Development of vehicle emission rates based on vehicle-specific power and velocity

The selective catalytic reduction (SCR) is the most commonly used technique for decreasing the emissions of nitrogen oxides (NOx) from heavy-duty diesel vehicles (HDDVs). However, the same injection strategy in the SCR system shows significant variations in NOx emissions even at the same operating mode. This kind of heterogeneity poses challenges to the development of emission inventories and to the assessment of emission reductions. Existing studies indicate that these differences are related to the exhaust temperature. In this study, an emission model is established for different source types of HDDVs based on the real-time data of operating modes. Firstly, the initial NOx emission rates (ERs) model is established using the field vehicle emission data. Secondly, a temperature model of the vehicle exhaust based on the vehicle specific power (VSP) and the heat loss coefficient is established by analyzing the influencing factors of the NOx conversion efficiency. Thirdly, the models of NOx emissions and the urea consumption are developed based on the chemical reaction in the SCR system. Finally, the NOx emissions are compared with the real-world emissions and the estimations by the proposed model and the Motor Vehicle Emission Simulator (MOVES). This indicates that the relative error by the proposed method is 12.5% lower than those calculated by MOVES. The characteristics of NOx emissions under different operating modes are analyzed through the proposed model. The results indicate that the NOx conversion rate of heavy-duty diesel trucks (HDDTs) is 39.2% higher than that of urban diesel transit buses (UDTBs).

Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle

In order to interpret how the uncertainty in the output can be apportioned to different sources of uncertainty in its inputs, it is critical to understand the MOVES model sensitivity. In this research, the Motor Vehicle Emission Simulator (MOVES) model project level sensitivity tests on running emission were conducted through the analysis of vehicle specific power (VSP), scaled tractive power, and MOVES emission rates versus speed curves. This study tested the speed, acceleration, and grade - the three most critical variables for vehicle specific power for light duty vehicles and scaled tractive power (STP) for heavy duty vehicles. A Latin Hypercube sampling-based method for estimation of the Sobal sensitivity indices showed that the speed is the most critical variable among the three inputs for both VSP and STP. Acceleration and grades showed lower response to the main effects and sensitivity indices. No significant differences on emission rates were identified among the regulatory classes of heavy duty vehicles.

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.

ABSTRACTFlex fuel vehicles (FFVs) typically operate on gasoline or E85, an 85%/15% volume blend of ethanol and gasoline. Differences in FFV fuel use and tailpipe emission rates are quantified for E85 versus gasoline based on real-world measurements of five FFVs with a portable emissions measurement system (PEMS), supplemented chassis dynamometer data, and estimates from the Motor Vehicle Emission Simulator (MOVES) model. Because of inter-vehicle variability, an individual FFV may have higher nitrogen oxide (NOx) or carbon monoxide (CO) emission rates on E85 versus gasoline, even though average rates are lower. Based on PEMS data, the comparison of tailpipe emission rates for E85 versus gasoline is sensitive to vehicle-specific power (VSP). For example, although CO emission rates are lower for all VSP modes, they are proportionally lowest at higher VSP. Driving cycles with high power demand are more advantageous with respect to CO emissions, but less advantageous for NOx. Chassis dynamometer data are available for 121 FFVs at 50,000 useful life miles. Based on the dynamometer data, the average difference in tailpipe emissions for E85 versus gasoline is −23% for NOx, −30% for CO, and no significant difference for hydrocarbons (HC). To account for both the fuel cycle and tailpipe emissions from the vehicle, a life cycle inventory was conducted. Although tailpipe NOx emissions are lower for E85 versus gasoline for FFVs and thus benefit areas where the vehicles operate, the life cycle NOx emissions are higher because the NOx emissions generated during fuel production are higher. The fuel production emissions take place typically in rural areas. Although there are not significant differences in the total HC emissions, there are differences in HC speciation. The net effect of lower tailpipe NOx emissions and differences in HC speciation on ozone formation should be further evaluated.Implications: Reported comparisons of flex fuel vehicle (FFV) tailpipe emission rates for E85 versus gasoline have been inconsistent. To date, this is the most comprehensive evaluation of available and new data. The large range of inter-vehicle variability illustrates why prior studies based on small sample sizes led to apparently contradictory findings. E85 leads to significant reductions in tailpipe nitrogen oxide (NOx) and carbon monoxide (CO) emission rates compared with gasoline, indicating a potential benefit for ozone air quality management in NOx-limited areas. The comparison of FFV tailpipe emissions between E85 and gasoline is sensitive to power demand and driving cycles.

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH3 emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM2.5) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppbv in January and 3.0 ppbv in July. The updated on-road NH3 emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM2.5 concentrations increased in urban areas, including enhancements of up to 0.5 μg/m3 in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

The Motor Vehicle Emissions Simulator (MOVES) quantifies emissions as a function of the operating mode (opmode) and emissions rates. The opmode, the determinant parameter in estimating emissions, is defined by two critical parameters: speed and scaled tractive power (STP). Activity characteristics of transit buses are commonly recognized as being quite different from those of other vehicles, and this study found the values of the two parameters for transit buses to be much smaller than those for other vehicles. However, the MOVES program uses an identical opmode binning method for transit buses and other vehicles, a method that likely leads to errors in emissions estimations for transit buses. This paper developed a binning method based on massive field data collected in Beijing to improve the opmode binning for transit buses. To this end, STP fractions, vehicle kilometers traveled (VKT) fractions, and emissions contributions were first investigated. The STP values were grouped into nine bins on the basis of analysis of emissions rates and emissions contributions. Three speed bins were then determined with the hierarchical clustering method and the averaging of VKT fractions. As a result, 29 opmode bins were defined for transit buses. Finally, the proposed method was applied to real-world emissions data in Beijing. The results indicated that the proposed binning method could reduce errors in emissions estimation errors. On average, the relative errors in estimating carbon dioxide, carbon monoxide, nitrogen oxide, and hydrocarbon emissions by the proposed method were 2.0%, 5.9%, 1.6%, and 1.5% lower, respectively, than errors made by the MOVES method.

