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%.     

Idle emissions from heavy-duty diesel and natural gas vehicles at high altitude

Idle emissions of total hydrocarbon (THC), CO, NOx, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NOx, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NOx, and 0.048 g/min PM. Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NOx emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NOx emissions increased with model year with a linear fit (r2 = 0.6). PART5 nationwide fleet average emissions are within 1 order of magnitude of emissions for the group of vehicles tested in this study. Aldehyde emissions for bus idling averaged 6 mg/min. The VOF averaged 19% of total PM for buses and 49% for trucks. CNG vehicle idle emissions averaged 1.435 g/min for THC, 1.119 g/min for CO, 0.267 g/min for NOx, and 0.003 g/min for PM. The g/min PM emissions are only a small fraction of g/min PM emissions during vehicle driving. However, idle emissions of NOx, CO, and THC are significant in comparison with driving emissions.     

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     

Effects of engine speed and accessory load on idling emissions from heavy-duty diesel truck engines

A nontrivial portion of heavy-duty vehicle emissions of NOx and particulate matter (PM) occurs during idling. Regulators and the environmental community are interested in curtailing truck idling emissions, but current emissions models do not characterize them accurately, and little quantitative data exist to evaluate the relative effectiveness of various policies. The objectives of this study were to quantify the effect of accessory loading and engine speed on idling emissions from a properly functioning, modern, heavy-duty diesel truck and to compare these results with data from earlier model year vehicles. It was found that emissions during idling varied greatly as a function of engine model year, engine speed, and accessory load conditions. For the 1999 model year Class 8 truck tested, raising the engine speed from 600 to 1050 rpm and turning on the air conditioning resulted in a 2.5-fold increase in NOx emissions in grams per hour, a 2-fold increase in CO2 emissions, and a 5-fold increase in CO emissions while idling. On a grams per gallon fuel basis, NOx emissions while idling were approximately twice as high as those at 55 mph. The CO2 emissions at the two conditions were closer. The NOx emissions from the 1999 truck while idling with air conditioning running were slightly more than those of two 1990 model year trucks under equivalent conditions, and the hydrocarbon (HC) and CO emissions were significantly lower. It was found that the NOx emissions used in the California Air Resources Board's (CARB) EMFAC2000 and the U.S. Environmental Protection Agency's (EPA) MOBILE5b emissions inventory models were lower than those measured in all of the idling conditions tested on the 1999 truck.     

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.     

Temperature effects on particulate emissions from DPF-equipped diesel trucks operating on conventional and biodiesel fuels

Emissions tests were conducted on two medium heavy-duty diesel trucks equipped with a particulate filter (DPF), with one vehicle using a NOx absorber and the other a selective catalytic reduction (SCR) system for control of nitrogen oxides (NOx). Both vehicles were tested with two different fuels (ultra-low-sulfur diesel [ULSD] and biodiesel [B20]) and ambient temperatures (70ºF and 20ºF), while the truck with the NOx absorber was also operated at two loads (a heavy weight and a light weight). The test procedure included three driving cycles, a cold start with low transients (CSLT), the federal heavy-duty urban dynamometer driving schedule (UDDS), and a warm start with low transients (WSLT). Particulate matter (PM) emissions were measured second-by-second using an Aethalometer for black carbon (BC) concentrations and an engine exhaust particle sizer (EEPS) for particle count measurements between 5.6 and 560 nm. The DPF/NOx absorber vehicle experienced increased BC and particle number concentrations during cold starts under cold ambient conditions, with concentrations two to three times higher than under warm starts at higher ambient temperatures. The average particle count for the UDDS showed an opposite trend, with an approximately 27% decrease when ambient temperatures decreased from 70ºF to 20ºF. This vehicle experienced decreased emissions when going from ULSD to B20. The DPF/SCR vehicle tested had much lower emissions, with many of the BC and particle number measurements below detectable limits. However, both vehicles did experience elevated emissions caused by DPF regeneration. All regeneration events occurred during the UDDS cycle. Slight increases in emissions were measured during the WSLT cycles after the regeneration. However, the day after a regeneration occurred, both vehicles showed significant increases in particle number and BC for the CSLT drive cycle, with increases from 93 to 1380% for PM number emissions compared with tests following a day with no regeneration.

Implications:?The use of diesel particulate filters (DPFs) on trucks is becoming more common throughout the world. Understanding how DPFs affect air pollution emissions under varying operating conditions will be critical in implementing effective air quality standards. This study evaluated particulate matter (PM) and black carbon (BC) emissions with two DPF-equipped heavy-duty diesel trucks operating on conventional fuel and a biodiesel fuel blend at varying ambient temperatures, loads, and drive cycles.     

