A chassis test procedure to mimic the heavy-duty engine transient emissions certification test

In-use emissions from vehicles using heavy-duty diesel engines can be significantly higher than the levels obtained during engine certification. These higher levels may be caused by a combination of degradation of engine components, poor engine maintenance, degradation or failure of emissions after-treatment devices, and engine and emissions system tampering. A direct comparison of in-use vehicle emissions with engine certification levels, however, is not possible without removing an engine from the vehicle in order to perform engine dynamometer emissions testing. The goal of this research was to develop a chassis test procedure that mimics the engine performance, and as such the expected emissions levels, from the engine certification emissions test prescribed in the U.S. Code of Federal Regulations. Emissions measurements were taken from two engines during testing on an engine dynamometer using the transient heavy-duty Federal Test Procedure (FTP). Additionally, each engine was installed in an appropriate vehicle, and emissions measurements were taken using a chassis dynamometer while employing a vehicle driving schedule intended to match closely the instantaneous torque and speed schedule of the engine FTP. Engine and chassis testing was performed with the engines in stock (unmodified) condition as well as in several modes to simulate either tampered or poorly maintained conditions. The use of a chassis test as a predictive tool for determining whether an engine in a vehicle would pass the engine certification test has proven to be worthwhile. Analysis of the data shows that identification of chassis-mounted engines with NOx emissions above certification levels is possible by employing engine-specific correction factors. In the case of PM emissions, significant data scatter allowed only the identification of gross PM emitters. Engine tampering and poor maintenance can raise PM and NOx emissions, and these increases can be correctly identified by a chassis test. Analysis of chassis and engine CO and HC emissions did not reveal a strong enough correlation to warrant the use of the chassis test for emissions screening of these two pollutants.     

Criteria and Air-Toxic Emissions from In-Use Automobiles in the National Low-Emission Vehicle Program

Abstract

The U.S. Environmental Protection Agency (EPA) implemented a program to identify tailpipe emissions of criteria and air-toxic contaminants from in-use, light-duty low-emission vehicles (LEVs). EPA recruited 25 LEVs in 2002 and measured emissions on a chassis dynamometer using the cold-start urban dynamometer driving schedule of the Federal Test Procedure. The emissions measured included regulated pollutants, particulate matter, speciated hydrocarbon compounds, and carbonyl compounds. The results provided a comparison of emissions from real-world LEVs with emission standards for criteria and air-toxic compounds. Emission measurements indicated that a portion of the in-use fleet tested exceeded standards for the criteria gases. Real-time regulated and speciated hydrocarbon measurements demonstrated that the majority of emissions occurred during the initial phases of the cold-start portion of the urban dynamometer driving schedule. Overall, the study provided updated emission factor data for real-world, in-use operation of LEVs for improved emissions modeling and mobile source inventory development.     

The effect of oils on PAH, PCDD, PCDF, and PCB emissions from a spark engine fueled with leaded gasoline

The effect of synthetic and mineral oils on the formation of polyaromatic hydrocarbons (PAHs), polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) in emissions from a spark ignition engine was studied on a Skoda Favorit engine fueled with leaded gasoline. The test cycle simulated urban traffic conditions on a chassis dynamometer, in accordance with the ECC 83.00 test. The data for selected PAHs as well as PCDDs, PCDFs, and PCBs congener profiles are presented. PCDD/Fs emissions for an unused oil and the oil after 10000-km operation varied from 300 to 2000 fmol/m3, PCBs emissions from 75 to 178 pmol/m3, and PAHs emissions from 150 to 420 microg/m3. The content of PCBs in oils varied from 2 to 920 mg/kg.     

Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.     

A Chassis Test Procedure to Mimic the Heavy-Duty Engine Transient Emissions Certification Test

ABSTRACT

In-use emissions from vehicles using heavy-duty diesel engines can be significantly higher than the levels obtained during engine certification. These higher levels may be caused by a combination of degradation of engine components, poor engine maintenance, degradation or failure of emissions after-treatment devices, and engine and emissions system tampering. A direct comparison of in-use vehicle emissions with engine certification levels, however, is not possible without removing an engine from the vehicle in order to perform engine dynamometer emissions testing. The goal of this research was to develop a chassis test procedure that mimics the engine performance, and as such the expected emissions levels, from the engine certification emissions test prescribed in the U.S. Code of Federal Regulations. Emissions measurements were taken from two engines during testing on an engine dynamometer using the transient heavy-duty Federal Test Procedure (FTP). Additionally, each engine was installed in an appropriate vehicle, and emissions measurements were taken using a chassis dynamometer while employing a vehicle driving schedule     

Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Abstract

With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO₂] and oxides of nitrogen [NO_x]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO₂ and NO_x in the case of a class-8 truck but were more accurate as the truck weight increased.     

