1. CEE6984: Special Topics – Transportation Sustainability
Energy and Emission Models

2. Course Instruction It is a pre-recorded class with narration.
Instructors:
Dr. Hesham Rakha
Dr. Kyoungho Ahn
Read the slide first and click the speaker icon to listen audio narration
Recommended to use earphone or headphone
CEE6984: Special Topics – Transportation Sustainability

3. Transportation Sustainability
The lecture series cover a wide range of transportation sustainability topics.
“Energy-Efficient Urban Transportation”
Vehicle fuel consumption and emission modeling
Vehicle engine basics and powertrain modeling
Traffic flow theory basics and vehicle car-following models
Fuel-efficient Intelligent Transportation Systems (ITS) and Connected Vehicle (CV) applications
CEE6984: Special Topics – Transportation Sustainability

4. Lecture Objectives
Understand the basic concept of vehicle energy and emission models
US EPA’s MOVES, VT-Micro, VT-CPFM models
Learn how to use energy and emission models using a simple case study
CEE6984: Special Topics – Transportation Sustainability

5. Overview:
The transportation sector consumes approximately 30% of the total energy.
mostly petroleum-based products including gasoline and diesel fuels.
In 2013, greenhouse gas emissions from transportation accounted for about 27% of total U.S. greenhouse gas (GHG) emissions
the second largest contributor of U.S. GHG emissions after the Electricity sector.
Greenhouse gas emissions from transportation have been increased by about 16% since 1990.
CEE6984: Special Topics – Transportation Sustainability

6. Overview:
Accurate estimation of fuel consumption and emission is one of most important tasks to improve the vehicle energy efficiency and cleaner environment.
A recent study estimated that teaching consumers to eco-drive can improve actual fuel efficiency by an average of 17%.
A simple, accurate, and efficient fuel consumption and emission model is one of the key elements of a real-time eco-driving system (e.g. eco-routing system).
CEE6984: Special Topics – Transportation Sustainability

7. Overview:
Emission Inventory – a database that lists, by source, the amount of air pollutants discharged into the atmosphere of a community during a given time period (U.S. EPA)
Emission inventories are a mandatory component for the development of an effective air quality management process.
CEE6984: Special Topics – Transportation Sustainability

8. Overview: Three levels of modeling Macroscopic Mesoscopic Microscopic
MOVES (U.S. EPA), EMFAC (California Air Resources Board)
Mesoscopic
VT-Meso: Based on average vehicle speed, number of vehicle stops, and stopped delay.
Microscopic
Statistical models: VT-Micro
Physical models: Based on vehicle power usage
VT-CPFM, MOVES (project level), and CMEM.
CEE6984: Special Topics – Transportation Sustainability

9. Energy and Emission Models:
MOVES
VT-Micro model
VT-CPFM model
CEE6984: Special Topics – Transportation Sustainability

10. MOVES: Overview
The MOtor Vehicle Emission Simulator (MOVES) estimates emissions for mobile sources covering a wide range of pollutants and allows multiple scale analysis.
MOVES was approved for
New State Implementation Plans(SIPs)
Use MOVES2014 now for any new SIPs
If significant work on a SIP with MOVES2010 has already been completed, you can continue
Transportation conformity analyses, including
Regional conformity analysis
Project-level conformity analysis (PM and CO Hotspots)
CEE6984: Special Topics – Transportation Sustainability
CEE5604

11. MOVES: Emission Process
Running
Start
Extended Idle (trucks “hoteling” under load)
Evaporative
Refueling
Crankcase
Tire Wear
Brake Wear
CEE6984: Special Topics – Transportation Sustainability

12. MOVES: Pollutants HC (THC, NMHC, NMOG, TOG, VOC) CO NOx NH3 SO2
PM10,2.5
GHG (CO2, CH4, N2O)
Toxics
Energy (total, petroleum, fossil)
CEE6984: Special Topics – Transportation Sustainability

