-
Notifications
You must be signed in to change notification settings - Fork 0
/
semi_parameter_scans.py
569 lines (427 loc) · 30.7 KB
/
semi_parameter_scans.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
"""
Date: Feb 14, 2024
Purpose: Evaluate truck model output parameters for different Tesla semi drivecycles and compare with parameters derived independently from the PepsiCo Tesla Semi NACFE data.
"""
# Import packages
import pandas as pd
import numpy as np
import scipy as scipy
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
import truck_model_tools
from scipy.interpolate import interp1d
from scipy.optimize import root_scalar
import pickle
new_rc_params = {'text.usetex': False,
"svg.fonttype": 'none'
}
plt.rcParams.update(new_rc_params)
KG_PER_TON = 1000
KG_PER_LB = 0.453592
SECONDS_PER_HOUR = 3600
###################################### Select drivecycles to consider #####################################
drivecycles = {
'pepsi_1': [2, 9, 13, 15, 33],
'pepsi_2': [7, 10, 14, 22, 25, 31],
'pepsi_3': [8, 10, 13, 16, 21, 24, 28, 32, 33]
}
###########################################################################################################
######################################### Obtain model parameters #########################################
# Annual VMT from VIUS 2002
VMT = np.array(pd.read_csv('data/default_vmt.csv')['VMT (miles)'])
# Default drivecycle used for emission and costing analysis
# Source: Jones, R et al. (2023).Developing and Benchmarking a US Long-haul Drive Cycle forVehicle Simulations, Costing and Emissions Analysis
# https://docs.google.com/spreadsheets/d/1Q2uO-JHfwvGxir_PU8IO5zmo0vs4ooC_/edit?usp=sharing&ouid=102742490305620802920&rtpof=true&sd=true
df_drivecycle = truck_model_tools.extract_drivecycle_data('data/drivecycle.xlsx') #drive cycle as a dataframe
df_drivecycle_flat = truck_model_tools.extract_drivecycle_data('data/drivecycle_nograde.xlsx') #drive cycle with zero road grade everywhere
#print(df_drivecycle.head())
# Payload distribution from VIUS 2002
# Note: Dataset from VIUS 2002, filtered and cleaned by authors for this analysis. Source: 2002 Economic Census: Vehicle Inventory and Use Survey
# https://docs.google.com/spreadsheets/d/1Oe_jBIUb-kJ5yy9vkwaPgldVe4cloAtG/edit?usp=sharing&ouid=102742490305620802920&rtpof=true&sd=true
df_payload_distribution = pd.read_excel('data/payloaddistribution.xlsx')
df_payload_distribution['Payload (kg)'] = df_payload_distribution['Payload (lb)']*KG_PER_LB #payload distribution in kgs
# Read in default truck model parameters
parameters = truck_model_tools.read_parameters('data/default_truck_params.csv', 'data/default_economy_params.csv', 'data/constants.csv', 'data/default_vmt.csv')
# Read in default battery parameters
df_battery_data = pd.read_csv('data/default_battery_params.csv', index_col=0)
# Read in present NMC battery parameters
df_scenarios = pd.read_csv('data/scenario_data.csv', index_col=0)
e_present_density_NMC = float(df_scenarios['NMC battery energy density'].iloc[0])
eta_battery_NMC = df_battery_data['Value'].loc['NMC roundtrip efficiency']
# Set the drag coefficient to the reported value for the Tesla semi
parameters.cd = 0.22 # Source: https://eightify.app/summary/technology-and-innovation/elon-musk-unveils-tesla-semi-impressive-aerodynamic-design-long-range-efficient-charging
parameters.a_cabin = 10.7 # Source: https://www.motormatchup.com/catalog/Tesla/Semi-Truck/2022/Empty
###########################################################################################################
# Function to get NACFE results for the given truck and driving event
def get_nacfe_results(truck_name, driving_event):
# Collect the battery info extracted from the NACFE data for each truck
battery_capacity_df = pd.read_csv('data/pepsi_semi_battery_capacities.csv')
# Collect the info extracted from the drivecycle
drivecycle_data_df = pd.read_csv(f'data/{truck_name}_drivecycle_data.csv', index_col='Driving event')
# Collect NACFE results
NACFE_results = {
'Battery capacity (kWh)': battery_capacity_df[truck_name].iloc[0],
'Battery capacity unc (kWh)': battery_capacity_df[truck_name].iloc[1],
'Fuel economy (kWh/mi)': drivecycle_data_df['Fuel economy (kWh/mile)'].loc[driving_event],
'Fuel economy unc (kWh/mi)': drivecycle_data_df['Fuel economy unc (kWh/mile)'].loc[driving_event],
}
return NACFE_results
# Function to get the relative depth of discharge evaluated for the given truck and driving event and update the parameters with this dod
def update_event_dod(parameters, truck_name, driving_event):
# Collect the info extracted from the drivecycle
drivecycle_data_df = pd.read_csv(f'data/{truck_name}_drivecycle_data.csv', index_col='Driving event')
parameters.DoD = drivecycle_data_df['Depth of Discharge (%)'].loc[driving_event]/100.
