Measuring and Modeling Mobile Source Fuel Consumption and Emissions Matthew Barth Professor and Director College of Engineering-Center for Environmental Research and Technology University of California-Riverside Acknowledgements: Kanok Boriboonsomsin, George Scora, University of California Energy Institute, University of California Transportation Center, VW Automotive Group, Nissan Motor Company
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Measuring and Modeling Mobile Source Fuel Consumption and Emissions
Matthew BarthProfessor and Director
College of Engineering-Center for Environmental Research and TechnologyUniversity of California-Riverside
Acknowledgements: Kanok Boriboonsomsin, George Scora, University of California Energy Institute, University of California Transportation Center, VW Automotive Group, Nissan Motor Company
UC Riverside CEUC Riverside CE--CERT:CERT:
CCollege of ollege of EEngineeringngineering--CCenter for enter for EEnvironmental nvironmental RResearch and esearch and TTechnologyechnology
• collaborative research center involving government, industry, and academia to develop and assess transportation technologies for environmental benefit and energy efficiency
• interdisciplinary center of approximately 40 full-time faculty and staff plus 10-40 graduate and undergraduate students
• contracts and grant activity at approximately $8M per year
www.cert.ucr.edu
CE-CERT’s Laboratories:Emissions and Fuels Research Lab
• Principal Investigators: Dr. Wayne Miller, Dr. Tom Durbin, Dr. David Cocker,, Dr. Heejung Jung, J. Pisano, Bill Welch
• focus areas:• mobile and stationary source emissions• fuel effects
Atmospheric Processes Research Lab• Principal Investigators: Dr. Bill Carter, Dennis Fitz, Dr.
David Cocker• focus areas:
• atmospheric processes: smog chambers• ambient air measurements
Intelligent Transportation Systems & Vehicle Technology Research Lab• Principal Investigators: Dr. Matt Barth, Dr. Yushan Yan• focus areas:
•• Emission Measurement and Characterization Emission Measurement and Characterization •• Advanced Emission Control Technology Advanced Emission Control Technology •• OnOn--Road Emission Measurements Road Emission Measurements •• Emissions Reactivity and InventoriesEmissions Reactivity and Inventories•• School Buses School Buses •• Stationary Sources: Commercial CookingStationary Sources: Commercial Cooking•• Fuel EffectsFuel Effects
controller
MDU
CR
antenna
vehicle signals
and controls
controller
MDU
CR
antenna
vehicle signals
and controls
controller
controller
MDU
CR
antenna
vehicle signals
and controls
Intelligent Transportation Systems and Vehicle TechnologyResearch Laboratory
•• Intelligent Transportation Systems Intelligent Transportation Systems •• Integrated Transportation and Emissions Modeling Integrated Transportation and Emissions Modeling •• Advanced Sensing and ControlsAdvanced Sensing and Controls•• Vehicle Guidance and ControlVehicle Guidance and Control•• Vehicle Activity Analysis Vehicle Activity Analysis •• Vehicle Technology Vehicle Technology •• Environmental Materials, Fuel Cells and Reformers Environmental Materials, Fuel Cells and Reformers
Atmospheric Processes Research Laboratory
•• SMOG Chamber Gas and Particle Studies SMOG Chamber Gas and Particle Studies •• Air Sampling and Monitoring Air Sampling and Monitoring •• Analysis and Modeling Analysis and Modeling •• Chemical Analysis Chemical Analysis
•• Synthetic Diesel Fuel from biomassSynthetic Diesel Fuel from biomass•• Synthetic Diesel Fuel from coalSynthetic Diesel Fuel from coal•• Renewable Alcohol Fuels: cellulosic ethanolRenewable Alcohol Fuels: cellulosic ethanol•• Biomass to Fuel Conversion Biomass to Fuel Conversion •• Solar Energy Solar Energy