13. MOVES: Fuel and Vehicle Types
Fuel types
Compressed Natural Gas, Diesel, Ethanol (E-85), Liquefied Petroleum Gas, and Gasoline.
Vehicle types
Passenger Car, Passenger Truck, Motorcycle, Light Commercial Truck, Intercity Bus, Transit Bus, School Bus, Refuse Truck, Single Unit Short-haul and Long-haul Trucks, Motor Home, Combination Short-haul and Long-haul Trucks
CEE6984: Special Topics – Transportation Sustainability

14. MOVES: Emission Rates
MOVES estimates a different emission rate for each combination of…
CEE6984: Special Topics – Transportation Sustainability

15. MOVES: Graphical User Interface (GUI)
CEE6984: Special Topics – Transportation Sustainability

16. MOVES: Graphical User Interface (GUI)
CEE6984: Special Topics – Transportation Sustainability

17. MOVES: Input and Output
To run MOVES,
A run specification file
Input database
MOVES creates a results as an output database
MySQL is typically used to view the results ( Database) and manipulate the results; the information can also be exported to another program (e.g., Excel)
CEE6984: Special Topics – Transportation Sustainability

18. MOVES: Three Scales of Analysis
National level
Entire nation
One or more states
One or more counties
County level
One county
A multi-county area
Project level
An individual transportation project (e.g., a highway, intersection, or transit project)
CEE6984: Special Topics – Transportation Sustainability

19. MOVES: Project Level Analysis
CEE6984: Special Topics – Transportation Sustainability

20. MOVES: Pros and Cons Pros Cons Comprehensive energy and emission model
all vehicle types and pollutant types
Can evaluate various impacts (vehicle age distribution, fuel types, temperature, I/M program, etc.)
National and County level estimation
Cons
Complexity of Input database
Computation time
Output formats
Not suitable for complex transportation projects
Can’t be implemented into microscopic traffic simulation models
CEE6984: Special Topics – Transportation Sustainability

21. MOVES: Further Information
MOVES - Training Sessions
Webinars
Hands-on Training Course
Project level training for PM hotspot analysis
CEE6984: Special Topics – Transportation Sustainability

22. VT-Micro: Overview
VT-Micro uses speed and acceleration values as input variables and includes 32 parameters that are calibrated for different vehicles [Ahn et al. (2002) and Rakha et al. (2004)]
Exponential transformation ensures positive MOE estimates
The model estimates second-by-second fuel, HC, CO, NOx, and CO2
Where:
L = Regression constant (a0)
M = Regression constant (a<0)
u = Instantaneous speed
a = Instantaneous acceleration
CEE6984: Special Topics – Transportation Sustainability

23. VT-Micro: Data and Vehicle Types
Chassis dynamometer data (ORNL and EPA 97 vehicles)
Vehicle types
ORNL vehicles
EPA Light duty vehicle (LDV): LDVs 1 to 5
EPA Light duty truck (LDT): LDTs 1 to 2
EPA High emitters (HE): HEs 1 to 4
Heavy Duty Truck
All data were collected at FTP conditions using the standard vehicle certification fuel.
CEE6984: Special Topics – Transportation Sustainability

24. VT-Micro: Model Validation
CEE6984: Special Topics – Transportation Sustainability

25. VT-Micro: Boundary condition
Vehicles have their maximum operating boundary.
Vehicle fuel consumption and emission data were measured in a laboratory within the vehicle’s feasible vehicle speed and acceleration envelope.
The VT-Micro model should be used within the boundaries used in the model calibration procedure.
This performance boundary is extremely important when the energy and emission models are used with microscopic traffic flow models.
CEE6984: Special Topics – Transportation Sustainability

26. VT-Micro: Boundary condition
A typical speed and acceleration performance boundary
CEE6984: Special Topics – Transportation Sustainability