Automated Chemical Mass Balance Receptor Modeling

Abstract

A nontrivial portion of heavy-duty vehicle emissions of NO_x and particulate matter (PM) occurs during idling. Regulators and the environmental community are interested in curtailing truck idling emissions, but current emissions models do not characterize them accurately, and little quantitative data exist to evaluate the relative effectiveness of various policies. The objectives of this study were to quantify the effect of accessory loading and engine speed on idling emissions from a properly functioning, modern, heavy-duty diesel truck and to compare these results with data from earlier model year vehicles. It was found that emissions during idling varied greatly as a function of engine model year, engine speed, and accessory load conditions. For the 1999 model year Class 8 truck tested, raising the engine speed from 600 to 1050 rpm and turning on the air conditioning resulted in a 2.5-fold increase in NO_x emissions in grams per hour, a 2-fold increase in CO₂ emissions, and a 5-fold increase in CO emissions while idling. On a grams per gallon fuel basis, NO_x emissions while idling were approximately twice as high as those at 55 mph. The CO₂ emissions at the two conditions were closer. The NO_x emissions from the 1999 truck while idling with air conditioning running were slightly more than those of two 1990 model year trucks under equivalent conditions, and the hydrocarbon (HC) and CO emissions were significantly lower. It was found that the NO_x emissions used in the California Air Resources Board’s (CARB) EMFAC2000 and the U.S. Environmental Protection Agency’s (EPA) MOBILE5b emissions inventory models were lower than those measured in all of the idling conditions tested on the 1999 truck.     

Field Measurements of Particulate Matter Emissions,Carbon Monoxide,and Exhaust Opacity from Heavy-Duty Diesel Vehicles

ABSTRACT

Diesel particulate matter (PM) is a significant contributor to ambient air PM₁₀ and PM_2.5 particulate levels. In addition, recent literature argues that submicron diesel PM is a pulmonary health hazard. There is difficulty in attributing PM emissions to specific operating modes of a diesel engine, although it is acknowledged that PM production rises dramatically with load and that high PM emissions occur during rapid load increases on turbocharged engines. Snap-acceleration tests generally identify PM associated with rapid transient operating conditions, but not with high load. To quantify the origin of PM during transient engine operation, continuous opacity measurements have been made using a Wager 650CP full flow exhaust opacity meter. Opacity measurements were taken while the vehicles were operated over transient driving cycles on a chassis dynamometer using the West Virginia University (WVU) Transportable Heavy Duty Vehicle Emissions Testing Laboratories. Data were gathered from Detroit Diesel, Cummins, Caterpillar, and Navistar heavy-duty (HD) diesel engines. Driving cycles used were the Central Business District (CBD) cycle, the WVU 5-Peak Truck cycle, the WVU 5-Mile route, and the New York City Bus (NYCB) cycle. Continuous opacity measurements, integrated over the entire driving cycle, were compared to total integrated PM mass. In addition, the truck was subjected to repeat snap-acceleration tests, and PM was collected for a composite of these snap-acceleration tests. Additional data were obtained from a fleet of 1996 New Flyer buses in Flint, MI, equipped with electronically controlled Detroit Diesel Series 50 engines. Again, continuous opacity, regulated gaseous emissions, and PM were measured. The relationship between continuous carbon monoxide (CO) emissions and continuous opacity was noted. In identifying the level of PM emissions in transient diesel engine operation, it is suggested that CO emissions may prove to be a useful indicator and may be used to apportion total PM on a continuous basis over a transient cycle. The projected continuous PM data will prove valuable in future mobile source inventory prediction.     

Unregulated greenhouse gas and ammonia emissions from current technology heavy-duty vehicles

The study presents the measurement of carbonyl, BTEX (benzene, toluene, ethyl benzene, and xylene), ammonia, elemental/organic carbon (EC/OC), and greenhouse gas emissions from modern heavy-duty diesel and natural gas vehicles. Vehicles from different vocations that included goods movement, refuse trucks, and transit buses were tested on driving cycles representative of their duty cycle. The natural gas vehicle technologies included the stoichiometric engine platform equipped with a three-way catalyst and a diesel-like dual-fuel high-pressure direct-injection technology equipped with a diesel particulate filter (DPF) and a selective catalytic reduction (SCR). The diesel vehicles were equipped with a DPF and SCR. Results of the study show that the BTEX emissions were below detection limits for both diesel and natural gas vehicles, while carbonyl emissions were observed during cold start and low-temperature operations of the natural gas vehicles. Ammonia emissions of about 1 g/mile were observed from the stoichiometric natural gas vehicles equipped with TWC over all the driving cycles. The tailpipe GWP of the stoichiometric natural gas goods movement application was 7% lower than DPF and SCR equipped diesel. In the case of a refuse truck application the stoichiometric natural gas engine exhibited 22% lower GWP than a diesel vehicle. Tailpipe methane emissions contribute to less than 6% of the total GHG emissions.