Modeling transit bus fuel consumption on the basis of cycle properties

A method exists to predict heavy-duty vehicle fuel economy and emissions over an "unseen" cycle or during unseen on-road activity on the basis of fuel consumption and emissions data from measured chassis dynamometer test cycles and properties (statistical parameters) of those cycles. No regression is required for the method, which relies solely on the linear association of vehicle performance with cycle properties. This method has been advanced and examined using previously published heavy-duty truck data gathered using the West Virginia University heavy-duty chassis dynamometer with the trucks exercised over limited test cycles. In this study, data were available from a Washington Metropolitan Area Transit Authority emission testing program conducted in 2006. Chassis dynamometer data from two conventional diesel buses, two compressed natural gas buses, and one hybrid diesel bus were evaluated using an expanded driving cycle set of 16 or 17 different driving cycles. Cycle properties and vehicle fuel consumption measurements from three baseline cycles were selected to generate a linear model and then to predict unseen fuel consumption over the remaining 13 or 14 cycles. Average velocity, average positive acceleration, and number of stops per distance were found to be the desired cycle properties for use in the model. The methodology allowed for the prediction of fuel consumption with an average error of 8.5% from vehicles operating on a diverse set of chassis dynamometer cycles on the basis of relatively few experimental measurements. It was found that the data used for prediction should be acquired from a set that must include an idle cycle along with a relatively slow transient cycle and a relatively high speed cycle. The method was also applied to oxides of nitrogen prediction and was found to have less predictive capability than for fuel consumption with an average error of 20.4%.     

Estimating Full IM240 Emissions from Partial Test Results: Evidence from Arizona

ABSTRACT

The expense and inconvenience of enhanced-vehicle-emissions testing using the full 240-second dynamometer test has led states to search for ways to shorten the test process. In fact, all states that currently use the IM240 allow some type of fast-pass, usually as early in the test as second 31, and Arizona has allowed vehicles to fast-fail after second 93. While these shorter tests save states millions of dollars in inspection lanes and driver costs, there is a loss of information since test results are no longer comparable across vehicles. This paper presents a methodology for estimating full 240-second results from partial-test results for three pollutants: HC, CO, and NO_x. If states can convert all tests to consistent IM240 readings, they will be able to better characterize fleet emissions and to evaluate the impact of inspection and maintenance and other programs on emissions over time. Using a random sample of vehicles in Arizona which received full 240-second tests, we use regression analysis to estimate the relationship between emissions at second 240 and emissions at earlier seconds in the test. We examine the influence of other variables such as age, model-year group, and the pollution level itself on this relationship. We also use the estimated coefficients in several applications. First, we try to shed light on the frequent assertion that the results of the dynamometer test provide guidance for vehicle repair of failing vehicles. Using a probit analysis, we find that the probability that a failing vehicle will pass the test on the first retest is greater the longer the test has progressed. Second, we test the accuracy of our estimates for forecasting fleet emissions from partial-test emissions results in Arizona. We find forecasted fleet average emissions to be very close to the actual fleet averages for light-duty vehicles, but not quite as good for trucks, particularly when NO_x emissions are forecast.     

Real-World Emissions from a Modern Production Vehicle Driven in Los Angeles

The emissions of hydrocarbons, nitric oxide, and carbon monoxide from one modern vehicle were measured using on-board instrumentation during about 350 miles of driving in Los Angeles, California. Emissions during on-road driving were compared to those obtained on dynamometers using the urban dynamometer driving schedule (UDDS). Although this study only used one driver and vehicle, tested over a relatively short distance, the analysis technique may be useful for a larger evaluation of off-cycle emissions.

The test vehicle had low warmed-up running emissions over the UDDS and for most of the on-road testing where the air-to-fuel ratio was maintained at the stoichiometric point. However, occasional heavily-loaded conditions during the on-road testing led to richerthan-stoiehiometric operation.

During these brief enrichment events, which lasted up to 29 seconds, CO emissions were increased by a factor of 2500 and HC by a factor of 40 over closed-loop stoichiometric operation. Nitrogen oxide emissions were similar during low-load stoichiometric operation and high-load enriched operation. The relatively constant gram-per-second emission rate of CO and HC observed during enriched operation suggests that such information can be combined with determinations of the frequency of enrichment for a large number of vehicles with similar calibrations to estimate the impact of the additional emissions from enrichment on emissions inventories.     