On-ramps and off-ramps that serve as connections between high-speed facilities and arterials are potential hotspots for vehicle emissions. The engine load associated with grade and acceleration on uphill ramps can lead to significant emissions of criteria pollutants and greenhouse gases (GHGs) over a short distance. This study explores transit bus operations and emissions at ramps using Global Positioning System (GPS) data collected from Detroit transit buses. Ramp-associated operating data are extracted from the vehicle traces using ArcGIS and assigned to the applicable United States Environmental Protection Agency’s emission rates, i.e., EPA’s Motor Vehicle Emission Simulator (MOVES). The results show that transit bus emission rates for on-ramp operations at 40 mph (64.37 km/h) are about double the average emission rate on the MOVES highway cycles. For lower on-ramp speeds (< 64.37 km/h), as average speeds decrease, on-ramp emission rates drop roughly to the highway emission rates given the less aggressive acceleration noted in the data. Off-ramp emission rates are approximately half of the highway emission rates. The study also finds that post-ramp acceleration, right after buses enter the highway from the on-ramp, contributes to high emissions, because of the high-speed and high-power operations. This is true for the loop on-ramp, where the bus emission rate after entering the highway is higher than the emissions associated with driving on the ramp. On-ramp emissions are found to vary across a wide range of conditions, indicating that further study and more data are needed to explore the overall impacts of on-ramp and post-ramp activity in emissions modeling. A sensitivity analysis of ramp grade effect on emission indicates that ramp grade should be specifically considered in project-level analyses. The research results are useful for understanding ramp driving characteristics, the potential impacts of ramp grade on emissions, and the ramp hotspot analysis.

Evaluation of on-road vehicle CO and NOx National Emission Inventories using an urban-scale source-oriented air quality model

Identifying the effect of vehicle operating history on vehicle running emissions

Assessing methods for comparing emissions from gasoline and diesel light-duty vehicles based on microscale measurements

Emission measurement of diesel vehicles in Hong Kong through on-road remote sensing: Performance review and identification of high-emitters

A detailed and accurate fuel model fuel consumption model that reflects real-world fuel consumption is required as input for devising and executing a model policy for prospective regulatory tools. The fuel consumption model based on the vehicle-specific power (VSP) has rapidly become the primary development direction since the release of the Motor Vehicle Emissions Simulator (MOVES) model. However, fuel consumption cannot be accurately characterized under high-speed scenarios. This work develops two fuel consumption models for the light-duty (gasoline) vehicles that can better characterize fuel consumption for light-duty vehicles under high-speed scenarios. For model 1, the VSP of −5kW/ton is a crucial turning point. When VSP∈ [−30, −5] kW/ton, the fuel rate is only determined by speed. When VSP∈(−5, 30], the fuel rate will gradually increase with VSP, and the growth characteristics will vary with speed. Model 2 develops the new interpretations for VSP and forms the one-to-one correspondence between the fuel rate and the new VSP. The two models can separately improve the accuracy by 12.2% and 13.8% compared with the conventional model. The fuel factor differences become significant when speed is higher than 65 km/h, which are separately 30.66% and 28.13% higher than the conventional VSP model when the speed is 100 km/h. Further, the fuel factors of the two models for freeways are, respectively, 6.33% and 7.56% higher than the conventional VSP model, and the distinction for arterial, collector, and local street roads is not notable.

With increasingly wide recognition and acceptance of vehicle-specific power (VSP) as a viable variable in modeling vehicle emissions, the method for defining VSP bins is becoming critical. The primary objective of this study is to develop emission-specific VSP bins for estimating carbon dioxide (CO2) emissions for light-duty vehicles by using the real-world data collected in Beijing. The proposed method is developed by considering both emission characteristics and emission contributions of each bin. In this method, speed is chosen as the secondary parameter in defining bins because it has a higher impact on the CO2 emission rate than the engine stress for each VSP bin. With the proposed method, 24 VSP bins are eventually defined on the basis of the emission data collected in Beijing. In the proposed VSP bins, the data under VSP < 0 are grouped into one single bin because of their similar CO2 emission rates and comparable total emission contributions to other bins. Data at VSP = 0 are defined as an independent bin for characteristics that this single VSP point carries, such as its high VSP frequency, high total CO2 emission contributions, and significant lower emission rate than that of adjacent bins. On the basis of the validation of the proposed method, it is found that the use of an independent bin for VSP = 0 improves the accuracy of CO2 emission estimates and that the use of a single bin for VSP < 0, which simplifies the computational procedure, will not increase the error in the estimation of CO2 emissions.

Using near-road observations of CO, NOy, and CO2 to investigate emissions from vehicles: Evidence for an impact of ambient temperature and specific humidity