Implications: Modern heavy-duty diesel and natural gas engines are equipped with multiple after-treatment systems and complex control strategies aimed at meeting both the performance standards for the end user and meeting stringent U.S. Environmental Protection Agency (EPA) emissions regulation. Compared to older technology diesel and natural gas engines, modern engines and after-treatment technology have reduced unregulated emissions to levels close to detection limits. However, brief periods of inefficiencies related to low exhaust thermal energy have been shown to increase both carbonyl and nitrous oxide emissions.     

Idle Emissions from Heavy-Duty Diesel and Natural Gas Vehicles at High Altitude

ABSTRACT

Idle emissions of total hydrocarbon (THC), CO, NO_x, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NO_x, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NO_x, and 0.048 g/min PM.

Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NO_x emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NO_x emissions increased with Health and Environment; June 30, 1999 (available from the authors).     

Factors affecting heavy-duty diesel vehicle emissions

Societal and governmental pressures to reduce diesel exhaust emissions are reflected in the existing and projected future heavy-duty certification standards of these emissions. Various factors affect the amount of emissions produced by a heterogeneous charge diesel engine in any given situation, but these are poorly quantified in the existing literature. The parameters that most heavily affect the emissions from compression ignition engine-powered vehicles include vehicle class and weight, driving cycle, vehicle vocation, fuel type, engine exhaust aftertreatment, vehicle age, and the terrain traveled. In addition, engine control effects (such as injection timing strategies) on measured emissions can be significant. Knowing the effect of each aspect of engine and vehicle operation on the emissions from diesel engines is useful in determining methods for reducing these emissions and in assessing the need for improvement in inventory models. The effects of each of these aspects have been quantified in this paper to provide an estimate of the impact each one has on the emissions of diesel engines.     

Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data

The methodology laid out in this paper shows that typical operational data from vehicle fleets monitored by a global positioning system (GPS) can be used to estimate heavy-duty diesel vehicle (HDDV) emissions, thereby enabling waste managers and governing bodies to internalize the responsibility for socioenvironmental costs traditionally absorbed by external parties. Although municipal solid waste (MSW) collection vehicles are the subjects of this particular study, the methodology presented here can be applied to any fleet of vehicles monitored by GPS. This study indicates that MSW collection trucks may be considerably less fuel efficient in the field than published values for HDDV fuel efficiency suggest. The average fuel efficiency of one MSW collection truck was estimated as 0.90 +/- 0.44 km/L (2.12 +/- 1.03 mi/gal). This same truck would generate approximately 42 metric tons of CO2 equivalents/yr, which is comparable to the greenhouse gas emissions of a large sport utility vehicle driving six times the distance, in town, for a year. In terms of the impacts such emissions have, projections for the monetary cost of emissions are available but highly variable. They suggest that the external monetary costs of emissions range between 6 and 39% of the annual fuel costs for the studied MSW collection truck. The results of this study indicate a need for further research into valuation of the hidden, external costs of emissions, borne by local and global socioecological communities. The possible implications of this result include poorly advised fleet procurement decisions and underestimation of MSW collection fleet emissions.     

Emission factors for high-emitting vehicles based on on-road measurements of individual vehicle exhaust with a mobile measurement platform

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO(x)), and particle mass with aerodynamic diameter below 2.5 microm (PM2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO(x), black carbon (BC), PM2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO(x) and CO fleet-average emission factors and more than 60% of PM2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM2.5, and NO(x) tended to cluster among the same vehicles.     

Remote Sensing Data and a Potential Model of Vehicle Exhaust Emissions

In June 1991, General Motors Research and Development Center (GMR&D) participated in a remote sensing study conducted by the California Air Resources Board and the U. S. Environmental Protection Agency. During this study, the GMR&D remote sensor was used to measure the carbon monoxide (CO) and hydrocarbon (HC) emissions from approximately 15,000 vehicles. The vehicle type (passenger car, light-duty truck, or medium/heavy-duty truck), manufacturer, and model year were identified for each vehicle by acquiring registration data from the state of California. Analyses were performed separately for each vehicle type and for passenger cars by separate model years. The data indicate that the passenger cars with the highest 10% of CO emissions generated approximately 58% of the total CO from all cars. Similarly, the 10% highest HC-emitting cars generated 65% of the total HC from cars. It was found that for each model year of vehicle, the distribution of emission concentrations followed a logarithmic relationship. The logarithmic functions that describe these relationships can be used to estimate the fraction of vehicles that emitted at or above any given concentration of CO or HC. However, these logarithmic functions only describe measured distributions for vehicles emitting more than 1% CO and 0.015% HC.     

