Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions

2003 ◽  
Vol 30 (6) ◽  
pp. 1010-1021 ◽  
Author(s):  
Hesham Rakha ◽  
Kyoungho Ahn ◽  
Antonio Trani
Keyword(s):  
Fuel Consumption ◽  
Environmental Protection Agency ◽  
Field Data ◽  
Vehicle Emissions ◽  
National Laboratory ◽  
Oxides Of Nitrogen ◽  
Micro Model ◽  
Oak Ridge ◽  
Light Duty ◽  
Light Duty Vehicle

The paper compares the MOBILE5a, MOBILE6, Virginia Tech microscopic energy and emission model (VT-Micro), and comprehensive modal emissions model (CMEM) models for estimating hot-stabilized, light-duty vehicle emissions. Specifically, Oak Ridge National Laboratory (ORNL) and Environmental Protection Agency (EPA) laboratory fuel consumption and emission databases are used for model comparisons. The comparisons demonstrate that CMEM exhibits some abnormal behaviors when compared with the ORNL data, EPA data, and the VT-Micro model estimates. Specifically, carbon monoxide (CO) emissions exhibit abrupt changes at low speeds and high acceleration levels and constant emissions at negative acceleration levels. Furthermore, oxides of nitrogen (NOx) emissions exhibit abrupt drops at high engine loads. In addition, the study demonstrates that MOBILE5a emission estimates compare poorly with EPA field data, while MOBILE6 model estimates show consistency with EPA field data and VT-Micro model estimates over various driving cycles. The VT-Micro model appears to be accurate in estimating hot-stabilized, light-duty, normal vehicle tailpipe emissions. Specifically, the emission estimates of the VT-Micro and MOBILE6 models are consistent in trends with laboratory measurements. Furthermore, the VT-Micro and MOBILE6 models accurately capture emission increases for aggressive acceleration drive cycles in comparison with other drive cycles.Key words: transportation energy, transportation environmental impacts, VT-Micro Model, CMEM, MOBILE5, MOBILE6, fuel consumption models, emission models.

Estimating Light-Duty Vehicle Emission Factors using Random Forest Regression Model with Pavement Roughness

2020 ◽  
Vol 2674 (8) ◽  
pp. 37-52
Author(s):  
Fengxiang Qiao ◽  
Mahreen Nabi ◽  
Qing Li ◽  
Lei Yu
Keyword(s):  
Random Forest ◽  
Regression Model ◽  
Fuel Consumption ◽  
Vehicle Emissions ◽  
Emission Factors ◽  
Specific Power ◽  
Oxides Of Nitrogen ◽  
Random Forest Regression ◽  
Pavement Roughness ◽  
Light Duty Vehicle

Pavement roughness would affect the running of vehicle movement, and thus possibly impact fuel consumption and vehicle emissions, the numerical relationships and analytical steps of which are, however, not yet well studied. The major objective of this paper is to quantify vehicular emission factors—hydrocarbons (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2)—and fuel consumption as a function of pavement roughness (the International Roughness Index [IRI]) and other factors. Within each operating mode identification (OMID) bins of vehicle operational status, a random forest regression model (RFRM) was identified to estimate emission factors and fuel consumption. The field test data, with a total length of 1,067.41 mi driving and 323,075 data pairs from one test vehicle, were used to train and validate models. The portable emissions measurement system (PEMS) and a smartphone application for IRI were employed for the tests in Texas, U.S., roadways. Results show that the optimum roughness conditions for lower emissions and fuel consumption are in categories B and C with moderate roughness. The root-mean-square error (RMSE) during training, testing, and validation processes of the RFRM are within 6.4%, implying a good fit of resulted models. IRI has the most OMID bins as number one predictor, followed by vehicle specific power (VSP) and speed. Through separated modeling for each OMID, the impacts of IRI are successfully grasped. It is recommended conducting more field measurements with more vehicle types. This would help with possible incorporation of vehicle emissions, fuel consumption, and other environmental factors into the pavement design, maintenance, and retrofitting process.

scholarly journals Comparison of TRANSIMS' Light Duty Vehicle Emissions with On-Road Emission Measurements

2010 ◽  
Author(s):  
Mansoureh Jeihani ◽  
Kyoungho Ahn ◽  
Antoine G. Hobeika ◽  
Hanif D. Sherali ◽  
Hesham A. Rakha
Keyword(s):  
Field Data ◽  
Vehicle Emissions ◽  
State Of The Art ◽  
Simulation System ◽  
Heavy Duty ◽  
Light Duty ◽  
Emission Models ◽  
Light Duty Vehicle ◽  
Light Duty Vehicles ◽  
Road Emission

The Transportation Analysis and Simulation System, TRANSIMS, contains a vehicle emissions module that estimates tailpipe emissions for light and heavy-duty vehicles and evaporative emissions for light-duty vehicles. This paper describes and validates the TRANSIMS emission module and compares its emission estimates to on-road emission-measurements and other state-of-the-art emission models. The trend of the emissions estimated in thirteen different runs in each model are compared. The results indicate that the TRANSIMS model provides consistent trends of estimated carbon monoxide (CO) and hydrocarbons (HC) with field data trends and inconsistent trends of estimated nitrogen lxides (NOx). However, the magnitude of the emission estimated in TRANSIMS is closer to the field data than for other models.

