George Scora

Modal Emissions Model for Heavy-Duty Diesel Vehicles

There have been significant improvements in recent years in transportation and emissions modeling to allow better evaluations of transportation operational effects and associated vehicle emissions. In particular, instantaneous or modal emissions models have been developed for a variety of light-duty vehicles. To date, most of the effort has focused primarily on developing these models for light-duty vehicles with less effort devoted to heavy-duty diesel (HDD) vehicles. Although HDD vehicles currently make up only a fraction of the total vehicle population, they are major contributors to the emissions inventory. A description is provided of an HDD truck model that is part of a larger comprehensive modal emissions modeling (CMEM) program developed at the University of California (UC), Riverside. Several HDD truck submodels have been developed in the CMEM framework, each corresponding to a distinctive vehicle-technology category. The developed models use a parameterized physical approach in which the entire emission process is broken down into different components that correspond to physical phenomena associated with vehicle operation and emission production. A variety of trucks were extensively tested under a wide range of operating conditions at UC Riversides Mobile Emissions Research Laboratory. The collected data were then used to calibrate the HDD models. Particular care was taken to investigate and implement the effects of varying grade and the use of variable fuel injection strategies. Results show good estimates for fuel use and the regulated emission species including nitrogen oxides, one of the key targets for HDD vehicles.

Estimating Emissions and Fuel Consumption for Different Levels of Freeway Congestion

To improve upon the speed correction factor methodology used by conventional emission models (i.e., MOBILE and EMFAC), the Environmental Protection Agency is introducing in its latest version of MOBILE (version 6) a new set of facility-specific driving cycles. These cycles represent driving patterns for different facility types (e.g., highway and arterial) and congestion conditions. Using a state-of-the-art comprehensive modal emissions model developed under NCHRP Project 25-11, one is able to predict the integrated emissions and fuel use values for these cycles for a wide variety of vehicle-technology categories. These facility-congestion results are then compared with steady-state emissions-fuel use measurements that were made in deriving the modal model. Furthermore, cruise modes that have mild speed perturbations are also investigated. All of these results are then compared with the speed correction equations used in the conventional emissions factor models. It is found that the mild acceleration perturbations at high speeds can lead to significantly higher emissions compared with the steady-state values. Because of this, the new high-speed freeway driving cycles (representing higher levels of service) in many cases have (modeled) emissions higher than those for the cycles that represent lower levels of service. Fuel consumption by speed does not change drastically in the comparisons.

Analysis of Modal Emissions From Diverse In-Use Vehicle Fleet

The initial phase of a long-term project with national implications for the improvement of transportation and air quality is described. The overall objective of the research is to develop and verify a computer model that accurately estimates the impacts of a vehicle’s operating mode on emissions. This model improves on current emission models by allowing for the prediction of how traffic changes affect vehicle emissions. Results are presented that address the following points: vehicle recruitment, preliminary estimates of reproducibility, preliminary estimates of air conditioner effects, and preliminary estimates of changes in emissions relative to speed. As part of the development of a comprehensive modal emission model for light-duty vehicles, 28 distinct vehicle/technology categories have been identified based on vehicle class, emission control technology, fuel system, emission standard level, power-to-weight ratio, and emitter level (i.e., normal versus high emitter). These categories and the sampling proportions in a large-scale emissions testing program (over 300 vehicles to be tested) have been chosen in part based on emissions contribution. As part of the initial model development, a specific modal emissions testing protocol has been developed that reflects both real-world and specific modal events associated with different levels of emissions. This testing protocol has thus far been applied to an initial fleet of 30 vehicles, where at least 1 vehicle falls into each defined vehicle/technology category. The different vehicle/technology categories, the emissions testing protocol, and preliminary analysis that has been performed on the initial vehicle fleet are described.

Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions

The ability to monitor the state of a given roadway in order to better manage traffic congestion has become increasingly important. Sophisticated traffic management systems able to process both the static and mobile sensor data and provide traffic information for the roadway network are under development. In addition to typical traffic data such as flow, density, and average traffic speed, there is now strong interest in environmental factors such as greenhouse gases, pollutant emissions, and fuel consumption. It is now possible to combine high-resolution real-time traffic data with instantaneous emission models to estimate these environmental measures in real time. In this paper, a system that estimates average traffic fuel economy, CO2 , CO, HC, and NOx emissions using a computer-vision-based methodology in combination with vehicle-specific power-based energy and emission models is presented. The CalSentry system provides not only typical traffic measures but also gives individual vehicle trajectories (instantaneous dynamics) and recognizes vehicle categories, which are used in the emission models to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared with static emission inventory estimation models.

Designing On-Road Vehicle Test Programs for the Development of Effective Vehicle Emission Models

Mobile source emission models for years have depended on laboratory-based dynamometer data. Recently, however, portable emission measurement systems (PEMS) have become commercially available and in widespread use, and make on-road real-world measurements possible. As a result, the newest mobile source emission models (e.g., U.S. Environmental Protection Agency’s mobile vehicle emission simulator) are becoming increasingly dependent on PEMS data. Although on-road measurements are made under more realistic conditions than laboratory-based dynamometer test cycles, they introduce influencing variables that must be carefully measured for properly developed emission models. Further, test programs that simply measure in-use driving patterns of randomly selected vehicles will result in models that can effectively predict current-year emission inventories for typical driving conditions. However, when predicting more aggressive transportation operations than current typical operations (e.g., higher speeds, accelerations), the model predictions will be less certain. In this paper, various issues associated with on-road emission measurements and modeling are presented. Further, an example on-road emission data set and the reduction in estimation error through the addition of a short aggressive driving test to the in-use data are examined. On the basis of these results, recommendations are made on how to improve the on-road test programs for developing more robust emission models.

Measuring and Modeling Emissions from Extremely Low Emitting Vehicles

In recent years, automobile manufacturers have been producing gasoline-fueled vehicles that have very low tailpipe and evaporative emissions to meet stringent certification standards set by the U.S. Environmental Protection Agency and the California Air Resources Board. These extremely low-emitting vehicles are 98% to 99% cleaner than the catalyst-equipped vehicles produced in the mid-1980s. To understand better the emissions characteristics of these extremely low-emitting vehicles, as well as their potential impact on future air quality, researchers at the University of California, Riverside, have conducted a comprehensive study consisting of (a) an emissions measurement program, (b) the development of specific emissions models, and (c) the application of future emissions inventories to air quality models. Results have shown that in nearly all cases, these vehicles have emissions that are well below their stringent certification standards, and the vehicles continue to have low emissions as they age. On the basis of the measurement results, new modal emissions models have been created for both ultra-low-emission-certified vehicles and partial-zero-emission-certified vehicles. The model results compare well with actual measurements. With these models, it is possible to predict accurately future mobile source emissions inventories that will have an increasing number of these extremely low-emitting vehicles in the overall vehicle population. It is expected that the large penetration of these vehicles into the vehicle fleet will have a significant role in meeting ozone attainment levels in many regions.

Using Portable Emission Measurement Systems for Transportation Emissions Studies: Comparison with Laboratory Methods

Portable emission measurement systems (PEMS) are increasingly being used in a variety of transportation research projects to determine the impact of real-world vehicle emissions. One of the key questions that remain is how well these systems perform compared with testing that occurs in controlled laboratory conditions. To help answer this question, three PEMS were carefully evaluated for both gasoline and diesel light-duty vehicles in a dynamometer test facility. The evaluation was focused on the systems’ accuracy, time correspondence, and suitability for measuring transient emissions. Both cumulative mass emissions and modal emissions for carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx), and carbon dioxide (CO2) were measured for three gasoline and three diesel vehicles on three widely varying driving cycles. All of the PEMS proved to be both reasonably accurate and precise. The CO2 emissions measured by the PEMS were in excellent agreement (within 98%) with measurements from the laboratory system. Other pollutants measured were found to be in reasonable agreement (within 20% or better) for NOx and HC on diesel vehicles and CO on gasoline vehicles. The second-by-second emission rate measured with the PEMS matched well with the corresponding laboratory modal analyzer data for CO2, NOx, and CO under all driving cycles. Transient emissions of all pollutants agreed within 10% of the two systems for more than 6,000 data points from each vehicle. The results suggest that when properly set up and calibrated, PEMS are capable of measuring emissions from both gasoline and diesel vehicles to an accuracy within 20% of conventional laboratory modal analyzer systems.

