Page 1
ENHANCING MOVES FOR
CONNECTED AND AUTOMATED VEHICLE ANALYSIS
Matthew Barth Yeager Families Professor, CE-CERT Director
[email protected]
ENHANCING MOVES FOR CONNECTED AND AUTOMATED VEHICLE ANALYSIS
David Oswald CE-CERT Graduate Student Researcher
Page 2
TRANSPORTATION IS UNDERGOING FOUR MAJOR REVOLUTIONS
Shared Mobility:
Electrification: • electric drivetrains are becoming more common • Drivers: advances in motors, controls, and batteries
Connectivity: • Vehicles are increasingly “connected” • Drivers: cellular communications, dedicated short range communications
• carsharing, ride hailing companies (e.g., Uber, Lyft), and advanced transit • Drivers: Internet connectivity, convenience, and transportation costs
Automation: • Vehicle automation is emerging in many forms • Automation comes with many social implications
Page 3
General Components of a Transportation-basedEmissions/Energy Inventory:
• emissions/energy factors environmental • vehicle activity inventory
• fleet composition
Page 4
Shared Mobility
RouteE
BEAM
CAV+ Traffic Flow
CAV+Mode Choice
FUTURE TRANSPORTATION: MODELING ENERGY & EMISSIONS
Shared Mobility: • Vehicle Activity: Travel Demand Models need to change • Vehicle Operation should remain the same
Electrification: • Vehicle category is simply changed within MOVES • Indirect Emissions: need to know energy sources that produce electricity
Connectivity: • Vehicle types will likely be newer, energy efficient, and low emissions • Vehicle Operation/Activity will likely be smoother
Automation: • Vehicle types will likely be newer, energy efficient, and low emissions • Vehicle Operation/Activity will likely be smoother
Page 5
or
v h1cl act y
veloc1 y traJec ory a
gr de 1f ava1la e)
br o
Project-Level Emission Modeling 70
Travel time = 10 minutes
Veh
icle
Spe
ed, m
ph
60 Travel distance = 3.7 miles 50 Travel speed = 21.9 mph 40
30
20
vehicle activity (velocity trajectory and grade if available) 10
0
3 0
or:
vehicle calibration Emissions parameters and Modeling
emissions factors
traffic simulation
0.018 3.0 NOx emission = 1.5 g
Fuel consumption = 464 g = 0.17 gallons 0.015 (NOx emission fee = X dollars/g)2.5
Fuel
Con
sum
ptio
n, g
Fuel price = $2.7/gallon Travel fuel cost = 0.5 dollars2.0
(Travel NOx emission cost = 1.5X dollars)0.012
0.009 1.5
0.006 1.0
0.0030.5
0.000 1 101 301 501201 401
fuel consumption emissions
0.0 601
Page 6
--
Three regimes on how Connected & Automated Vehicles can reduce on-road energy and emissions
CO2
(g/m
i) 1000
900
800
700
600
500
400
300
200
100
0
Increases capacity of roadways through congestion reduction
Reduction of stop and go driving
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
Average Speed (mph)
Real-world driving Smooth driving
Congestion Reduction
Traffic flow smoothing
Platooning reduces aerodynamic drag
Aerodynamic Drag Reduction
Source: Barth, M. and Boriboonsomsin, K. (2008). Real-world carbon dioxide impacts of traffic congestion. Transportation Research Record, 2058, 163-171.
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-
0
3
6
9
5 12 -~ .:.:: - 18 Q. Cl)
> 24
30
30+
0
11 12 13 14 15
>
16
Speed (mph) 25 50 50+
21 22 33 23 24 35 25
27 37
28 38
29 39
30 40
Motor Vehicle Emission Simulator (MOVES) • Data-Driven Emission Model • Uses a Binning Approach for Vehicle Operation Mode (OpMode) and Emission Factors
Page 8
Vehicle Equipped with the
Eco-Approach and Departure at Signalized
Intersections Application (CACC capabilities
optional)
Source: Noblis . November 2013 Traffic Signal Head
Traffic Signal
1controller with
I ,,., '"""""
MOVES Sensitivity Analysis • In our real-world connected and automated vehicle experiments, we noticed
that MOVES was under predicting the energy and emissions benefits • We initiated a study to compare real-world fuel consumption, MOVES, and
CMEM
Page 9
Speed (mph) 0 50 50+
0 00 0
0
00 0 0 0 0 O O O 0 O O O O O
oO O O O o ooo:},oo o
..
3 0 0 8 ••
6
9 35
12
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00 37
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000 0 0 0 0 <oo '/, .. 0 ..
