Development of Local Emissions Rate Model for Light-Duty Gasoline Vehicles: Beijing Field Data and Patterns of Emissions Rates in EPA Simulator

Author(s):  
Chenxu Li ◽  
Lei Yu ◽  
Weinan He ◽  
Ying Cheng ◽  
Guohua Song

A local emissions rate (ER) model is an important tool that is often combined with vehicle activity data in assessing the effect of traffic control strategies on emissions. Such a model is especially critical in developing countries where local emissions data are either unavailable or limited. This study sought to develop a local ER model for light-duty gasoline vehicles (LDGVs) based on limited emissions testing data from Beijing, emissions factors in the China vehicle emission model, and the regular patterns of ERs in the Motor Vehicle Emission Simulator (MOVES) program. To this end, the research team first analyzed the characteristics of vehicle emissions on the basis of field data collected in Beijing. Then the authors summarized the regular pattern of ERs for LDGVs embedded in the MOVES model and examined consistency of normalized ERs derived from Beijing and the MOVES program. The normalized mean square error was used to evaluate the level of consistency. When consistency was sufficiently high, the regular pattern of ERs in the MOVES program was used to fill the missing field emissions data. Development of the model involved four essential elements: ( a) data-driven ERs, ( b) a supplement for high-power operating modes, ( c) modeling ERs of zero-mile-level emissions, and ( d) development of a deterioration model of ERs. On the basis of the proposed model, a local database of ERs for LDGVs was established and applied to assess the emissions benefit of electronic toll collection lanes.

Author(s):  
Tongchuan Wei ◽  
H. Christopher Frey

A vehicle specific power (VSP) modal model and the MOtor Vehicle Emission Simulator (MOVES) Operating Mode (OpMode) model have been used to evaluate and quantify the fuel use and emission rates (FUERs) for on-road vehicles. These models bin second-by-second FUERs based on factors such as VSP, speed, and others. The validity of binning approaches depends on their precision and accuracy in predicting variability in cycle-average emission rates (CAERs). The objective is to quantify the precision and accuracy of the two modeling methods. Since 2008, North Carolina State University has used portable emission measurement systems to measure tailpipe emission rates for 214 light duty gasoline vehicles on 1,677 driving cycles, including 839 outbound cycles and 838 inbound cycles on the same routes. These vehicles represent a wide range of characteristics and emission standards. For each vehicle, the models were calibrated based on outbound cycles and were validated based on inbound cycles. The goodness-of-fit of the calibrated models was assessed using linear least squares regression without intercept between model-predicted versus empirical CAERs for individual vehicles. Based on model calibration and validation, the coefficients of determination ( R2) typically range from 0.60 to 0.97 depending on the vehicle group and pollutant, indicating moderate to high precision, with precision typically higher for higher-emitting vehicle groups. The slopes of parity plots for each vehicle group and all vehicles typically range from 0.90 to 1.10, indicating good accuracy. The two modeling approaches are similar to each other at the microscopic and macroscopic levels.


2015 ◽  
Vol 110 ◽  
pp. 103-110 ◽  
Author(s):  
Liang Qu ◽  
Mengliang Li ◽  
Dong Chen ◽  
Kaibo Lu ◽  
Taosheng Jin ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Haiwei Wang ◽  
Huiying Wen ◽  
Feng You ◽  
Jianmin Xu ◽  
Hailin Kui

In urban road traffic systems, roundabout is considered as one of the core traffic bottlenecks, which are also a core impact of vehicle emission and city environment. In this paper, we proposed a transport control and management method for solving traffic jam and reducing emission in roundabout. The platform of motor vehicle testing system and VSP-based emission model was established firstly. By using the topology chart of the roundabout and microsimulation software, we calculated the instantaneous emission rates of different vehicle and total vehicle emissions. We argued that Integration-Model, combing traffic simulation and vehicle emission, can be performed to calculate the instantaneous emission rates of different vehicle and total vehicle emissions at the roundabout. By contrasting the exhaust emissions result between no signal control and signal control in this area at the rush hour, it draws a conclusion that setting the optimizing signal control can effectively reduce the regional vehicle emission. The proposed approach has been submitted to a simulation and experiment that involved an environmental assessment in Satellite Square, a roundabout in medium city located in China. It has been verified that setting signal control with knowledge engineering and Integration-Model is a practical way for solving the traffic jams and environmental pollution.