MODELING ENLEANMENT EMISSIONS FOR LIGHT-DUTY VEHICLES

A comprehensive modal emissions model for light-duty cars and trucks is being developed under the sponsorship of NCHRP Project 25-11. Model development has been described previously for vehicles operating under stoichiometric and enrichment conditions. A modal emissions model is presented for vehicles operated under enleanment conditions. Enleanment typically occurs with sharp deceleration or load reduction events, and sometimes during long deceleration. Under enleanment conditions, the air/fuel ratio is lean and incomplete combustion or misfire occurs. Preliminary research indicates that enleanment emissions (particularly for hydrocarbons) contribute significantly to a vehicle’s overall emissions. An enleanment emissions module has been developed on the basis of second-by-second emission measurements generated at the College of Engineering—Center for Environmental Research and Technology’s vehicle testing facility using the Federal Test Procedure, US06, and a specially designed modal emission cycle (MEC01). On the basis of more than 200 vehicles tested and modeled, lean-burn hydrocarbon emissions (HClean) account for 10 to 20 percent of the overall HC emissions under the various test cycles. HClean emission contributions vary greatly from vehicle to vehicle, ranging from near 0 to more than 30 percent of total HC emissions of individual vehicles. After detailed analysis of the second-by-second emission data over the modal emission cycle MECO1, it was found that enleanment hydrocarbons emissions are mostly associated with rapid load reduction events and long deceleration events. The former is most likely to cause extremely high levels of HC as short spikes, and the latter is mostly associated with longer-lasting HC puffs. A methodology has been developed to characterize and model enleanment hydrocarbons emissions associated with these two events. The model estimates are compared with measurements, with encouraging results.

Intelligent off-board management of vehicle operating parameters

Todays modern vehicle has a number of on-board embedded control systems that perform a number of functions. These systems provide for a higher user convenience (e.g., cruise control), better safety (e.g., air bag systems), and lower pollutant emissions (e.g., energy and emission control systems). These systems generally operate autonomously, relying on internal state information. In this paper, we describe how these embedded systems can be improved upon through the use of external information provided through ITS telematic capability. There are several examples of this, such as dynamic intelligent speed adaptation, energy management strategies for hybrid electric and fuel-cell vehicles, and dynamically controlled emission control systems. These different application areas and methods are described herein. Further, a number of simulation experiments have been carried for the case of controlling the vehicle operating parameters of a heavy-duty vehicles emission control system. It has been shown that by simply changing the emission control strategy based on location can result in significantly lower emissions in critical areas at a relatively low cost.

Real-time roadway emissions estimation using visual traffic measurements

Monitoring the state of our roadways has become increasingly important in order to better manage traffic congestion. Sophisticated traffic management systems are being developed that are able to process both static and mobile sensor data that provide traffic information for the roadway network. In addition to typical traffic data such as flow, density, and average traffic speed, there is now strong interest in environmental factors such as greenhouse gas and pollutant emissions from traffic. It is now possible to combine real-time traffic data along with instantaneous emission models to estimate these environmental measures in real-time. In this paper, we describe a system that can more accurately determine average traffic fuel economy, CO2, CO, HC, and NOx emissions using a computer vision-based methodology that also incorporates energy/emission profiles from the comprehensive modal emissions model CMEM and EPAs MOVES emission factor database. The vision system provides information not only on average traffic speed, density, and flow, but also on individual vehicle trajectories and recognized vehicle categories. The vehicle trajectories for the specific identified categories are used by the emissions model to predict environmental parameters. This estimation process provides far more dynamic and accurate environmental information compared to static emission inventory estimation models.