Sustainable Energy Research
E-10
E-11
P-1
E-12
P-4
E-13
E-14
E-15
E-17
E-16
E-19
E-18
P-9
P-16 P-17
P-18
P-19
P-20
E-20
E-21
V-1
Hydro-gasifier
Steamgenerator
SteamReformer
FuelSynthesizer
P-21
E-24
E-25
E-27
E-28
P-23
P-24
P-25
P-26
P-27
V-2
MotorGenerator
Liquid FuelProduct
RecycledLiquidWater
RecycledHydrogen
SlurryPump
WasteMaterials
RecycledLiquidWater
SlurryTank
P-32
P-34
P-37
P-36
Molten SaltLoops
W a s t e t o E n e r g y v ia U C R C E -C E R T H y d r o -g a s i f ic a t io n & F u e l S y n t h e s is *
• MOBILE Model• developed in the late 1970’s by U.S. Environmental Protection Agency• has undergone significant expansion, improvements over the years• uses method of base emission rates and correction factors• transition to MOVES model
• California Air Resources Board’s EMFAC Model• developed separately from MOBILE by CARB• based on stricter standards and fuels used in California• uses similar methodology as MOBILE
Other Vehicle Emission Modeling Methods• Array of new modeling techniques developed in 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
Normal Emitting Trucks12 Pre-1979 (<=8500 GVW)13 1979 to 1983 (<=8500 GVW)14 1984 to 1987 (<=8500 GVW)15 1988 to 1993, <=3750 LVW16 1988 to 1993, >3750 LVW17 Tier 1 LDT2/3 (3751-5750 LVW or Alt. LVW)18 Tier 1 LDT4 (6001-8500 GVW, >5750 Alt. LVW)25 Gasoline-powered, LDT (> 8500 GVW)40 Diesel-powered, LDT (> 8500 GVW)41 Pre 1991, 2-stroke HDDT42 Pre 1991, 4-stroke HDDT43
52 PZEVNormal Emitting Trucks
12 Pre-1979 (<=8500 GVW)13 1979 to 1983 (<=8500 GVW)14 1984 to 1987 (<=8500 GVW)15 1988 to 1993, <=3750 LVW16 1988 to 1993, >3750 LVW17 Tier 1 LDT2/3 (3751-5750 LVW or Alt. LVW)18 Tier 1 LDT4 (6001-8500 GVW, >5750 Alt. LVW)25 Gasoline-powered, LDT (> 8500 GVW)40 Diesel-powered, LDT (> 8500 GVW)41 Pre 1991, 2-stroke HDDT42 Pre 1991, 4-stroke HDDT43 1991 to 1993, 4-stroke, Mech. FI HDDT44 1991 to 1993, 4-stroke, Elect. FI HDDT45 1994 to 1997, 4-stroke, Elect. FI HDDT46 1998, 4-stroke, Elect. FI HDDT47 1999 to 2002, 4-stroke, Elect. FI HDDT
High Emitting Light Duty Vehicles19 Runs lean20 Runs rich21 Misfire22 Bad catalyst23 Runs very rich
• corridor analysis• truck lane analysis• HOT (high
occupancy toll) lane analysis
• tunnel study• BRT (bus rapid
transit)
• PARAMICS integration with CMEM
• Deriving “link-based” or “trip-based” emission factors
• Using specific facility-congestion cycles
microscopicparameters:
sec-by-sec vehicleoperation data
Transportation Models / Data Modal Emission Model
macroscopicparameters:
e.g., avg. speed
mesoscopicparameters:
e.g., vel, acc,v/c by facility
regionaltransportation
models
facility-basedtransportation models;
modal activity distributions
microscopic transportation models;driving cycles
sec-by-sec emissionsfor vehicle(s)
total link emissionsfor facility-type
regionalemissions
(SCFs)
EmissionInventory
Mesoscale Integration of Transportation and Emission Models
International Vehicle Emissions (IVE) Model• project lead by Jim Lents, ISSRC (originally developed at CE-CERT)
• sponsored by U.S. EPA
• IVE Modeling Characteristics:
– low-cost, easy to use methodologies for developing key motor vehiclerelated data
– provide a sophisticated model that is flexible and easy to use
– adaptable to multiple international locations
– useful for analyzing policy decisions and vehicle growth impacts
– provides a broad range of criteria, toxic, and global warming pollutant data
• implemented for many international cities
IVE Model continued…
• uses a well-developed data collection methodology to supplement local data that can be completed in 2-3 weeks using about 12-15 participants