27. VT-Micro: Conclusion
Current state-of-the-art models estimate vehicle fuel consumption and emissions based on aggregated variables (average speed and distance).
Can’t capture transient changes in a vehicle’s speed and acceleration
Stop and go driving with average speed of 50 mph and constant speed driving with average speed of 50 mph can generate significantly different fuel consumption and emission rates: Average speed model will generate the same results
VT-Micro model
Can capture transient changes in a vehicle’s speed and acceleration
Can evaluate the energy and environmental impact on various transportation projects and can be easy implemented into eco-driving applications and traffic simulation models.
CEE6984: Special Topics – Transportation Sustainability

28. VT-CPFM: Overview
Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) was developed to be implemented into vehicle eco-driving applications and microscopic traffic simulation models.
The model is calibrated using:
the EPA fuel economy data (city and highway mpg)
publically available vehicle specific data without using field collected data.
CEE6984: Special Topics – Transportation Sustainability

29. VT-CPFM: Overview On-board Diagnostic (OBD) data demonstrate:
For positive power conditions the fuel consumption function is convex
Fuel consumption could be modeled using a second-order polynomial model
CEE6984: Special Topics – Transportation Sustainability

30. VT-CPFM: Overview
A second-order model provides a good model accuracy and applicability.
VT-CPFM 1 model
VT-CPFM 2 model
Where α0, α1, α2 and β0, β1, and β2 are vehicle-specific model constants that are calibrated for each vehicle and ωidle is the engine idling speed (rpm).
CEE6984: Special Topics – Transportation Sustainability

31. VT-CPFM: Overview VT-CPFM 1 model utilizes instantaneous vehicle power
The power is a function of the vehicle speed and acceleration level and roadway grade
Input variables can be measured directly using non-engine instrumentation, e.g. a Global Positioning System (GPS).
VT-CPFM 2 model requires engine data in addition to vehicle power
Typical fuel consumption models can’t estimate the impacts of gear-shifting.
Vehicle’s gear shifting affects the engine speed – results in changes in the fuel consumption rates.
This model can be used to estimate the impacts of gear-shifting strategies and can be used with the vehicle powertrain modeling.
Both models are ideal for implementation within microscopic traffic simulation software and eco-driving applications.
CEE6984: Special Topics – Transportation Sustainability

32. VT-CPFM: LA92 Driving Cycle
CEE6984: Special Topics – Transportation Sustainability

33. Case Study: VT-Micro model
Estimate fuel consumption and emissions for the city test (LA04) cycle with VT-Micro model (LDV3)
Use VT-Micro_Model v1.xlsx in the assignment folder
CEE6984: Special Topics – Transportation Sustainability

34. Case Study: VT-Micro model
The city test cycle
Also referred as the EPA Urban Dynamometer Driving Schedule (UDDS) or LA04 cycle
Length: 23 minutes
Distance: 11.9 km (or 7.5 mile)
Average speed: 31.2 km/h (or 19.5 mph)
CEE6984: Special Topics – Transportation Sustainability

35. Case Study: VT-Micro model
CEE6984: Special Topics – Transportation Sustainability

36. Case Study: VT-Micro model (VT-Micro_Model V1.xlsx)
CEE6984: Special Topics – Transportation Sustainability

37. Case Study: VT-Micro model (VT-Micro_Model V1.xlsx)
CEE6984: Special Topics – Transportation Sustainability

38. Case Study: VT-Micro model
CEE6984: Special Topics – Transportation Sustainability

39. Case Study: VT-Micro model
CEE6984: Special Topics – Transportation Sustainability

40. Case Study: VT-Micro model
For the city test cycle, The VT-Micro model predicts:
10.72 km/l or mpg
Fuel HC CO
NOx
CO2
Total
1.113 L
0.229 g
4.954 g
1.017 g
3.579 kg
Rates
0.093 l/km
0.019 g/km
0.415 g/km
0.085 g/km
0.300 kg/km
CEE6984: Special Topics – Transportation Sustainability

41. Summary: Three fuel consumption and emission models
Case study using VT-Micro model
Question – Please to Kyoungho Ahn
CEE6984: Special Topics – Transportation Sustainability