0 ':. 0
0 0 0 0 0 0 .. 29 8 0 .. · 0 0
0 39 0 0 0 0 .. 0 0
0 0
30 • r-------.,---,i-------<..--:;---~=----f------o---------.:__--::--__ _J 0
0 .. 0
0 0
0
0
30 0 40 0
0 .. 0 30+ '------------___.L_ ____ ____:_ ..:::__ ___ _l__ __ ....:.._ ___ __:._ __ _J
Calibrating MOVES: Training Data Set
• Second-by-second data were collected from numerous driving trips and experiments • Data are used to calibrate MOVES fuel consumption factors and activity in the various
OpModes
Page 10
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>
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12
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24
30
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• . • • • • • •
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5
4.5
4
3.5
3
2.5
2
1.5
0.5
0
Fuel Consumption mapped to MOVES bins
• Second-by-second fuel consumption (grams/s) • Max value: 5.41 grams/s
Page 11
• 15
20
C
B
I Q. (/)
>
25
30
JS
10
• . • • • • • •• •
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Bin 16 VSP vs MPH plot
Speed (mph) 16
• • •
' • • • • •
• • .. • • • • • ...
• • •
•
•
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18
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20
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• • • • • • • •• • •• •
• • •
• • • 2.5
• • • • • •
• • •
• 1.5
• • •
• 0.5
Fuel Consumption map of MOVES Bin 16
• Wide range of values with lower VSP values dominated by lower fuel values • Range: [0.38, 4.59]
Page 12
MOVES Bin Statistics 11 # of Values: 6057
Range: [0.199, 3.01] Mean = 0.324 S.D. = 0.177 21 # of Values: 2923
Range: [0.23, 3.6266] Mean = 0.195 S.D. = 0.323
33 # of Values: 108 Range: [0.327, 3.99]
Mean = 0.511 S.D. = 0.869 12 # of Values: 2335
Range: [0.21,2.967] Mean = 0.624 S.D. = 0.357 22 # of Values: 1631
Range: [0.238,2.73] Mean = 0.465 S.D. = 0.403
13 # of Values: 1288 Range: [0.24, 2.856]
Mean = 0.953 S.D. = 0.429 23 # of Values: 1931
Range: [0.24, 3.566] Mean = 0.714 S.D. = 0.412
14 # of Values: 1229 Range: [0.27, 3.5]
Mean = 1.22 S.D. = 0.437 24 # of Values: 1470
Range: [0.26, 3.744] Mean = 0.939 S.D. = 0.417
35 # of Values: 56 Range: [0.35, 3.51]
Mean = 1.2 S.D. = 1.139
15 # of Values: 914 Range: [0.34, 3.586]
Mean = 1.501 S.D. = 0.451 25 # of Values: 1002
Range: [0.247, 3.59] Mean = 1.18 S.D. = 0.507
27 # of Values: 842 Range: [0.31, 4.11]
Mean = 1.64 S.D. = 0.651 37 # of Values: 42
Range: [0.368, 3.75] Mean = 1.908 S.D. = 1.1454
16 # of Values: 600 Range: [0.38, 4.59]
Mean = 2.07 S.D. = 0.699
28 # of Values: 188 Range: [0.43, 4.37]
Mean = 2.39 S.D. = 0.831 38 # of Values: 28
Range: [0.4, 4.1159] Mean = 2.14 S.D. = 1.282
29 # of Values: 62 Range: [0.36, 5.1]
Mean = 3.17 S.D. = 0.892 39 # of Values: 30
Range: [0.4566, 4.745] Mean = 3.23 S.D. = 1.435
30 # of Values: 30 Range: [2.44, 5.22]
Mean = 3.745 S.D. = 0.856 40 # of Values: 30
Range: [0.67, 5.41] Mean = 3.83 S.D. = 1.54
Page 13
0
3
6
9
~ 12 -~ .=.:: - 18 Q. Cl)
> 24
30
30+
0
11 12 13 14 15
16
Speed (mph) 25 50 50+
21 22 33 23 24 --25 ~~
-- --.... vi