Criteria and air-toxic emissions from in-use automobiles in the National Low-Emission Vehicle program

The U.S. Environmental Protection Agency (EPA) implemented a program to identify tailpipe emissions of criteria and air-toxic contaminants from in-use, light-duty low-emission vehicles (LEVs). EPA recruited 25 LEVs in 2002 and measured emissions on a chassis dynamometer using the cold-start urban dynamometer driving schedule of the Federal Test Procedure. The emissions measured included regulated pollutants, particulate matter, speciated hydrocarbon compounds, and carbonyl compounds. The results provided a comparison of emissions from real-world LEVs with emission standards for criteria and air-toxic compounds. Emission measurements indicated that a portion of the in-use fleet tested exceeded standards for the criteria gases. Real-time regulated and speciated hydrocarbon measurements demonstrated that the majority of emissions occurred during the initial phases of the cold-start portion of the urban dynamometer driving schedule. Overall, the study provided updated emission factor data for real-world, in-use operation of LEVs for improved emissions modeling and mobile source inventory development.     

Development of engine activity cycles for the prime movers of unconventional natural gas well development

With the advent of unconventional natural gas resources, new research focuses on the efficiency and emissions of the prime movers powering these fleets. These prime movers also play important roles in emissions inventories for this sector. Industry seeks to reduce operating costs by decreasing the required fuel demands of these high horsepower engines but conducting in-field or full-scale research on new technologies is cost prohibitive. As such, this research completed extensive in-use data collection efforts for the engines powering over-the-road trucks, drilling engines, and hydraulic stimulation pump engines. These engine activity data were processed in order to make representative test cycles using a Markov Chain, Monte Carlo (MCMC) simulation method. Such cycles can be applied under controlled environments on scaled engines for future research. In addition to MCMC, genetic algorithms were used to improve the overall performance values for the test cycles and smoothing was applied to ensure regression criteria were met during implementation on a test engine and dynamometer. The variations in cycle and in-use statistics are presented along with comparisons to conventional test cycles used for emissions compliance.

Implications: Development of representative, engine dynamometer test cycles, from in-use activity data, is crucial in understanding fuel efficiency and emissions for engine operating modes that are different from cycles mandated by the Code of Federal Regulations. Representative cycles were created for the prime movers of unconventional well development—over-the-road (OTR) trucks and drilling and hydraulic fracturing engines. The representative cycles are implemented on scaled engines to reduce fuel consumption during research and development of new technologies in controlled laboratory environments.     

Comparison of on-road emissions for B-0, B-10, and B-20 in transit buses

Biodiesels are often marketed as being cleaner than regular diesel for emissions. Emission test results depend on the biodiesel blend, but laboratory tests suggest that biodiesels decrease particulate matter, carbon monoxide, hydrocarbons, and air toxins when compared to regular diesel. Results for the amount of oxides of nitrogen (NOx) have been less conclusive. Tests have also not evaluated the commonly available ranges of biodiesel blends in the laboratory. Additionally, little information is available from on-road studies, so the effectiveness of using biodiesels to reduce actual emissions is unknown. A more complex relationship exists between engine operation and the rate of emission production than is typically evaluated using engine or chassis dynamometer tests. On-road emissions can vary dramatically because emissions are correlated to engine mode. Additionally, activity such as idling, acceleration, deceleration, and operation against a grade can produce higher emissions than more stable engine operating modes. Because these modes are not well captured in a laboratory environment, understanding on-road relationships is critical in evaluating the emissions reductions that may be possible with biodiesels. More tests and quantifications of the effects of different blends on engine and vehicle performance are required to promote widespread use of biodiesel. The objective of this research was to conduct on-road tests to compare the emission impacts of different blends of biodiesel to regular diesel fuel under different operating conditions. The team conducted on-road tests that utilized a portable emissions monitoring system that was used to instrument transit buses. Regular diesel and different blends of biodiesel were evaluated during on-road engine operation by instrumenting three in-use transit buses, from the CyRide system of Ames, Iowa, along an existing transit route.     

The Formation of Mg3Ca(S04)4 during the Sulfation Reaction of Dolomite

Abstract

Inspection and maintenance programs for motor vehicles in the United States increasingly use loaded mode mass emissions testing (IM240). A method was developed to predict mass emission rates and mass emission changes, particularly from repair benefits, using a low-cost, portable four-gas non-dispersive infrared (NDIR) vehicle exhaust gas analyzer. A single vehicle was tested several times with the analyzer while on the dynamometer and undergoing successive repairs. Excellent correlations for CO and HC were observed. Five vehicles were measured using an on-road driving loop before and after emissions-related repairs, while another three vehicles were tested with no repairs performed. The on-road concentration data used to guide the repair process were converted to grams per gallon; when divided by estimated miles per gallon, this gave grams per mile emissions for comparison to IM240. Correlation coefficients (r²) of 0.87 for CO and 0.76 for HC were achieved for the 13 tests. The linear correlations between IM240 and emissions measured by this method would allow repair facilities to perform a relatively inexpensive test for diagnostic purposes and to estimate repair effectiveness without the need for a dynamometer.     

