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

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 9/22/2018 Advancing Transportation Through Innovation

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. 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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. 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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. 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation Poll # 4: Do you know the difference between macroscopic and microscopic energy and emission models? Yes No 9/22/2018 Advancing Transportation Through Innovation

Energy & Environmental Modeling Macroscopic Models EPA’s MOBILE models EPA’s MOVES model Microscopic Models VT-Micro VT-CPFM CMEM 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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. 9/22/2018 Advancing Transportation Through Innovation

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) 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation MOVES Model 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation MOVES Model 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation MOVES Emission Rates MOVES includes a different emission rate for each combination of… 9/22/2018 Advancing Transportation Through Innovation

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) 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

Project Level Analysis 9/22/2018 Advancing Transportation Through Innovation

Why we don’t want to use MOVES for this study Complexity of Input database Computation time Output formats 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation Operational Modes Ranges of vehicle speed and vehicle specific power (VSP). Operational Mode Bin Definitions for Running Energy Consumption 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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 9/22/2018 Advancing Transportation Through Innovation

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 = 33.44 mpg Used Default Values for other adjustment factors FuelAdjustment, TempAdjustment, IMOBDAdjustment, ACAdjustment 9/22/2018 Advancing Transportation Through Innovation

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. 9/22/2018 Advancing Transportation Through Innovation

EPA highway fuel economy test (HWFET) cycle 9/22/2018 Advancing Transportation Through Innovation

Operational Mode Distribution 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation Simulation Results Results MOVES: 0.938 liters, VT-Micro: 1.072 liters, CMEM: 0.989 liters 9/22/2018 Advancing Transportation Through Innovation

Advancing Transportation Through Innovation Next Step Develops MATLAB interface using 2014 MOVES database Testing with OBE and RSE field data 9/22/2018 Advancing Transportation Through Innovation

Kyoungho Ahn