Assessing the real-world performance of modern pollutant abatement systems on motorcycles

The present statutory pollutant emission limits Euro-3 for motorcycles imply the use of modern emission abatement systems such as three-way catalytic converters. Determining the quality of implementation of these new systems in different driving situations such as real-world driving is important, since motorcycles are commonly used for personal transportation in urban areas. For this reason, a test bench series was carried out with a sample of 10 motorcycles of state-of-the-art certification category Euro-3. Emission factors of regulated pollutants and CO₂ are presented on the basis of the statutory driving cycle, the latest version of the real-world Worldwide Motorcycle Test Cycle (WMTC) and the real-world Common Artemis Driving Cycle (CADC).The results of the statutory driving cycle show that 7 out of 10 motorcycles fail to comply with the present emission limits. The results of both real-world driving cycles confirm notable emissions of HC in urban and NO_x in motorway driving conditions. CO emissions of motorcycles with small displacement increase significantly in the urban and extra-urban sections of the CADC, which has a more dynamic velocity profile than the equivalent WMTC. Although pollutant emissions of motorcycles show a marked improvement compared with earlier certification classes, they clearly exceed the emission levels of modern light gasoline passenger cars, especially for CO and HC.     

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.     

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%.     

Effects of Fuel Type,Driving Cycle,and Emission Status on In-Use Vehicle Exhaust Reactivity

ABSTRACT

The introduction of reformulated gasolines significantly reduced exhaust hydrocarbon (HC) mass emissions, but few data are available concerning how these new fuels affect exhaust reactivity. Similarly, while it is well established that high-emitting vehicles contribute a significant portion of total mobile source HC mass emissions, it is also important to evaluate the exhaust reactivity from these vehicles. The objective of this study was to evaluate the relative influence on in-use vehicle exhaust reactivity of three critical factors: fuel, driving cycle, and vehicle emission status. Nineteen in-use vehicles were tested with seven randomly assigned fuel types and two driving cycles: the Federal Test Procedure (FTP) and the Unified Cycle (UC). Total exhaust reactivity was not statistically different between the FTP and UC cycles but was significantly affected by fuel type. On average, the exhaust reactivity for California Phase 2 fuel was the lowest (16 % below the highest fuel type) among the seven fuels tested for cold start emissions. The average exhaust reactivity for high-emitting vehicles was significantly higher for hot stabilized (11%) and hot start (15%) emissions than for low-emitting vehicles. The exhaust reactivities for the FTP and UC cycles for light-end HCs and carbonyls were significantly different for the hot stabilized mode. There was a significant fuel effect on the mean specific reactivity (SR) for the mid-range HCs, but not for light-end HCs or carbonyls, while vehicle emission status affected the mean SR for all three HC compound classes.     

Analysis of Remote Sensing Errors of Omission and Commission Under FTP Conditions

Abstract

Second-by-second modal emissions data from a 73-vehicle fleet of 1990 and 1991 light duty cars and trucks driven on the Federal Test Procedure (FTP) driving cycle were examined to determine remote sensing errors of commission in identifying high emissions vehicles. Results are combined with a similar analysis of errors of omission based on modal FTP data from high emissions vehicles. Extremely low errors of commission combined with modest errors of omission indicate that remote sensing should be very effective in isolating high CO and HC emitting vehicles in a fleet of late model vehicles on the road.     

Effects of diesel/biodiesel blends on regulated and unregulated pollutants from a passenger vehicle operated over the European and the Athens driving cycles

This paper presents the regulated and unregulated exhaust emissions of a diesel passenger vehicle, operated with low sulphur automotive diesel and soy methyl ester blends. Emission and fuel consumption measurements were conducted under real driving conditions (Athens Driving Cycle, ADC) and compared with those of a modified New European Driving Cycle (NEDC) using a chassis dynamometer. A Euro II compliant diesel vehicle was used in this study, equipped with an indirect injection diesel engine, fuelled with diesel fuel and biodiesel blends at proportions of 5, 10, and 20% respectively. Unregulated emissions of 11 polycyclic aromatic hydrocarbons (PAHs), 5 nitro-PAHs, 13 carbonyl compounds (CBCs) and the soluble organic fraction (SOF) of the particulate matter were measured. Qualitative hydrocarbon analysis was also performed on the SOF. Regulated emissions of NO_x, CO, HC, CO₂, and PM were also measured over the two test cycles. It was established that some of the emissions measured over the (hot-start) NEDC differed from the real-world cycle. Significant differences were also observed in the vehicle's fuel consumption between the two test cycles. The addition of biodiesel reduced the regulated emissions of CO, HC and PM, while an increase in NO_x was observed over the ADC. Carbonyl emissions, PAHs and nitro-PAHs were reduced with the addition of biodiesel over both driving cycles.     