• computer based emissions model that allows consideration of local geographic information, fleet technologies, and driving patterns; graphical user interface in multiple languages
• estimates criteria pollutants, toxic pollutants, and global warming gases
Portable Emissions Measurement System (PEMS) is placed in the trunk of the test vehicle
On-Board Vehicle Emission Measurements
additional equipment (e.g., FTIR, PM instrumentation occupies the rear seat of the vehicle
emissions vs. emissions vs. fuel signaturefuel signature
Heavy Duty Vehicle Emission Measurements
0102030405060708090
100
7:00 7:03 7:06 7:09 7:12 7:14 7:17 7:20
Time [hr:min]
Velo
city
[km
/hr]
Highway Arterial Residential
Vehicle Activity Data Collection: using GPS
data loggers
Measuring Vehicle Starts• must consider start-up and running emissions
• all methods are now modeling start-up emissions:– 10 bins: (¼, ½, 1, 2, 3, 4, 6, 8, 12, 18 hours engine off)– start-up emission factor set separately for each vehicle category– start-patterns determined through surveys and instrumentation
Designed and built by Global Sustainable Systems Research
Voltage monitored in cigarette lighter.
VOCE Unit records second by second voltage that is used to determine driving times and start-up information.
Additional Vehicle Activity Data: Real-Time Traffic Data
• real-time traffic density, speed, and flow is become more readily available
• Example: California Traffic Performance Measurement System (PeMS)
• Real-Time data can be used measure congestion
Fleet Composition
video taping of traffic
• can use local vehicle registration database
• can use on-road observations• parking lot surveys
parking lot survey
driving data collection
Istanbul: Istanbul: Vehicle Activity Study: Vehicle Activity Study: Precursor to Mobile Source Emissions InventoryPrecursor to Mobile Source Emissions Inventory
October 31 October 31 –– November 17, 2006November 17, 2006
Five Study Components:Five Study Components:Driving behavior in Istanbul (via GPS)Driving behavior in Istanbul (via GPS)StartStart--patterns of vehicles (via VOCE)patterns of vehicles (via VOCE)General vehicle distribution (via Video)General vehicle distribution (via Video)Specific technology distribution (via Surveys)Specific technology distribution (via Surveys)Targeted emission measurements (PEMS)Targeted emission measurements (PEMS)
Create mobileCreate mobile--source emissions inventory for source emissions inventory for Istanbul, TurkeyIstanbul, TurkeyCalibrate International Vehicle Emissions (IVE) Calibrate International Vehicle Emissions (IVE) model for Istanbul scenario analysismodel for Istanbul scenario analysis
Overall Project GoalsOverall Project Goals
Driving behavior in IstanbulDriving behavior in Istanbul
GPS data from GPS data from passenger cars, taxis, passenger cars, taxis, buses and trucksbuses and trucksSecondSecond--byby--second second data collection during data collection during 14 days on different 14 days on different areas and streetsareas and streetsUTC time, latitude, UTC time, latitude, longitude, altitude, longitude, altitude, speed, #satellitesspeed, #satellites
Driving Behavior Driving Behavior –– Areas of StudyAreas of Study
AA
BB
CC
DD
EE
A A –– Europe lower Europe lower incomeincomeB B –– Europe higher Europe higher incomeincomeC C –– Europe commercialEurope commercialD D –– Asia higher incomeAsia higher income
A: EuropeanA: European--side lower income areaside lower income areaB: EuropeanB: European--side higher income areaside higher income areaC: EuropeanC: European--side commercial areaside commercial areaD: AsianD: Asian--side higher income areaside higher income areaE: AsianE: Asian--side lower income areaside lower income area