# Function to get truck model results over a range of payload sizes
def get_model_results_vs_payload(truck_name, driving_event, payload_min=0, payload_max=100000, n_payloads=10, e_density_battery = e_present_density_NMC, battery_roundtrip_efficiency = eta_battery_NMC):
# Collect the drivecycle
df_drivecycle = truck_model_tools.extract_drivecycle_data(f'data/{truck_name}_drive_cycle_{driving_event}.csv')
vehicle_model_results = pd.DataFrame(columns = ['Average Payload (lb)', 'Battery capacity (kWh)', 'Fuel economy (kWh/mi)', 'Total vehicle mass (lbs)'])
for m_ave_payload in np.linspace(payload_min, payload_max, n_payloads):
parameters.m_ave_payload = m_ave_payload * KG_PER_LB
m_bat, e_bat, mileage, m = truck_model_tools.truck_model(parameters).get_battery_size(df_drivecycle, battery_roundtrip_efficiency, e_density_battery)
new_row = new_row = pd.DataFrame({
'Average Payload (lb)': [m_ave_payload],
'Battery capacity (kWh)': [e_bat],
'Fuel economy (kWh/mi)': [mileage],
'Total vehicle mass (lbs)': [m/KG_PER_LB]
})
vehicle_model_results = pd.concat([vehicle_model_results, new_row], ignore_index=True)
return vehicle_model_results
# Function to evaluate the payload and GVW for which the fuel economy and battery capacity best match the values extrapolated from the NACFE data
def evaluate_matching_payloads(vehicle_model_results, payload_min=0, payload_max=100000):
cs_e_bat = interp1d(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Battery capacity (kWh)'])
cs_mileage = interp1d(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Fuel economy (kWh/mi)'])
cs_m = interp1d(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Total vehicle mass (lbs)'])
def root_func(x, cs, y_target):
return cs(x) - y_target
payload_e_bat = root_scalar(lambda x: root_func(x, cs_e_bat, NACFE_results['Battery capacity (kWh)']), bracket=[payload_min, payload_max]).root
payload_mileage = root_scalar(lambda x: root_func(x, cs_mileage, NACFE_results['Fuel economy (kWh/mi)']), bracket=[payload_min, payload_max]).root
payload_average = (payload_e_bat + payload_mileage) / 2.
