1. Developing a Real-Time Energy and Environmental Monitoring System
Prepared By
Kyoungho Ahn (VTTI)
Sangjun Park (Chosun University, South Korea)
Hesham Rakha (VTTI)
December 11th 2014

2. Advancing Transportation Through Innovation
Overview
Objective
Developing a real-time energy and environmental monitoring system using real-time traffic data
The developed model can be used as an application for Dynamic Low Emissions Zones
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3. Advancing Transportation Through Innovation
Background
Surface transportation has a significant impact on the environment:
Transport sector in the US accounts for 30% of GHG emissions and 70% of US petroleum consumption.
Surface vehicles represent almost 84% of the transport sector GHG in the US.
A Key program of MAP21
Environmental Sustainability – To enhance the performance of the transportation system while protecting and enhancing the natural environment.
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4. Advancing Transportation Through Innovation
Poll # 1: Have you ever heard about connected vehicle (CV) technologies and research programs?
Never
I have heard but not sure about them
I know the concepts
I am a specialist on CV research
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Advancing Transportation Through Innovation

5. Advancing Transportation Through Innovation
AERIS Program
A connected vehicle research program
vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to improve fuel efficiency and air quality
The Applications for the Environment: Real-time Information Synthesis (AERIS) Program was initiated by the U.S. Department of Transportation
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6. Poll # 2: Have you ever heard about the AERIS program?
Never
I have heard but not sure about them
I know the concepts
I am working on the AERIS research program
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Advancing Transportation Through Innovation

7. Advancing Transportation Through Innovation
AERIS Program
Eco-Signal Operations
Dynamic Eco-Lanes
Dynamic Low Emissions Zones
Eco-Traveler Information
Support for Alternative Fuel Vehicle
Eco-Integrated Corridor Management (Eco-ICM)
Dynamic Low Emissions Zones
Geographically defined areas that seek to incentive “green transportation choices” or restrict specific categories of high-polluting vehicles from entering the zone to improve the air quality within the geographic area.
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8. Advancing Transportation Through Innovation
Poll # 3: Have you ever used any vehicle energy (fuel consumption) and emission models?
Never
Yes. I have used the models but many years ago
Yes. I have used the models recently (within 3 years)
Yes. I have used models and also have developed an energy and emission model
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9. Energy & Environmental Modeling
Models are derived from a relationship between fuel consumption rate and measurements of various explanatory variables including vehicle power, force, speed, acceleration, and other factors.
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10. Advancing Transportation Through Innovation
Poll # 4: Do you know the difference between macroscopic and microscopic energy and emission models?
Yes No
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11. Energy & Environmental Modeling
Macroscopic Models
EPA’s MOBILE models
EPA’s MOVES model
Microscopic Models
VT-Micro
VT-CPFM
CMEM
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12. Advancing Transportation Through Innovation
MOVES Model
MOtor Vehicle Emission Simulator
State-of-the-art modeling framework
Designed to allow easier incorporation of large amounts of in-use data from a variety of sources
Replaced MOBILE for on-road vehicle emissions
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13. Advancing Transportation Through Innovation
MOVES Model
Developed by U.S. EPA
MOVES can be used for transportation conformity in air quality nonattainment areas required by federal regulations
MOVES has integrated the most up-to-date data (Recently updated with 2014 database)
Macroscopic and Microscopic analyses
It provides particulate matter (PM) and greenhouse gases (GHG) emission rates
MOVES has a graphical user interface (GUI), more user-friendly than the text based programs.
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14. Advancing Transportation Through Innovation
Pollutants in MOVES
HC (THC, NMHC, NMOG, TOG, VOC) CO
NOx (NO, NO2)
NH3
SO2
PM10,2.5 (OC, EC, sulfate, brake, tire)
GHG (CO2, CH4, N2O)
Toxics (e.g., benzene, naphthalene, formaldehyde, etc.)
Energy (total, petroleum, fossil)
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15. Advancing Transportation Through Innovation
Vehicle Types in MOVES
Passenger Car
Passenger Truck
Motorcycle
Light Commercial Truck
Intercity Bus
Transit Bus
School Bus
Refuse Truck
Single Unit Short-haul Truck
Single Unit Long-haul Truck
Motor Home
Combination Short-haul Truck
Combination Long-haul Truck
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16. Advancing Transportation Through Innovation
MOVES Model
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17. Advancing Transportation Through Innovation
MOVES Model
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18. Advancing Transportation Through Innovation
MOVES Emission Rates
MOVES includes a different emission rate for each combination of…
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19. Advancing Transportation Through Innovation
MOVES Input and Output
To run MOVES,
A run specification
Input databases (county and project scales)
MOVES creates an output database
Database can be viewed and manipulated in MySQL; information can also be exported to another program (e.g., Excel)
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20. MOVES Three Scales of Analysis
National level
County level
Project level
An individual transportation project (e.g., a highway, intersection, or transit project)
User provides project-specific information through the Project Data Manager
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21. Project Level Analysis
Link level modeling of specific transportation projects - Highways, intersections, interchanges, transit projects, parking lots
Required for quantitative hot-spot analyses for conformity
User must enter project-specific data, via the Project Data Manager (PDM), for the input database
Vehicle operational Input options – Average speed, OP-Mode Distribution, and Driving Schedule
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22. Project Level Analysis
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23. Why we don’t want to use MOVES for this study
Complexity of Input database
Computation time
Output formats
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24. Advancing Transportation Through Innovation
Operational Modes
Ranges of vehicle speed and vehicle specific power (VSP).
Operational Mode Bin Definitions for Running Energy Consumption
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25. Vehicle Specific Power (VSP)
Instantaneous VSP by using
A is the rolling term, B is the rotating term, C is the drag term
Vehicle-specific coefficients (13 vehicle types) available in the “SourceUseType” table in MOVES database
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26. Energy (or Emission) Estimation
Lookup emissionrate table from Database
Includes: sourceBinID, polProcessID, opModeID, meanBaseRate
sourceBinID - is a 19-digit numeric label, of the form “1fftteeyysssswwww00,”
ff represents the fueltypeID,
tt represents the engTechID,
ee represents the regClassID,
yy represents the shortModYrGrpID,
ssss represents the engSizeID,
wwww represents the weightClassID
polProcessID - pollutantID
opModeID - operational mode
meanBaseRate – base energy consumption rate
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27. Energy (or Emission) Estimation
Example
Model year 2014, light duty vehicle using gasoline with conventional interval combustion
85 km/h and 0 km/h/s
Generate VSP = 7.6 and Op_mode = 35
Lookup table from MOVES database
92721 KJ/hr = 55 KJ/s = mpg
Used Default Values for other adjustment factors
FuelAdjustment, TempAdjustment, IMOBDAdjustment, ACAdjustment
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28. Advancing Transportation Through Innovation
Model Comparison
Test Driving Schedule - EPA highway fuel economy test (HWFET) cycle
Test Vehicle - A 1995 passenger car, Gasoline engine.
Compared to VT-micro (LDV1) and CMEM (Category-11)
39% of OpModes were categorized in OpMode bin-1, which represents braking and deceleration maneuvers.
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29. EPA highway fuel economy test (HWFET) cycle
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30. Operational Mode Distribution
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31. Advancing Transportation Through Innovation
Simulation Results
Results
MOVES: liters,
VT-Micro: liters,
CMEM: liters
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32. Advancing Transportation Through Innovation
Next Step
Develops MATLAB interface using 2014 MOVES database
Testing with OBE and RSE field data
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33. Kyoungho Ahn