Comment on “Mortality and Air Pollution: Revisited”

Abstract

Test-to-test variability has been observed by many current testing methods, including the Federal Test Procedure, the IM240 dynamometer test, the idle test common to many Inspection and Maintenance programs, and on-road remote sensing. The variability is attributable to the vehicle, not to the testing procedure. Because the vehicles are the dominant source of variability, the only way such vehicles can be reliably identified is through the use of multiple tests. The emissions variability increases with increasing average emissions, and it appears to be prevalent among the few newer technology vehicles with defective, but untampered, closed-loop emissions control systems (1981 and newer models). In one fleet the variable emitters constitute 2.2% to 4.8% of the vehicles and contribute 8.5% to 22% of the total carbon monoxide emissions. Scheduled I/M programs that fail to ensure repair of these vehicles allow a significant portion of vehicles with excess emissions to escape reduction measures.     

Development of the Tehran car driving cycle

This paper describes the development of the car driving cycle for the capital city of Tehran. Driving cycle is an essential requirement for the evaluation of the exhaust emissions using the chassis dynamometer test. In this study, the driving data are collected from several cars under real traffic conditions. The method used in this study for data analysis is based on the definition and the classification of the microtrips. The developed cycle is named TEH_CAR. The results reveal that the TEH_CAR cycle characteristics are close to the transient FTP cycle and different from the synthetic ECE cycle.     

Generation and characterization of diesel exhaust in a facility for controlled human exposures

An idling medium-duty diesel truck operated on ultralow sulfur diesel fuel was used as an emission source to generate diesel exhaust for controlled human exposure. Repeat tests were conducted on the Federal Test Procedure using a chassis dynamometer to demonstrate the reproducibility of this vehicle as a source of diesel emissions. Exhaust was supplied to a specially constructed exposure chamber at a target concentration of 100 microg x m(-3) diesel particulate matter (DPM). Spatial variability within the chamber was negligible, whereas emission concentrations were stable, reproducible, and similar to concentrations observed on the dynamometer. Measurements of nitric oxide, nitrogen dioxide, carbon monoxide, particulate matter (PM), elemental and organic carbon, carbonyls, trace elements, and polycyclic aromatic hydrocarbons were made during exposures of both healthy and asthmatic volunteers to DPM and control conditions. The effect of the so-called "personal cloud" on total PM mass concentrations was also observed and accounted for. Conventional lung function tests in 11 volunteer subjects (7 stable asthmatic) did not demonstrate a significant change after 2-hr exposures to diesel exhaust. In summary, we demonstrated that this facility can be effectively and safely used to evaluate acute responses to diesel exhaust exposure in human volunteers.     

Real-world fuel use and gaseous emission rates for flex fuel vehicles operated on E85 versus gasoline

Flex 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 (NO_x) 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 NO_x. 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 NO_x, ?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 NO_x emissions are lower for E85 versus gasoline for FFVs and thus benefit areas where the vehicles operate, the life cycle NO_x emissions are higher because the NO_x 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 NO_x 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 (NO_x) and carbon monoxide (CO) emission rates compared with gasoline, indicating a potential benefit for ozone air quality management in NO_x-limited areas. The comparison of FFV tailpipe emissions between E85 and gasoline is sensitive to power demand and driving cycles.     

Evolutionary algorithm for vehicle driving cycle generation

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.     

At the Crossroads

ABSTRACT

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of “microtrips”, which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of “microtrips” isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate “microtrips” is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

IMPLICATIONS It is expected that the paper will provide a useful tool for modeling and analysis of vehicle fuel economy and emissions and for the design, optimization, and analysis of driving cycles for testing and vehicle fleet management.     

Regulated and unregulated emissions from an internal combustion engine operating on ethanol-containing fuels

In the present work, the effect of ethanol addition to gasoline on regulated and unregulated emissions is studied. A 4-cylinder OPEL 1.6 L internal combustion engine equipped with a hydraulic brake dynamometer was used in all the experiments. For exhaust emissions treatment a typical three-way catalyst was used. Among the various compounds detected in exhaust emissions, the following ones were monitored at engine and catalyst outlet: methane, hexane, ethylene, acetaldehyde, acetone, benzene, 1,3-butadiene, toluene, acetic acid and ethanol. Addition of ethanol in the fuel up to 10% w/w had as a result an increase in the Reid vapour pressure of the fuel, which indicates indirectly increased evaporative emissions, while carbon monoxide tailpipe emissions were decreased. For ethanol-containing fuels, acetaldehyde emissions were appreciably increased (up to 100%), especially for fuel containing 3% w/w ethanol. In contrast, aromatics emissions were decreased by ethanol addition to gasoline. Methane and ethanol were the most resistant compounds to oxidation while ethylene was the most degradable compound over the catalyst. Ethylene, methane and acetaldehyde were the main compounds present at engine exhaust while methane, acetaldehyde and ethanol were the main compounds in tailpipe emissions for ethanol fuels after the catalyst operation.