Time Low Income Area Commercial Area High Income Area
Vehicle Activity Vehicle Activity –– Area A RoutesArea A Routes
Vehicle Activity Vehicle Activity –– Areas B & C RoutesAreas B & C Routes
Vehicle Activity Vehicle Activity –– Areas D & E RoutesAreas D & E Routes
Vehicle Activity:Vehicle Activity:Buses, Trucks and TaxiBuses, Trucks and Taxi’’ss
Riders With GPS On BusesRiders With GPS On BusesGPS Placed In Working TrucksGPS Placed In Working TrucksGPS Placed In Working Taxis GPS Placed In Working Taxis Vehicles operated in metropolitan areaVehicles operated in metropolitan area
Trucks:Trucks: Heavy duty truck data collected: 4500 seconds Heavy duty truck data collected: 4500 seconds (highway/arterial)(highway/arterial)
Start Patterns of VehiclesStart Patterns of VehiclesVVehicle ehicle OOperating perating CCharacteristics haracteristics EEnunciators (VOCE) Units installed nunciators (VOCE) Units installed on 89 passenger vehicles and on 89 passenger vehicles and truckstrucks77--day measurement period; various day measurement period; various installation daysinstallation daysmaintain log of vehicles using VOCEmaintain log of vehicles using VOCEVehicle types:Vehicle types:
Private cars: different groupsPrivate cars: different groupsCommercial carsCommercial carsGovernmental carsGovernmental carsCommercial trucksCommercial trucks 0
Transportation: Energy and Emissions• Increasing concern to stabilize greenhouse gases to below
levels emitted today (while still meeting energy needs)
• Transportation accounts for 33% of U.S. CO2 emissions
• 80% of transportation CO2 comes from cars and trucks
• Major emphasis is on cleaner, more efficient vehicles:• making vehicles lighter (and smaller) while maintaining safety
• improving powertrain efficiency
• developing alternative technologies (e.g.,hybrids, fuel-cell vehicles)
• Focus has also been placed on alternative fuels:• biofuels (cellulosic ethanol)
• synthetic fuels
• Increasing realization that we must also reduce VMT
• Savings can also be gained by improving efficiency
Roadway Congestion: impacts on energy and emissions
• Texas Transportation Institute Annual Mobility Study:• http://mobility.tamu.edu/ums• congestion has grown everywhere in areas of all sizes• congestion occurs during longer portions of the day and delays more travelers
and goods than ever before• billions of gallons of fuel are wasted every year, more emissions
“slow speeds caused by heavy traffic and/or narrow roadways due to construction, incidents, or too few lanes for the demand”
General Solutions to Roadway Congestion• Manage Supply:
• build more lanes to increase roadway capacity• build more infrastructure for alternative modes (bike, rail, transit)
shown to be more cost effective (Lipman, 2006)• improve system operations (e.g., respond quickly to incidents)• implement intelligent transportation system techniques
• Manage Demand:• implement pricing mechanisms to limit use of resources• provide greater range of alternative modes• allow for alternative work locations and schedules• have employers provide travel support programs
• Manage Land Use:• implement better urban design• provide for mixed use development of land• increase housing and industrial density• allow for innovative planning and zoning• implement some type of growth management
• Anytime congestion brings average vehicle speed below 45 mph (for a freeway scenario), there is a net negative fuel consumption and emissions impact; vehicles are spending more time on the road and as a result fuel economy is worse and total emissions is greater