-- --,._ V ~u
-- --,~ ~~
30 40
Improving Resolution Using Sub-Binning
• Green lines show new bin borders
Page 14
C 0
0
3
6
9
12
.., 15
~ ~ 18 en > 21
24
27
30
30+
0
111
121
131
141
151
161
162
163
164
165
166
167
Speed (mph) 25 50 50+
211 331
221 332
231 333
241 351
251 352
271 371
272 372
281 381
282 382
291 391
292 392
301 401
New Bin Definitions
• New Bin Numbering System
Page 15
0
3
6
9
- 12 0
C 0 - 15 ~ .:a::
0
- 18 c.. "' > 21
<9 0 -.r>
163° 0 =i 0o 0
0 00
164 0
24 165
27 166
30
167
0 0
0 0
0
25
0
0 0 00
0
0 0
Speed (mph)
0 0 0
0
0
0
50 0
0 0
0 0 0 i1e O 0 o oo o o oo
0 oJ O 0
">01 Q:io 4y O 00
0o 0
0 0
0
292 0 0
0
0 00
0
0
0 0
0
0
0
0
0 oo
O 00 0 331°
0
0
0
0
0 0
00 0 0
0 0 0
0 0 0
c§) 0 0
0 0
0o 0
0 00332 0
0
~3 ooo
0
0 0
351 0
352
371
0
381 ° 0
0 0 0 Oo O 'lC,2
~ oO
0 0 0 00 391 0
0
0 391 0
0
0 401 0
50+
0
0
0 0
0
0 oO 0 30+ L::::::==============i_ ________ _:_ _____ ...L. ___ _:_ __________ ...J
0
New Bin Definitions
• Training data plotted in new bins • Green boxes highlight what was changed
Page 16
50+
--'2 12
-0 120 ~ 15
24
~ 18 0.. Ul 21 >
I 24
?7
27
30
30+
122 ° 121 . , 0 i O ' • ., 220
222 c o V "'
& •
l
Emissions & Fuel Factors
3.64208
3.41281
3.54149
3.67345
High Resolution Bins
3.41281 + 3.54149 + 3.67345 + 3.64208 = 3.5674575 ≅ 3.5675 4
Emission factor for bin 120 is 3.5675 Do the same process for each bin of High Res. MOVES
Page 17
D
D
D
Calibration of Emissions & Fuel Factors
0 0.2533 1 0.2908 111 0.3746 112 0.6262 113 0.9336 114 1.2210 115 1.4943
0 0.2533 1 0.2908 11 0.3746 12 0.6262 13 0.9336 14 1.2210 15 1.4943 16 3.0875
211 0.2908 212 0.4712 Calibrated 213 0.7115 214 0.9475 MOVES 215 1.2142
Avg.
Avg. 116 1.8635 117 2.3965 118 2.7907 119 2.9698 120 3.5675 121 3.7988 122 4.2254
21 0.2908 22 0.4712 23 0.7115 24 0.9475 25 1.2142 High Res.
311 0.1432 312 0.7473 313 0.7243 314 0.7131 315 1.8969 316 1.8942 317 1.9222 318 1.0207 319 2.7613 320 2.8209 321 4.1931
30 3.9454
0.5383 33 1.3050 35 1.9082 37 1.8910 38 3.5070 39
Calibrated MOVES
216 1.5973 217 2.0022 218 2.3361 219 2.8737 220 2.9989 221 3.8236 222 3.9454
High Res.
40 3.8337
Calibrated 322 3.8337 MOVES
High Res.
27 1.7998 28 2.6049 29 3.4113
Avg.
Page 18
Comparison Results
Fuel Consumption Avg. g/mile
Method Measured MOVES Calibrated MOVES
High Res. MOVES
gram/mile 147.9 170.5 154.69 150.41
% Diff +15.3% +4.59% +1.69%
• Data from typical driving • MOVES: uncalibrated results • Calibrated MOVES: MOVES calibrated based on vehicle fuel consumption data • High Resolution MOVES: uses sub-bins
Page 19
Extrapolation: Developing a Bin-Pyramid
Decrease Resolution
Increase Resolution
Top Level (Current Bins)
...
Bottom Layer (1 VSP by 12.5 mph)
Page 20
-
Eco-Approach and Departure at Signalized
Intersections Application (CACC capabilities
optional)
Source: Noblis. November 2013
Roadside
Traffic Signal Head
Traffic Signal Controller with SPaT Interface
Trajectory Smoothing
• There are many applications that attempt to “smooth” trajectories without loss oftravel time: Eco-Pedal, traffic-lightanticipation, etc.