Author(s):  
Zeyu Zhang ◽  
Guohua Song ◽  
Zhiqiang Zhai ◽  
Chenxu Li ◽  
Yizheng Wu

Vehicle-specific power (VSP) distributions, or operating mode (OpMode) distributions, are one of the most important parameters in VSP-based emission models, such as the motor vehicle emission simulator (MOVES) model. The collection of second-by-second vehicle activity data is required to develop facility- and speed-specific (FaSS) VSP distributions. This then raises the problem of how many trajectories are needed to develop FaSS VSP distributions for emission estimation. This study attempts to investigate the adaptive sample size for developing robust VSP distributions for emission estimations for light-duty vehicles. First, vehicle activity data are divided into trajectories and categorized into different trajectory pools. Then, the uncertainty of FaSS VSP distribution caused by sample size is analyzed. Further, the relationship between VSP distribution sample size and emission factor uncertainty is discussed. The case study indicates that error in developing FaSS VSP distributions decreases significantly with increased sample size. In different speed bins, the sample size required to develop robust FaSS VSP distributions and estimate emission factors is significantly different. In detail, in each speed bin, for a 90% confidence level, 30 trajectories (1,800 s) are enough to develop robust FaSS VSP distributions for light-duty vehicles with the root mean square errors (RMSEs) lower than 2%, which means errors in calculating fuel consumption and greenhouse gas (GHG) emissions are lower than 5%. However, 35 trajectories (2,100 s) are needed to estimate emissions of carbon monoxide (CO), nitrogen oxide (NOX), and hydrocarbons (HC) with an estimation error lower than 5%.


2015 ◽  
Vol 2503 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Kanok Boriboonsomsin ◽  
Guoyuan Wu ◽  
Peng Hao ◽  
Matthew Barth

Vehicle weight is one of several factors that affect vehicle emissions. Vehicle weight is especially important when modeling emissions from heavy-duty trucks (HDTs). The motor vehicle emission simulator (MOVES) model provides a way to account for vehicle weight during the construction of vehicle emissions inventories. To date, vehicle weight has not received much attention, although reliable vehicle weight data have become increasingly available in the past several years with the deployment of weigh-in-motion (WIM) technology. This study developed a method for fusing vehicle weight data from WIM stations and traffic data from vehicle detector stations (VDS) to obtain HDT activity data for input in MOVES as vehicle operating mode distributions. The study identified trucks recorded by a WIM station that were likely to travel over a VDS during a specified time period. The measured weight data of the trucks were fused with the second-by-second speed and acceleration values in truck trajectories that were created based on the knowledge of truck traffic speed at the VDS. The study used freeways in Los Angeles County, California, as a case study. The case study showed that the distributions of vehicle operating mode were quite different between the proposed method and the existing method, which assumed an average weight value for all HDTs in the same class. In the example case illustrated in this paper, the proposed method resulted in 78% higher nitrogen oxide emissions and 30% higher particulate matter emissions than the existing method.


Author(s):  
Tanzila Khan ◽  
H. Christopher Frey

With more stringent U.S. fuel economy (FE) standards, the effect of auxiliary devices such as air-conditioning (AC) have received increased attention. AC is the largest auxiliary engine load for light duty gasoline vehicles (LDGVs). However, there are few data regarding the effect of AC operation on FE for LDGVs based on real-world measurements, especially for recent model year vehicles. The Motor Vehicle Emission Simulator (MOVES) is a regulatory model for estimating on-road vehicle energy-use and emissions. MOVES adjusts vehicle energy-use rates for AC effects. However, MOVES-predicted FE with AC has not been evaluated based on empirical measurements. The research objectives are to quantify the LDGVs FE penalty from AC and assess the accuracy of MOVES2014a-predicted FE with AC. The AC effect on real-world fleet-average FE was quantified based on 78 AC-off vehicles versus 55 AC-on vehicles, measured with onboard instruments on defined study routes. MOVES2014a-based FE penalty from AC was evaluated based on real-world estimates and chassis dynamometer-based FE test results used for FE ratings. The real-world FE penalty ranges between 1.3% and 7.5% among a wide range of driving cycles. Fuel consumption at idle is 13% higher with AC on. MOVES underestimates the real-world FE with AC by 6%, on average. MOVES overestimates the AC effect on cycle-average FE ranging between 13.5% and 18.5% for real-world and MOVES default cycles, and between 11.1% and 14.5% for standard cycles.


2005 ◽  
Vol 39 (5) ◽  
pp. 931-940 ◽  
Author(s):  
I. Schifter ◽  
L. Díaz ◽  
V. Múgica ◽  
E. López-Salinas

1999 ◽  
Vol 33 (14) ◽  
pp. 2328-2339 ◽  
Author(s):  
Steven H. Cadle ◽  
Patricia A. Mulawa ◽  
Eric C. Hunsanger ◽  
Ken Nelson ◽  
Ronald A. Ragazzi ◽  
...  

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