• If congestion brings average speed down from a freeflow speed of around 65 mph to a slower 45 - 50 mph, then congestion is actually helping improve fuel consumption and emissions
• If relieving the congestion such that the average traffic speed increases back to the freeflow state, fuel consumption and emissions increases
• If the real-world stop-and-go velocity pattern of vehicles were somehow smoothed out where average speed was preserved, then significant fuel consumption and emissions savings could be achieved
• similar (but more complex) for arterial and residential roads
• fuel/emissions congestion effects are more pronounced with heavy-duty trucks (lower power-to-weight ratios)
““Dynamic EcoDynamic Eco--DrivingDriving””EcoEco--Driving Advice with Dynamic FeedbackDriving Advice with Dynamic Feedback
•• Static advice, for example:Static advice, for example:•• Shift up as soon as possibleShift up as soon as possible•• Maintain a steady speedMaintain a steady speed•• Anticipate traffic flowAnticipate traffic flow•• Accelerate smoothlyAccelerate smoothly•• Decelerate softlyDecelerate softly•• Check the tire pressure frequentlyCheck the tire pressure frequently
Travel Behaviors - Trip degeneration - Trip chaining - Alternative destinations - Mode shift - Alternative routes
Reduced # of trips; reduced VMT
Smoother drive; less unnecessa- ry idling
Reduced fuel usage; reduced GHG and other
pollutant emissions;
reduced # of accidents
Tools Feedback Changes in Behaviors Results Outcomes
System Architecture of Dynamic Eco-Driving
Traffic Management
Center
Traffic Management
Center
Traffic Management
Center
Embedded Road Sensor Data
Traffic Performance Measurement
System (PeMS)Internet
Wireless Communications
Provider
Vehicle Data
Instrumented Vehicles
System Server
GPRS WAAS-DGPS
DSRC local RF
transceiver
For field experiment of dynamic ECO-
Driving system in the Los Angeles Metro
Area
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7 8 9 10 1
Distance Traveled, km
Veh
icle
Spe
ed, k
m/h
Non-ISA ISA ISA Maximum Recommended Speed
1 12 13 14 15 16 17 18 19 20 21 22
Distance Traveled, km
EcoEco--Driving Field Experiments: Preliminary ResultsDriving Field Experiments: Preliminary Results
same travel time results:same travel time results:
-13%15341766Fuel (g)
-37%3.976.28NOx (g)
-41%1.903.20HC (g)-48%50.4797.01CO (g)
-12%47815439CO2 (g)
DifferenceISANon-ISAEnergy/Emissions
-13%15341766Fuel (g)
-37%3.976.28NOx (g)
-41%1.903.20HC (g)-48%50.4797.01CO (g)
-12%47815439CO2 (g)
DifferenceISANon-ISAEnergy/Emissions
Speed-Acceleration Histograms for bothISA-equipped and non-ISA vehicles
Conceptual Displays of EcoConceptual Displays of Eco--Driving Device Driving Device with Dynamic Feedbackwith Dynamic Feedback
6.4
MPG
10.5
----------Current Trip Total---------
Distance (mi):
Travel time (min):
0 60
$3.75
Eco-Driving Score
1.26
------------Trip Summary------------
Fuel cost:
CO2 emission (kg):
0 10
-- During the Trip During the Trip -- -- At Trip End At Trip End --
Summary and Conclusions:
• congestion mitigation strategies that reduce severe congestion such that higher average traffic speeds are achieved (e.g. ramp metering, incident management);
• speed management techniques that can bring down excessive speeds to more moderate speeds of approximately 60 mph (e.g. enforcement, ISA); and
• traffic flow smoothing techniques that can suppress shock waves, and thus, reduce the number of acceleration and deceleration events (e.g. variable speed limits, ISA)
• Traffic congestion has a significant impact on fuel consumption and emissions
• Improved traffic conditions can be accomplished through:
• CO2: Each can save 5 – 12%, can be additive for greater savings
• EFNav: Extending work to arterials and surface streets; road grade