gvw_payload_average = cs_m(payload_average)
return payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m
# Function to visualize fit results
def visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m, combined_eff=None, max_power=None, battery_energy_density=None, battery_roundtrip_efficiency=None, resistance_coef=None):
fig, axs = plt.subplots(3, 1, figsize=(14, 10), gridspec_kw={'height_ratios': [1, 1, 1]}) # 3 rows, 1 column
name_title = truck_name.replace('_', ' ').capitalize()
if not combined_eff is None:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event} (Combined Eff: {combined_eff:.2f})', fontsize=20)
elif not max_power is None:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event} (Max Power: {max_power:.0f})', fontsize=20)
elif not battery_energy_density is None:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event} (Battery Energy Density: {battery_energy_density:.0f} kWh/ton)', fontsize=20)
elif not battery_roundtrip_efficiency is None:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event} (Battery Roundtrip Efficiency: {battery_roundtrip_efficiency:.2f})', fontsize=20)
elif not resistance_coef is None:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event} (Rolling Resistance: {resistance_coef:.4f})', fontsize=20)
else:
axs[0].set_title(f'{name_title}: Payload Estimation for Driving Event {driving_event}', fontsize=20)
axs[0].tick_params(axis='both', which='major', labelsize=14)
axs[1].tick_params(axis='both', which='major', labelsize=14)
axs[2].tick_params(axis='both', which='major', labelsize=14)
axs[0].set_ylabel('Battery capacity (kWh)', fontsize=16)
axs[1].set_ylabel('Fuel economy (kWh/mile)', fontsize=16)
axs[2].set_ylabel('GVW (lbs)', fontsize=16)
axs[2].set_xlabel('Payload (lb)', fontsize=16)
axs[0].plot(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Battery capacity (kWh)'], 'o')
xmin = min(vehicle_model_results['Average Payload (lb)'])
xmax = max(vehicle_model_results['Average Payload (lb)'])
xs=np.linspace(xmin, xmax, 100)
axs[0].plot(xs, cs_e_bat(xs), color='purple', label='cubic spline')
axs[0].axvline(payload_e_bat, color='green', label='Payload to match NACFE battery capacity')
axs[0].axhline(NACFE_results['Battery capacity (kWh)'], label='NACFE Analysis Result', color='red')
axs[0].fill_between(np.linspace(xmin, xmax, 5), NACFE_results['Battery capacity (kWh)']-NACFE_results['Battery capacity unc (kWh)'], NACFE_results['Battery capacity (kWh)']+NACFE_results['Battery capacity unc (kWh)'], label='NACFE Analysis Result', color='red', alpha=0.5, edgecolor=None)
xmin_plot, xmax_plot = axs[0].get_xlim()
ymin, ymax = axs[0].get_ylim()
axs[0].set_ylim(ymin - 0.5*(ymax-ymin), ymax)
axs[1].plot(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Fuel economy (kWh/mi)'], 'o')
xs=np.linspace(xmin, xmax, 100)
axs[1].plot(xs, cs_mileage(xs), color='purple')
axs[1].axvline(payload_mileage, color='green', label='Payload to match NACFE fuel economy')
axs[1].axhline(NACFE_results['Fuel economy (kWh/mi)'], color='red')
axs[1].fill_between(np.linspace(xmin, xmax, 5), NACFE_results['Fuel economy (kWh/mi)']-NACFE_results['Fuel economy unc (kWh/mi)'], NACFE_results['Fuel economy (kWh/mi)']+NACFE_results['Fuel economy unc (kWh/mi)'], color='red', alpha=0.5, edgecolor=None)
axs[1].set_xlim(xmin_plot, xmax_plot)
axs[2].plot(vehicle_model_results['Average Payload (lb)'], vehicle_model_results['Total vehicle mass (lbs)'], 'o')
xs=np.linspace(xmin, xmax, 100)
axs[2].plot(xs, cs_m(xs), color='purple')
axs[2].axvline(payload_average, color='green', ls='--', label=f'Average payload to match NACFE: {payload_average:.0f}')
axs[2].set_xlim(xmin_plot, xmax_plot)
battery_weight_payload_average = gvw_payload_average - payload_average - parameters.m_truck_no_bat / KG_PER_LB
tractor_weight_payload_average = gvw_payload_average - payload_average
axs[2].axhline(gvw_payload_average, color='red', ls='--', label=f'GVW for matching payload: {gvw_payload_average:.0f} lb\nBattery weight: {battery_weight_payload_average:.0f} lb\nUnloaded weight: {tractor_weight_payload_average:.0f} lb')
axs[0].legend(fontsize=16)
axs[1].legend(fontsize=16)
axs[2].legend(fontsize=16)
plt.tight_layout()
if not combined_eff is None:
combined_eff_save = str(combined_eff).replace('.', '')
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}_combinedeff_{combined_eff_save}.png')
elif not max_power is None:
max_power_save = str(int(max_power))
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}_maxpower_{max_power_save}.png')
elif not battery_energy_density is None:
battery_energy_density_save = str(int(battery_energy_density))
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}_battery_density_{battery_energy_density_save}.png')
elif not battery_roundtrip_efficiency is None:
battery_roundtrip_efficiency_save = str(int(battery_roundtrip_efficiency))