Tomato Root: Site of Initial Alteration in Minor Elements in Cadmium Treated Plants

Abstract

Societal and governmental pressures to reduce diesel exhaust emissions are reflected in the existing and projected future heavy-duty certification standards of these emissions. Various factors affect the amount of emissions produced by a heterogeneous charge diesel engine in any given situation, but these are poorly quantified in the existing literature. The parameters that most heavily affect the emissions from compression ignition engine-powered vehicles include vehicle class and weight, driving cycle, vehicle vocation, fuel type, engine exhaust aftertreatment, vehicle age, and the terrain traveled. In addition, engine control effects (such as injection timing strategies) on measured emissions can be significant. Knowing the effect of each aspect of engine and vehicle operation on the emissions from diesel engines is useful in determining methods for reducing these emissions and in assessing the need for improvement in inventory models. The effects of each of these aspects have been quantified in this paper to provide an estimate of the impact each one has on the emissions of diesel engines.     

Determining gaseous emission factors and driver's particle exposures during traffic congestion by vehicle-following measurement techniques

Vehicle gaseous emissions (NO, CO, CO2, and hydrocarbon [HC]) and driver's particle exposures (particulate matter < 1 microm [PM1], < 2.5 microm [PM2.5], and < 10 microm [PM10]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People's Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver's particle exposures, the inlets of the particle analyzers were mounted at the height of the driver's breathing position in the mobile laboratory, with the driver's window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver's particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO2, and nitrogen oxide (NO(x)) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO(x). The impact of urban canyons is shown to cause a significant increase in the PM1 peak. The background concentrations contributed a significant amount of the driver's particle exposures.     

Idle emissions from heavy-duty diesel vehicles: review and recent data

Heavy-duty diesel vehicle idling consumes fuel and reduces atmospheric quality, but its restriction cannot simply be proscribed, because cab heat or air-conditioning provides essential driver comfort. A comprehensive tailpipe emissions database to describe idling impacts is not yet available. This paper presents a substantial data set that incorporates results from the West Virginia University transient engine test cell, the E-55/59 Study and the Gasoline/Diesel PM Split Study. It covered 75 heavy-duty diesel engines and trucks, which were divided into two groups: vehicles with mechanical fuel injection (MFI) and vehicles with electronic fuel injection (EFI). Idle emissions of CO, hydrocarbon (HC), oxides of nitrogen (NOx), particulate matter (PM), and carbon dioxide (CO2) have been reported. Idle CO2 emissions allowed the projection of fuel consumption during idling. Test-to-test variations were observed for repeat idle tests on the same vehicle because of measurement variation, accessory loads, and ambient conditions. Vehicles fitted with EFI, on average, emitted approximately 20 g/hr of CO, 6 g/hr of HC, 86 g/hr of NOx, 1 g/hr of PM, and 4636 g/hr of CO2 during idle. MFI equipped vehicles emitted approximately 35 g/hr of CO, 23 g/hr of HC, 48 g/hr of NOx, 4 g/hr of PM, and 4484 g/hr of CO2, on average, during idle. Vehicles with EFI emitted less idle CO, HC, and PM, which could be attributed to the efficient combustion and superior fuel atomization in EFI systems. Idle NOx, however, increased with EFI, which corresponds with the advancing of timing to improve idle combustion. Fuel injection management did not have any effect on CO2 and, hence, fuel consumption. Use of air conditioning without increasing engine speed increased idle CO2, NOx, PM, HC, and fuel consumption by 25% on average. When the engine speed was elevated from 600 to 1100 revolutions per minute, CO2 and NOx emissions and fuel consumption increased by >150%, whereas PM and HC emissions increased by approximately 100% and 70%, respectively. Six Detroit Diesel Corp. (DDC) Series 60 engines in engine test cell were found to emit less CO, NOx, and PM emissions and consumed fuel at only 75% of the level found in the chassis dynamometer data. This is because fan and compressor loads were absent in the engine test cell.     

Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO₂), oxides of nitrogen (NO_X), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NO_X/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NO_X/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NO_X threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles.

Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.     

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.