Low-Level Automated Light-Duty Vehicle Technologies Provide Opportunities to Reduce Fuel Consumption

2018 ◽  
Vol 2672 (24) ◽  
pp. 60-74 ◽  
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Constantine Samaras ◽  
Chris Hendrickson
Keyword(s):  
Fuel Consumption ◽  
Aerodynamic Force ◽  
The United States ◽  
Route Guidance ◽  
Conventional Vehicle ◽  
Automated Vehicle ◽  
Fuel Savings ◽  
Light Duty ◽  
Vehicle Systems ◽  
Light Duty Vehicle

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

scholarly journals High Emitter Light Duty Vehicle Contributions to On-road Mobile Emissions in 2018 and 2030

2015 ◽  
Vol 2 (1) ◽  
pp. 47 ◽  
Author(s):  
Susan Collet ◽  
Toru Kidokoro ◽  
Yukio Kinugasa ◽  
Prakash Karamchandani ◽  
Allison DenBleyker
Keyword(s):  
Air Quality ◽  
Environmental Protection Agency ◽  
Protection Agency ◽  
Vehicle Miles Traveled ◽  
Improvement Strategy ◽  
Quality Improvement Strategy ◽  
Light Duty ◽  
Air Quality Improvement ◽  
Light Duty Vehicle ◽  
Tier 3

Quantifying the proportion of normal- and high-emitting vehicles and their emissions is vital for creating an air quality improvement strategy for emission reduction policies. This paper includes the California LEV III and United States Environmental Protection Agency Tier 3 vehicle regulations in this projection of high emitter quantification for 2018 and 2030. Results show high emitting vehicles account for up to 6% of vehicle population and vehicle miles traveled. Yet, they will contribute to over 75% of exhaust and 66% of evaporative emissions. As these high emitting vehicles are gradually retired from service and are removed from the roads, the overall effect on air quality from vehicle emissions will be reduced.

scholarly journals Model-based estimation of light-duty vehicle fuel economy at high altitude

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401988625 ◽  
Author(s):  
Lijun Hao ◽  
Chunjie Wang ◽  
Hang Yin ◽  
Chunxiao Hao ◽  
Haohao Wang ◽  
...  
Keyword(s):  
Internal Combustion Engine ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Specific Fuel Consumption ◽  
Brake Specific Fuel Consumption ◽  
Engine Model ◽  
Light Duty ◽  
Light Duty Vehicle

In order to estimate the light-duty vehicle fuel economy at high-altitude areas, the coast-down tests of a passenger car on level road were conducted at different elevations, and the coast-down resistance coefficients were calculated. Furthermore, a fuel economy model for a light-duty vehicle adopting backward simulation method was developed, and it mainly consists of vehicle dynamic model, internal combustion engine model, transmission model, and differential model. The internal combustion engine model consists of the brake-specific fuel consumption maps as functions of engine torque and engine speed, and the brake-specific fuel consumption map near sea level was constructed based on engine experimental data, and the brake-specific fuel consumption maps at high altitudes were calculated by GT-Power Modeling of the internal combustion engine. The fuel consumption rate was calculated from the brake-specific fuel consumption maps and brake power and used to calculate the fuel economy of the light-duty vehicle. The model predicted fuel consumption data met well with the test results, and the model prediction errors are within 5%.

Guiding Model and Empirical Analysis of Vehicle Behavior at Signalized Intersections Based on Carbon Emissions

2012 ◽  
Vol 253-255 ◽  
pp. 1282-1288
Author(s):  
Yi Ling Zheng ◽  
Yuan Qi Wei ◽  
Ya Xing Xiao ◽  
Yu Feng
Keyword(s):  
Empirical Analysis ◽  
Working Conditions ◽  
Carbon Emissions ◽  
National Laboratory ◽  
Signalized Intersections ◽  
Emission Characteristics ◽  
Micro Model ◽  
Low Carbon ◽  
Oak Ridge ◽  
Working Plan

A model based on carbon emissions is established to guide vehicle behavior at signalized intersections. This model takes working conditions and emission characteristics of vehicles into account, uses VT-Micro model exploited by Oak Ridge National Laboratory to calculate emissions, and provides vehicles with the lowest-emission working plan to drive through intersections. This model is able to reducing carbon emissions at the source and it agrees with the development of low-carbon transportation.

Impact of Oxygenated Gasoline Use on California Light-Duty Vehicle Emissions

1996 ◽  
Vol 30 (2) ◽  
pp. 661-670 ◽  
Author(s):  
Thomas W. Kirchstetter ◽  
Brett C. Singer ◽  
Robert A. Harley ◽  
Gary R. Kendall ◽  
Waymond Chan
Keyword(s):  
Vehicle Emissions ◽  
Light Duty ◽  
Light Duty Vehicle
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