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}_battery_roundtrip_efficiency_{battery_roundtrip_efficiency_save}.png')
elif not resistance_coef is None:
resistance_coef_save = str(int(resistance_coef))
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}_battery_resistance_coef_{resistance_coef}.png')
else:
plt.savefig(f'plots/truck_model_results_vs_payload_{truck_name}_drivecycle_{driving_event}.png')
plt.close()
# Evaluate GVW for each truck and drivecycle event
evaluated_gvws = {}
for truck_name in drivecycles:
evaluated_gvws[truck_name] = []
drivecycle_events_list = drivecycles[truck_name]
for driving_event in drivecycle_events_list:
print(f'Processing {truck_name} event {driving_event}')
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m)
# Document the evaluated GVW
evaluated_gvws[truck_name].append(gvw_payload_average)
# Save the evaluated GVWs as a pickle file
with open('pickle/fitted_gvws.pkl', 'wb') as f:
pickle.dump(evaluated_gvws, f)
###########################################################################################################
######################### Analyze the distribution of GVWs evaluated by the model #########################
with open('pickle/fitted_gvws.pkl', 'rb') as f:
evaluated_gvws = pickle.load(f)
all_evaluated_gvws = np.zeros(0)
data_boxplot = []
labels_boxplot = []
for truck_name in evaluated_gvws:
evaluated_gvws_truck = np.array([float(i) for i in evaluated_gvws[truck_name]])
evaluated_gvws[truck_name] = evaluated_gvws_truck
all_evaluated_gvws = np.append(all_evaluated_gvws, evaluated_gvws_truck)
data_boxplot.append(evaluated_gvws_truck)
labels_boxplot.append(truck_name.replace('_', ' ').capitalize())
data_boxplot.append(all_evaluated_gvws)
labels_boxplot.append('Combined')
#print(all_evaluated_gvws)
#print(evaluated_gvws)
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.axhline(70000, color='red', ls='--')
ax.tick_params(axis='both', which='major', labelsize=14)
box = plt.boxplot(data_boxplot)
plt.xticks([1, 2, 3, 4], labels_boxplot)
for i in range(len(data_boxplot)):
# Get the x position for the current box plot
x_position = i+1
# Get the y position for the text annotation. This can be slightly above the box plot.
# You may need to adjust this depending on your specific data range and desired appearance.
data_max = max(data_boxplot[i])
data_min = min(data_boxplot[i])
y_position = data_max + 0.2*(data_max-data_min) # Just above the upper whisker
# Place the text annotation
n_drivecycles = len(data_boxplot[i])
ax.text(x_position, y_position, f'{n_drivecycles} drivecycles', ha='center', va='bottom', fontsize=12)
plt.tight_layout()
plt.savefig('plots/Evaluated_GVW_Distribution.png')
plt.close()
###########################################################################################################
###################### Plot the best-fitting GVW as a function of various parameters ######################
# Allow the max motor power to vary between 300,000W and 1,000,000W
truck_name = 'pepsi_1'
name_title = truck_name.replace('_', ' ').capitalize()
driving_event = 2
motor_powers = np.linspace(300000, 1000000, 10)
########## Evaluate best-fitting GVW vs. max motor power ##########
evaluated_gvws_df = pd.DataFrame(columns=['Max Motor Power (W)', 'Max GVW (lb)'])
for power in motor_powers:
parameters.p_motor_max = power
print(f'Processing {truck_name} event {driving_event} with motor power {power:.0f}W')
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m, max_power=power)
# Document the evaluated GVW
evaluated_gvws_df = pd.concat([evaluated_gvws_df, pd.DataFrame({'Max Motor Power (W)': [power], 'Max GVW (lb)': [gvw_payload_average]})], ignore_index=True)
# Save the evaluated GVWs as a pickle file
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_motor_power.pkl', 'wb') as f:
pickle.dump(evaluated_gvws_df, f)
###################################################################
############ Plot best-fitting GVW vs. max motor power ############
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_motor_power.pkl', 'rb') as f:
evaluated_gvws_df = pickle.load(f)
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(f'{name_title} Event {driving_event}', fontsize=20)
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.set_xlabel('Max Motor Power (W)', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.plot(evaluated_gvws_df['Max Motor Power (W)'], evaluated_gvws_df['Max GVW (lb)'], 'o')
ax.axvline(942900, color='red', ls='--', label='Tesla Semi Motor Power')
ax.legend(fontsize=16)
plt.savefig('plots/matching_gvw_vs_max_motor_power.png')
###################################################################
############# Evaluate best-fitting GVW vs. efficiency ############
evaluated_gvws_df = pd.DataFrame(columns=['Max GVW (lb)', 'Combined efficiency'])
combined_effs = np.linspace(0.83, 1., 10)
parameters.p_motor_max = 942900
for combined_eff in combined_effs:
parameters.eta_i = 1.