Development of the typical driving cycle for buses in Hanoi,Vietnam

This paper develops a typical driving cycle for buses in Hanoi that does not require the deconstruction of the natural driving patterns. Real velocity–time data were collected along 15 routes in the inner city. The raw velocity–time series were preprocessed to remove errors, and smooth and denoise the data. These data, then, were tested for stationary behavior before being used in the construction of the driving cycle based on Markov chain theory. The 14 representative parameters of the driving cycle, including vehicle-specific power, which were extracted from 33 driving cycle parameters using the hierarchical agglomerative clustering method, were used to integrate the features of realistic driving patterns into the typical driving cycle. The conformity of the developed driving cycle with the real-world driving data was evaluated by the speed–acceleration frequency distribution (SAFD). A typical driving cycle for buses in Hanoi with a SAFD of 13.2% was developed. This is the first driving cycle developed for buses in Vietnam.

Implications: A typical driving cycle was developed for the first time for buses in Hanoi. With the deviation in speed-acceleration frequency distribution (SAFD) reaching to 13.2%, the developed driving cycle reflects well the overall real-world driving data in the city. This driving cycle, therefore, can be applied for the development of the country-specific emission factors and emission inventories for buses which are a very good tool as well as useful information for integrated air quality management in Hanoi.     

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.     

Construction of electric vehicle driving cycle for studying electric vehicle energy consumption and equivalent emissions

Environmental Science and Pollution Research - This paper proposes an effective and scientific method for the construction of a representative driving cycle for electric vehicles (EV) and takes it...     

A fuel-based approach for emission factor development for highway paving construction equipment in China

The objective of this paper is to develop and demonstrate a fuel-based approach for emissions factor estimation for highway paving construction equipment in China for better accuracy. A highway construction site in Chengdu was selected for this study with NO emissions being characterized and demonstrated. Four commonly used paving equipment, i.e., three rollers and one paver were selected in this study. A portable emission measurement system (PEMS) was developed and used for emission measurements of selected equipment during real–world highway construction duties. Three duty modes were defined to characterize the NO emissions, i.e., idling, moving, and working. In order to develop a representative emission factor for these highway construction equipment, composite emission factors were estimated using modal emission rates and the corresponding modal durations in the process of typical construction duties. Depending on duty mode and equipment type, NO emission rate ranged from 2.6–63.7mg/s and 6.0–55.6g/kg–fuel with the fuel consumption ranging from 0.31–4.52 g/s correspondingly. The NO composite emission factor was estimated to be 9–41mg/s with the single-drum roller being the highest and double-drum roller being the lowest and 6–30g/kg-fuel with the pneumatic tire roller being the highest while the double-drum roller being the lowest. For the paver, both time-based and fuel consumption-based NO composite emission rates are higher than all of the rollers with 56mg/s and 30g/kg-fuel, respectively. In terms of time–based quantity, the working mode contributes more than the other modes with idling being the least for both emissions and fuel consumption. In contrast, the fuel-based emission rate appears to have less variability in emissions. Thus, in order to estimate emission factors for emission inventory development, the fuel-based emission factor may be selected for better accuracy.

Implications: The fuel-based composite emissions factors will be less variable and more accurate than time-based emission factors. As a consequence, emissions inventory developed using this approach will be more accurate and practical.     

Development of a driving cycle for Hong Kong

The paper presents the results of the development of a standard driving cycle in the urban areas of Hong Kong. On-road speed–time data were collected by an instrumented diesel vehicle along two fixed routes located in two urban districts in Hong Kong. The collected data were analyzed and compared with mandatory driving cycles used elsewhere. It was found that none of these mandatory cycles could satisfactorily describe the driving characteristics in Hong Kong. A unique driving cycle was therefore developed for Hong Kong. The cycle was built up by extracting parts of the on-road speed data such that the summary statistics of the sample are close to that derived from the data population of the test runs.     

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.     

A modal approach to vehicular emissions and fuel consumption model development

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On-road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on-road measurements are feasible for the development of modal emission and fuel consumption models.     