• Example: the Eco-Approach and Departure(EAD) connected vehicle application
• Two cars drove at the same time on the same street, one using EAD techniques and the other driving normally
Increases capacity of roadways through congestion reduction
Reduction of stop
Real-world driving Smooth driving
Congestion Reduction
Traffic flow smoothing
Platooning reduces aerodynamic drag
Aerodynamic Drag Reduction
Page 21
Speed (mph) Speed (mph) 0 25 50 50+ 0 25 50 50+
0 0 33 33
3 3
6 6
9 35 9 35 S 12 S 12 - -~ 0 37 ~ 37
.:.:: .:.:: - 18 - 18 Q. 0 Q. CJ) 0
~8 38 CJ) 28 38 > 46° > 16 24 24
29 39 29 39 30 30
30 40 30 40 30+ 30+
EAD vs. Non-EAD Driving
Non-EAD EAD
Page 22
EAD vs. Non-EAD Driving
Fuel Consumption Avg. g/mile
Method Measured MOVES Calibrated MOVES
High Res. MOVES
No EAD 137.63 158.3 144.9 140.63
EAD 128.51 154.6 141.2 136.3
Improvement 6.63% 2.33% 2.55% 3.08%
Page 23
Speed (mph) Speed (mph) 0 25 50 50+ 0 25 50 50+
0 0 33 33
3 3
6 6
9 35 9 35 S 12 S 12 - -~ O 0 37 ~ 37
.:.:: .:.:: - 18 - 18 Q. 0 Q. CJ) 0
38 CJ) 38 > 60 > 16 24 24
29 39 29 39 30 30
30 40 30 40 30+ 30+
EAD vs. Non-EAD Driving
Non-EAD EAD
Page 24
Speed (mph) Speed (mph) 0 25 50 50+ 0 25 50 50+
0 0 33 33
3 3
6 6
9 9 C: 12 C: 12 0 0 -~
.:.:: -Q. 18
-~ .:.:: -Q.
18 CJ) CJ)
> > 24 24
30 30
30 40 30 40 30+ 30+
EAD vs. Non-EAD Driving
Non-EAD EAD
Page 25
Speed (mph) 0 25 50 50+
0 331
3 332
6 ~~~~ 333 9 I--~ ~ 351
- 352 5 12 1---f'--ii~~~~~~ +--..;;...;;;..;;;;;__--i i 15 l---i+--=~~r-~~+--+--3.;:;_7;;_1.:..,.._--i ~ 18 1----+---------f"-"'-~----t---+--3_7_2_ ..... 0..
0163 0 381 "'> 21 . ~ ~ ==:t===~~ ;--,-:;;;-::;--,
~~4 382 24 -------------165 391 27 -------------166 392 30 -------------
167 301 401 30+-----------------
Speed (mph) 0 25 50 50+
0 331
3 332
6 1---.11~ 333 9 ~ ~!.i.. ~ jijlil.Mi~ 351
- I -~~~~ 352 5 12 t- ~ ;..,....--+---..;;...;;;..;;;;;__~ ~ 15 1------/o-l~~~....,,.;.:~;..__-+---3.;:;_7;;_1.:..,.._~ ~ 18 l-------'1------------~...,.o
0
-+-7_2_-+-_3_7_2_--1 o.. 281 381 "'> 21
164 282 382 24 -------------165 291 391 27 -------------166 292 392 30 -------------
167 301 401 30+-----------------
EAD vs. Non-EAD Driving
Non-EAD EAD
Page 26
\ \
\ \
Conclusions and Recommendations • Traffic Smoothing effects tend to get
washed out in MOVES due to bin size • Recommendation: MOVES can be preserved
and enhanced with a sub-binning approach • MOVES could be used at different
“resolutions” using a Bin-Pyramid approach; original MOVES model is preserved
• New Guidance Documents can be written that suggests what resolution should beused automated resolution determination
Top Level (Current Bins)
...
Page 27
Alternative Vehicle Emission Modeling Approaches
Array of new modeling techniques developed since the late 1990’s:
• Fuel-Based Emission Inventories • normalizes vehicle emissions to fuel consumption, not VMT • requires estimates of fuel use, e.g., from fuel tax • generates reasonable emission inventories for large databases
• Modal and instantaneous vehicle emission models: • concerned with estimating emissions as a function of vehicle operating
mode, (e.g., idle, acceleration, cruise, deceleration) • predicts emissions second-by-second
• Statistical Models: • Many models exist…
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COMP'REHEINSIVE M ,ODAL EMISSIONS IMODEL (CMIEM)
COMPREHENSIVE MODAL EMISSIONS MODEL (CMEM)
• Microscale emission model • Developed at UCR CE-CERT • Initially developed in the 1990’s, lightly maintained
• Sponsorship • National Cooperative Highway Research Program (NCHRP) • U.S. Environmental Protection Agency (EPA)
• Objective • Model vehicle emissions at the project level ( sec-by-sec) • Accurately reflect the impact on emissions from various operating
conditions/parameters • vehicle speed, acceleration, and road grade • starting conditions, temperature (history effects) • secondary engine load, etc.