parameters.eta_m = 1.
parameters.eta_gs = combined_eff
print(f'Processing {truck_name} event {driving_event} with combined efficiency {combined_eff:.2f}W')
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
print(f'Evaluated GVW: {gvw_payload_average}')
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m)
# Document the evaluated GVW
evaluated_gvws_df = pd.concat([evaluated_gvws_df, pd.DataFrame({'Combined efficiency': [combined_eff], 'Max GVW (lb)': [gvw_payload_average]})], ignore_index=True)
# Save the evaluated GVWs as a pickle file
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_combined_eff.pkl', 'wb') as f:
pickle.dump(evaluated_gvws_df, f)
###################################################################
############ Plot best-fitting GVW vs. combined efficiency ############
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_combined_eff.pkl', 'rb') as f:
evaluated_gvws_df = pickle.load(f)
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(f'{name_title} Event {driving_event}', fontsize=20)
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.set_xlabel('Combined powertrain efficiency (%)', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.plot(evaluated_gvws_df['Combined efficiency'], evaluated_gvws_df['Max GVW (lb)'], 'o')
plt.savefig('plots/matching_gvw_vs_combined_eff.png')
###################################################################
###########################################################################################################
############# Evaluate best-fitting GVW vs. energy density ############
truck_name = 'pepsi_1'
name_title = truck_name.replace('_', ' ').capitalize()
driving_event = 2
evaluated_gvws_df = pd.DataFrame(columns=['Max GVW (lb)', 'Battery Energy Density (kWh/ton)'])
battery_energy_densities = np.linspace(150, 500, 10)
for e_density_battery in battery_energy_densities:
print(f'Processing {truck_name} event {driving_event} with energy denstiy {e_density_battery:.0f}kWh/ton')
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event, e_density_battery = e_density_battery)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
print(f'Evaluated GVW: {gvw_payload_average}')
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m, battery_energy_density = e_density_battery)
# Document the evaluated GVW
evaluated_gvws_df = pd.concat([evaluated_gvws_df, pd.DataFrame({'Battery Energy Density (kWh/ton)': [e_density_battery], 'Max GVW (lb)': [gvw_payload_average]})], ignore_index=True)
# Save the evaluated GVWs as a pickle file
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_battery_energy_density.pkl', 'wb') as f:
pickle.dump(evaluated_gvws_df, f)
###################################################################
############ Plot best-fitting GVW vs. combined efficiency ############
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_battery_energy_density.pkl', 'rb') as f:
evaluated_gvws_df = pickle.load(f)
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(f'{name_title} Event {driving_event}', fontsize=20)
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.set_xlabel('Battery Energy Density (kWh/ton)', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.plot(evaluated_gvws_df['Battery Energy Density (kWh/ton)'], evaluated_gvws_df['Max GVW (lb)'], 'o')
plt.savefig('plots/matching_gvw_vs_battery_energy_density.png')
###################################################################
###########################################################################################################
############# Evaluate best-fitting GVW vs. battery roundtrip efficiency ############
truck_name = 'pepsi_1'
name_title = truck_name.replace('_', ' ').capitalize()
driving_event = 2
evaluated_gvws_df = pd.DataFrame(columns=['Max GVW (lb)', 'Battery Roundtrip Efficiency'])
battery_roundtrip_efficiencies = np.linspace(0.9, 1, 10)
parameters.m_max = 100000
for roundtrip_efficiency in battery_roundtrip_efficiencies:
print(f'Processing {truck_name} event {driving_event} with roundtrip effiency {roundtrip_efficiency:.2f}')
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event, battery_roundtrip_efficiency = roundtrip_efficiency)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