Total life cycle emissions of post-Panamax containerships powered by conventional fuel or natural gas

This study proposes an easy-to-apply method, the Total Life Cycle Emission Model (TLCEM), to calculate the total emissions from shipping and help ship management groups assess the impact on emissions caused by their capital investment or operation decisions. Using TLCEM, we present the total emissions of air pollutants and greenhouse gases (GHGs) during the 25-yr life cycle of 10 post-Panamax containerships under slow steaming conditions. The life cycle consists of steel production, shipbuilding, crude oil extraction and transportation, fuel refining, bunkering, and ship operation. We calculate total emissions from containerships and compare the effect of emission reduction by using various fuels. The results can be used to differentiate the emissions from various processes and to assess the effectiveness of various reduction approaches. Critical pollutants and GHGs emitted from each process are calculated. If the containerships use heavy fuel oil (HFO), emissions of CO₂ total 2.79 million tonnes (Mt), accounting for 95.37% of total emissions, followed by NO_x and SO_x emissions,which account for 2.25% and 1.30%, respectively.The most significant emissions are from the operation of the ship and originate from the main engine (ME).When fuel is switched to 100% natural gas (NG), SO_x, PM₁₀, and CO₂ emissions show remarkable reductions of 98.60%, 99.06%, and 21.70%, respectively. Determining the emission factor of each process is critical for estimating the total emissions. The estimated emission factors were compared with the values adopted by the International Maritime Organization (IMO).The proposed TLCEM may contribute to more accurate estimates of total life cycle emissions from global shipping.

Implications: We propose a total life cycle emissions model for 10 post-Panamax container ships. Using heavy fuel oil, emissions of CO₂ total 2.79 Mt, accounting for approximately 95% of emissions, followed by NO_x and SO_x emissions. Using 100% natural gas, SO_x, PM₁₀, and CO₂ emissions reduce by 98.6%, 99.1%, and 21.7%, respectively. NO_x emissions increase by 1.14% when running a dual fuel engine at low load in natural gas mode.     

Development of molecular marker source profiles for emissions from on-road gasoline and diesel vehicle fleets

As part of the Gasoline/Diesel PM Split Study, relatively large fleets of gasoline vehicles and diesel vehicles were tested on a chassis dynamometer to develop chemical source profiles for source attribution of atmospheric particulate matter in California's South Coast Air Basin. Gasoline vehicles were tested in cold-start and warm-start conditions, and diesel vehicles were tested through several driving cycles. Tailpipe emissions of particulate matter were analyzed for organic tracer compounds, including hopanes, steranes, and polycyclic aromatic hydrocarbons. Large intervehicle variation was seen in emission rate and composition, and results were averaged to examine the impacts of vehicle ages, weight classes, and driving cycles on the variation. Average profiles, weighted by mass emission rate, had much lower uncertainty than that associated with intervehicle variation. Mass emission rates and elemental carbon/organic carbon (EC/OC) ratios for gasoline vehicle age classes were influenced most by use of cold-start or warm-start driving cycle (factor of 2-7). Individual smoker vehicles had a large range of mass and EC/OC (factors of 40 and 625, respectively). Gasoline vehicle age averages, data on vehicle ages and miles traveled in the area, and several assumptions about smoker contributions were used to create emissions profiles representative of on-road vehicle fleets in the Los Angeles area in 2001. In the representative gasoline fleet profiles, variation was further reduced, with cold-start or warm-start and the representation of smoker vehicles making a difference of approximately a factor of two in mass emission rate and EC/OC. Diesel vehicle profiles were created on the basis of vehicle age, weight class, and driving cycle. Mass emission rate and EC/OC for diesel averages were influenced by vehicle age (factor of 2-5), weight class (factor of 2-7), and driving cycle (factor of 10-20). Absolute and relative emissions of molecular marker compounds showed levels of variation similar to those of mass and EC/OC.     

Modelling fuel consumption and pollutant emissions based on real-world driving patterns: the HBEFA approach

Emissions of passenger cars and light-duty vehicles with complex exhaust gas after-treatment are difficult to predict, especially if the prediction is only based on kinematic parameters without vehicle-specific data. A new method for modelling fleet emission factors based on testbench data is presented. It has been used for modern passenger cars and light-duty vehicles (EURO-2 and -3) in the new version 2.1 of the German-Austrian-Swiss Handbook Emission Factors for Road Transport (HBEFA). The new method, not relying on vehicle-specific data, avoids decomposing the measured real-world driving behaviour and all associated uncertainties. Emission factors can be predicted for any given driving pattern which is characterised through kinematic parameters or representative time series of vehicle speed. The methodology determines the linear combination of measured driving patterns that is most representative for the driving pattern whose emissions are to be predicted. The approach is illustrated using testbench real-world measurements of 44 passenger cars of technology stages EURO-2 and -3.     