Page 29
-{
1
2
3
4
5
6
7
8
9
10
11
24
26
27
12
13
14
15
16
17
18
25
40
Vehicle Technolo
2-w ay Cat alyst
3-way Cat alyst, Carburet ed
3-way Cata lyst, Fl, >SOK m iles, low power/weight
3-w ay Cat alyst, Fl, >SOK miles, high pow er/weight
3-w ay Cat alyst, Fl, <SOK miles, low power/weight
3-way Cat alyst, Fl, <SOK miles, high pow er/weight
Tier 1, >SOK miles, low pow er/weight
Tier 1, >SOK mi les, high power/weight
Tier 1, <SOK miles, low power/weight
Tier 1, <SOK mi les, high pow er/weight
Tier 1, >lOOK miles
Ultra l ow Emission Veh icle {ULEV)
Super Ultra l ow Emission Veh icle {SUlEV) / Parti al Zero
Emission Veh icle (PZEV)
Normal Emitting Trucks '=========== Pre-1979 ( <=8500 GVW)
1979 t o 1983 ( <=8500 GVW}
1984 t o 1987 {<=8500 GVW}
1988 t o 1993, <=3750 LVW
1988 t o 199-3, >3750 LVW
Tier 1 LDT2/3 {3751-5750 l VW or A lt. l VW)
Tier 1 LDT4 (6001-8500 GVW, >5750Alt. l VW)
Gasolin e-powered, LDT {> 8500 GVW}
Diese l-powered, LDT {> 8500 GVW}
...... """!!' .................................... .....,H...,igh Em itti ng...,V..,e;.,.h...,i...,.cl..,e.;.,s .................................... ....,1
19
20
21
22
23
45
46
47
Runs lean
Runs ri ch
Misfire
Bad cat alyst
Runs very r ich
19,94 t o 1997, 4-st rok.e, Elec. Fl HOOT
1998, 4-st rn lke, Elec. Fl HOOT
1999 t o WOO, 4-st ro k.e, Elec. Fl HDDT
Veh
icle
Spe
ed, m
ph
Vehicle Activity (velocity trajectory) 70
Travel time = 10 minutes 60 Travel distance = 3.7 miles 50 Travel speed = 21.9 mph 40
30
20
10
0
3 0 Grade (optional) Calibration Vehicle Parameters
General Vehicle Parameters
28 LDV CMEM Emissions
0.018 NOx emission = 1.5 g
0.015 (NOx emission fee = X dollars/g) (Travel NOx emission cost = 1.5X dollars)
0.012
0.009
0.006
0.003
0.000 1 101 201 301 401 501
Fuel Consumption
Fu
el C
onsu
mp
tion
, g
Fuel consumption = 464 g = 0.17 gallons 2.5 Fuel price = $2.7/gallon
Travel fuel cost = 0.5 dollars2.0
1.5
1.0
0.5 3 HDDV 0.0
601
3.0
Page 30
CMEM EMISSION MODEL STRUCTURE
~ • operating
parameters ~
Engine Power
--------Demand
vehicle parameters ~
--------
Engine Speed
1 Fuel Rate
i Eugiu Contrnl
Engine-Out ------. Emissions
i---------.
i-----------.: Exhaust After
Treatment
tailpipe emissions,
fuel use
CMEM EMISSION MODEL STRUCTURE
• Fuel is a function of Engine Power Demand and Engine Speed
• Fuel rate is related to emissions through analysis based on measured data
• Model Inputs • Operating parameters - vehicle speed, road grade, accessory power, etc. • Vehicle parameters – weight, gear ratios, calibrated emission parameters, etc.
• Model Outputs • Second–by–second emission data and fuel use
Page 31
EAD vs. ON-EAD WITH CMEM
31
EAD VS. NON-EAD WITH CMEM
Fuel Consumption Avg. g/mile
Method Measured MOVES Calibrated MOVES
High Res. MOVES CMEM
No EAD 137.63 158.3 144.9 140.63 138.97
EAD 128.51 154.6 141.2 136.3 132.5
Improvement 6.63% 2.33% 2.55% 3.08% 4.65%
Page 32
CONCLUSIONS AND RECOMMENDATIONS
• MOVES Modeling Approach tends to under-estimate traffic smoothing effects due to connected and automated applications
• Sub-Binning Approach can improve resolution • All Modeling Approaches tends to miss effects of aerodynamic
drag reduction effects • Consider adopting a complementary physical modal or
instantaneous emissions model for connected and automated vehicle scenarios, as well as others that have a strong historyeffect in their emissions generation (SCR, after treatment, etc.)
• Dust off PERE emissions generator model that was previouslyused for MOVES?
Page 33
THA K vouJ THANK YOU! David Oswald
CE-CERT Graduate Student Researcher