print(f'Evaluated GVW: {gvw_payload_average}')
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m, battery_roundtrip_efficiency = roundtrip_efficiency)
# Document the evaluated GVW
evaluated_gvws_df = pd.concat([evaluated_gvws_df, pd.DataFrame({'Battery Roundtrip Efficiency': [roundtrip_efficiency], 'Max GVW (lb)': [gvw_payload_average]})], ignore_index=True)
# Save the evaluated GVWs as a pickle file
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_battery_roundtrip_efficiency.pkl', 'wb') as f:
pickle.dump(evaluated_gvws_df, f)
###################################################################
############ Plot best-fitting GVW vs. combined efficiency ############
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_battery_roundtrip_efficiency.pkl', 'rb') as f:
evaluated_gvws_df = pickle.load(f)
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(f'{name_title} Event {driving_event}', fontsize=20)
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.set_xlabel('Battery Roundtrip Efficiency', fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.plot(evaluated_gvws_df['Battery Roundtrip Efficiency'], evaluated_gvws_df['Max GVW (lb)'], 'o')
plt.savefig('plots/matching_gvw_vs_battery_roundtrip_efficiency.png')
###################################################################
###########################################################################################################
############# Evaluate best-fitting GVW vs. rolling resistance coefficient ############
truck_name = 'pepsi_1'
name_title = truck_name.replace('_', ' ').capitalize()
driving_event = 2
evaluated_gvws_df = pd.DataFrame(columns=['Max GVW (lb)', 'Resistance Coefficient'])
resistance_coefs = np.linspace(0.004, 0.008, 10)
parameters.m_max = 100000
for resistance_coef in resistance_coefs:
print(f'Processing {truck_name} event {driving_event} with resistance coef {resistance_coef:.4f}')
parameters.cr = resistance_coef
# Read in the NACFE results
NACFE_results = get_nacfe_results(truck_name, driving_event)
# Update the depth of discharge for the driving event based on the NACFE data
update_event_dod(parameters, truck_name, driving_event)
# Get the vehicle model results (as a dataframe) as a function of payload
vehicle_model_results = get_model_results_vs_payload(truck_name, driving_event)
# Get the payloads and resulting GVW for which the truck model results best match the NACFE data. Also collect the cubic splines used for this evaluation (for the purpose of visualization)
payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m = evaluate_matching_payloads(vehicle_model_results)
print(f'Evaluated GVW: {gvw_payload_average}')
# Visualize the results
visualize_results(truck_name, driving_event, vehicle_model_results, NACFE_results, payload_e_bat, payload_mileage, payload_average, gvw_payload_average, cs_e_bat, cs_mileage, cs_m, resistance_coef = resistance_coef)
# Document the evaluated GVW
evaluated_gvws_df = pd.concat([evaluated_gvws_df, pd.DataFrame({'Resistance Coefficient': [resistance_coef], 'Max GVW (lb)': [gvw_payload_average]})], ignore_index=True)
# Save the evaluated GVWs as a pickle file
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_resistance_coef.pkl', 'wb') as f:
pickle.dump(evaluated_gvws_df, f)
###################################################################
############ Plot best-fitting GVW vs. combined efficiency ############
with open(f'pickle/fitted_gvws_{truck_name}_{driving_event}_vs_resistance_coef.pkl', 'rb') as f:
evaluated_gvws_df = pickle.load(f)
fig, ax = plt.subplots(figsize=(9, 5))
ax.set_title(f'{name_title} Event {driving_event}', fontsize=20)
ax.set_ylabel('GVW best matching NACFE Results (lbs)', fontsize=15)
ax.set_xlabel('Rolling Resistance Coefficient', fontsize=15)
ax.axvline(0.0044, ls='--', color='red', label='Best value in literature')
ax.tick_params(axis='both', which='major', labelsize=14)
ax.plot(evaluated_gvws_df['Resistance Coefficient'], evaluated_gvws_df['Max GVW (lb)'], 'o')
ax.legend(fontsize=16)
plt.tight_layout()
plt.savefig('plots/matching_gvw_vs_resistance_coef.png')
###################################################################
###########################################################################################################