A Modal Approach to Vehicular Emissions and Fuel Consumption Model Development

Abstract

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On‐road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on‐road measurements are feasible for the development of modal emission and fuel consumption models.     

Fast and quantitative measurement of benzene,toluene and C2-benzenes in automotive exhaust during transient engine operation with and without catalytic exhaust gas treatment

Time-Resolved Chemical Ionization Mass Spectrometry (CIMS) has been used to investigate the emission profiles of benzene, toluene and the C₂-benzenes (xylenes and ethyl benzene) in automotive exhaust during transient engine operation. On-line emission measurements with a frequency of 1–5 Hz clearly identified the critical driving conditions that are mainly responsible for the overall aromatic hydrocarbon emissions. The passenger car, equipped with a catalytic converter showed significant BTXE-emissions only in the first part of the New European Driving Cycle (NEDC) due to sub-optimal catalyst temperature. On the same car without a catalytic converter, emissions of aromatic hydrocarbons were detected over the entire test run and the benzene–toluene mixing ratios of the exhaust gas were rather constant. With catalytic exhaust gas treatment the observed benzene–toluene mixing ratios varied to a greater extent reflecting predominantly different catalytic converter conditions. The average molar ratio of benzene over toluene rose from 0.33 to 0.53 upon exhaust gas treatment. With catalytic converter the emissions during extra urban (EUDC) driving repeatedly showed benzene–toluene mixing ratios >1 and an average molar benzene/toluene ratio of 0.74 was detected during the EUDC part of the driving cycle. Whereas the total hydrocarbon (T.HC) emissions were decreased by 83% upon exhaust gas treatment the overall reduction of the benzene emissions was only 70%.     

On-road motor vehicle emissions and fuel consumption in urban driving conditions

This paper reports on the analysis of on-road vehicle speed, emission, and fuel consumption data collected by four instrumented vehicles. Time-, distance-, and fuel-based average fuel consumption, as well as CO, HC, NOx, and soot emission factors, were derived. The influences of instantaneous vehicle speed on emissions and fuel consumption were studied. It was found that the fuel-based emission factors varied much less than the time- and distance-based emission factors as instantaneous speed changed. The trends are similar to the results obtained from laboratory tests. The low driving speed contributed to a significant portion of the total emissions over a trip. Furthermore, the on-road data were analyzed using the modal approach. The four standard driving modes are acceleration, cruising, deceleration, and idling. It was found that the transient driving modes (i.e., acceleration and deceleration) were more polluting than the steady-speed driving modes (i.e., cruising and idling) in terms of g/km and g/sec. These results indicated that the on-road emission measurement is feasible in deriving vehicle emissions and fuel consumption factors in urban driving conditions.     

Ammonia emissions from a representative in-use fleet of light and medium-duty vehicles in the California South Coast Air Basin

We used Fourier Transform Infrared Spectroscopy (FTIR) to measure tailpipe ammonia emissions from a representative fleet of 41 light and medium-duty vehicles recruited in the California South Coast Air Basin. A total of 121 chassis dynamometer emissions tests were conducted on these vehicles and the test results were examined to determine the effects of several key variables on ammonia emissions. Variables included vehicle type, driving cycle, emissions technology, ammonia precursor emissions (i.e. CO and NOx) and odometer readings/model year as a proxy for catalyst age. The mean ammonia emissions factor was 46 mg km^?1 (σ = 48 mg km^?1) for the vehicle fleet. Average emission factors for specific vehicle groups are also reported in this study. Results of this study suggest vehicles with the highest ammonia emission rates possess the following characteristics: medium-duty vehicles, older emissions technologies, mid-range odometer readings, and higher CO emissions. In addition, vehicles subjected to aggressive driving conditions are likely to be higher ammonia emitters. Since the vehicles we studied were representative of recent model year vehicles and technologies in urban airsheds, the results of our study will be useful for developing ammonia emissions inventories in Los Angeles and other urban areas where California-certified vehicles are driven. However, efforts should also be made to continue emissions testing on in-use vehicles to ensure greater confidence in the ammonia emission factors reported here.