_general().reindex([
'C3_batt', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty', 'cd',
'Em_elBatt', 'L', 'D', 'r', 'C_fuelEl', 'C_batt', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cc_bev, cg_bev])
elif vehicle == 1: # FCEV
cc_fcev = gin.changed_compact().reindex([
'FE_h2', 'E_battEmpty', 'E_battEmptyPHEV', 'P_batt', 'P_fc',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_fcev = gin.changed_general().reindex([
'C3_h2', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelH2', 'C_batt',
'C_fc'
],
axis='rows')
lhs_vals = pd.concat([cc_fcev, cg_fcev])
elif vehicle == 2: # PHEV
pass
elif vehicle == 3: # ICEV
cc_icev = gin.changed_compact().reindex([
'FE_synth', 'E_battEmpty', 'E_battEmptyPHEV', 'P_battEmpty',
'P_fcEmpty', 'c_main_bev', 'c_main_fcev', 'c_main_phev',
'c_main_icev'
],
axis='rows')
cg_icev = gin.changed_general().reindex([
'C3_synth', 'C5_icev', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelSynth',
'C_battEmpty', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cc_icev, cg_icev])
elif class_sel == 2: # midsize SUV # cs = changed suv
if vehicle == 0: # BEV
cs_bev = gin.changed_suv().reindex([
'FE_batt', 'E_batt', 'E_battPHEV', 'P_battEmpty', 'P_fcEmpty',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_bev = gin.changed_general().reindex([
'C3_batt', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty', 'cd',
'Em_elBatt', 'L', 'D', 'r', 'C_fuelEl', 'C_batt', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cs_bev, cg_bev])
elif vehicle == 1: # FCEV
cs_fcev = gin.changed_suv().reindex([
'FE_h2', 'E_battEmpty', 'E_battEmptyPHEV', 'P_batt', 'P_fc',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_fcev = gin.changed_general().reindex([
'C3_h2', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelH2', 'C_batt',
'C_fc'
],
axis='rows')
lhs_vals = pd.concat([cs_fcev, cg_fcev])
elif vehicle == 3: # ICEV
cs_icev = gin.changed_suv().reindex([
'FE_synth', 'E_battEmpty', 'E_battEmptyPHEV', 'P_battEmpty',
'P_fcEmpty', 'c_main_bev', 'c_main_fcev', 'c_main_phev',
'c_main_icev'
],
axis='rows')
cg_icev = gin.changed_general().reindex([
'C3_synth', 'C5_icev', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelSynth',
'C_battEmpty', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cs_icev, cg_icev])
elif class_sel == 3: # Light Duty Vehicle # cl = changed ldv
if vehicle == 0: # BEV
cl_bev = gin.changed_ldv().reindex([
'FE_batt', 'E_batt', 'E_battPHEV', 'P_battEmpty', 'P_fcEmpty',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_bev = gin.changed_general().reindex([
'C3_batt', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty', 'cd',
'Em_elBatt', 'L', 'D', 'r', 'C_fuelEl', 'C_batt', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cl_bev, cg_bev])
elif vehicle == 1: # FCEV
cl_fcev = gin.changed_ldv().reindex([
'FE_h2', 'E_battEmpty', 'E_battEmptyPHEV', 'P_batt', 'P_fc',
'c_main_bev', 'c_main_fcev', 'c_main_phev', 'c_main_icev'
],
axis='rows')
cg_fcev = gin.changed_general().reindex([
'C3_h2', 'C5_empty', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelH2', 'C_batt',
'C_fc'
],
axis='rows')
lhs_vals = pd.concat([cl_fcev, cg_fcev])
elif vehicle == 3: # ICEV
cl_icev = gin.changed_ldv().reindex([
'FE_synth', 'E_battEmpty', 'E_battEmptyPHEV', 'P_battEmpty',
'P_fcEmpty', 'c_main_bev', 'c_main_fcev', 'c_main_phev',
'c_main_icev'
],
axis='rows')
cg_icev = gin.changed_general().reindex([
'C3_synth', 'C5_icev', 'Em_elFC', 'Em_elVC', 'cd_empty',
'cd_empty', 'Em_elBatt', 'L', 'D', 'r', 'C_fuelSynth',
'C_battEmpty', 'C_fcEmpty'
],
axis='rows')
lhs_vals = pd.concat([cl_icev, cg_icev])
else:
print('Wrong Input! \n')
veh_class_sel() # repeat selection of vehicle class
return lhs_vals