INTRODUCTION
EMISSION DATA BASE
- S PECIFIC MEASUREMENTS
- Pollutants considered
- Vehicle sample
- Driving cycles used
- Test sequence
- O THER A RTEMIS MEASUREMENTS
- E XTERNAL DATA
- A RTEMIS LIGHT VEHICLE EMISSION MEASUREMENT DATABASE (A RTEMIS LVEM DB)
- Objectives
- Building
- Data submission
- Data harmonisation
- Content
- Public availability
A final goal of the database is to be easily supplemented by new emission measurements in the future. The current version of the Artemis LVEM database contains data from 2847 passenger cars and light duty vehicles, measured from 1980 to 2004.
EMISSION MODELLING
G ENERAL SHAPE
- Shape of the emission model
- Pollutants and units
- Volatile organic compounds VOCs considered
- Polyaromatics PAH considered according to their toxicity
- Nitrogen oxydes
- Particulates
Naphthalene belongs to the second list, but was already included in the group of the 4 most volatile PAHs. The benzo[a]pyrene (BaP) belongs to both groups of the 12 least volatile and 6 most carcinogenic PAH.
I NSTANTANEOUS MODELS
- EMPA model
- P HEM model
- Conclusion of instantaneous models
The results show excellent prediction quality for gasoline engine emissions (Figure 4). Validation of the model for simulating different road gradients was done by Zallinger and Hausberger (2004).
K INEMATIC REGRESSION MODEL
- Data
- Method
- Results
When we look at fleets (groups) of vehicles, the quality of the models improves compared to the individual vehicle, even with a small number of vehicles. A logarithm transformation was applied to the response Yi (i.e. the emissions), so the model predicted quantities fitting data such as lnY must be retransformed to the original scale to obtain emission factors expressed in g/km.
T RAFFIC SITUATIONS MODEL
- Traffic situation definition
- Speed data representative of the traffic situations
- Emission data harmonization through Reference test patterns
- Emission factors of Reference test patterns
For each vehicle, emission data were analyzed by average cycle speed. Then the coefficients a', b' and c' were expressed according to the average speed of the speed zone (see an example Figure 23).
Emission factors of Traffic situations
The distances between a traffic situation (represented by its speed curve) and the test cycles made it possible to identify the nearest test patterns and consider each traffic situation as a linear combination of the reference test patterns, proportional to the proximity – in terms of kinematics – to these test patterns. A set of weighting coefficients for each traffic situation was determined according to the 15 reference test patterns given in the appendix.
Emission factors of macro traffic situations
These emission factors are illustrated in Figure 12 according to some vehicle classes and for the four traffic conditions described in Figure 8. The use of model 1 to calculate these emission factors is possible, but requires a new calculation of the combination factors (compared to those given in Appendix 19).
A VERAGE SPEED MODEL
- Design through speed range averages
- Design through Reference test patterns
- Comparison of the two average speed models
- Reduction factors for future technologies
The homogenization of the data with respect to vehicle mileage, performed only in the second method, provides fully standardized emission factors. In the second method, the equation is chosen mainly to avoid going outside the enveloping curve of the points.
U NREGULATED POLLUTANTS OF PASSENGER CARS
- Homogeneity of the VOC and PAH emission data
- VOC and PAH emission factors
- NO 2 emission factors
- Emission factors for particle properties
The VOC emission factors decrease drastically from pre-Euro 1 to Euro 1 petrol cars (on average by an order of magnitude). The aggregated average of the vehicle emissions in each category was used to derive the emission factors.
L IGHT D UTY V EHICLES
- Data extraction and classification
- Emission as a function of average speed
- Emission as a function of vehicle loading rate
- Conclusion
The vehicles that verified Pearson's test were separated into 3 groups as a function of the coefficient of determination obtained. The second verification consisted of comparing the values calculated by the equation with the values of the emission curves. The comparisons were also classified into 4 groups as a function of the validation of Pearson's test and of the coefficient of determination obtained.
This shows that the load parameter has a significant impact on the accuracy of the emission factor equation. This is the case for 32% and 25% of the emission factors calculated for diesel and petrol vehicles respectively. This method was used to statistically validate 97 % and 96 % of the emission factors of diesel and petrol LDV respectively.
I NFLUENCE OF MILEAGE
For these groups, it was possible to determine an equation that is highly representative of the group, using average velocity and/or strain rate as the only parameters. Regarding the remaining vehicles, another factor needed to be examined, although only 3% and 4% of the equations were not confirmed by the Pearson test. For each of the resulting equations, and especially for those involving a load factor, it was necessary to model the emission behavior outside the studied load range.
In other cases, the calculation is performed based on the equation for each load or speed. By updating the data in Artemis and calculating the emission factors using only the average speed as a parameter, a slight improvement in the coefficients of determination could be observed as it was relevant. However, the testing of the obtained emission factors should continue and their equations improved, if necessary, using additional tools added during updates of the Artemis LVEM database.
I NFLUENCE OF AMBIENT AIR TEMPERATURE AND HUMIDITY
- Influence of ambient air temperature
- Influence of ambient air humidity
Worldwide, mileage has no effect on CO2 emissions either on diesel car emissions, but greatly increases CO, HC and NOx emissions from petrol cars: between 0 and 100,000 km these emissions increase by a factor of 3.6 on average per km. Euro 1 and 2 vehicles, but only with 15% for Euro 3 and 4 vehicles (see an example, Figure 26). However, both groups of petrol cars would need much stronger correction, as the relative change over the permitted humidity range is around 35% for the Euro 2 to and over 55% for the Euro 3 test fleet, and the normative factor is only corrected by some 20% before for the same humidity range. All linear correction models developed here are almost on top of each other, and the required correction is less than 20%, even somewhat less than the standard method provides.
CO for petrol Euro 2 vehicles in urban situation - HC for diesel cars and for petrol Euro 2 cars - HC for petrol Euro 3 cars in urban situation. In the case of NOx emissions, the influence of the humidity is expressed by the formulas emission H( )1. It is recommended to use the rural figures for highway driving behavior, and to use the petrol Euro 2 figures for petrol Euro 0 and 1, petrol Euro 3 figures for petrol Euro 4, and diesel Euro 2 figures for the other diesel cases.
I NFLUENCE OF ROAD GRADIENT AND VEHICLE LOAD
- Measurement results
- Comparison with other sources
- Combination of road gradient and vehicle loading
- Final correction factors
- Conclusion and discussion
Due to the small sample and the different vehicles (engine power and capacity) in this sample it is not reasonable to calculate the average emission for diesel and gasoline for the different road gradients. For the respective emissions of diesel vehicles (NOx, PM and FC) the influence of the loading situation can be seen and thus it is possible to generate load factors for these emissions. Due to the small sample, the results for emissions are again within the repeatability range.
For the calculation of three different driving situations (urban, rural and highway) the Artemis driving cycle was chosen. In the case of diesel vehicles, for NOx, PM and fuel consumption, the agreement of simulation with measurement is good (see Figure 33). The impact of loading rate on emissions and fuel consumption of gasoline vehicles is more or less the same at different road gradients.
I NFLUENCE OF AUXILIARIES OF PASSENGER CARS
- Emission database and analysis of effects on fuel consumption and CO 2
- A physical model for air conditioning effects
- Simplified model of excess fuel consumption and weather data
- Excess pollutants emissions analysis
- Conclusion
Therefore, we assume that the fuel consumption of AC does not depend on technical parameters. Heat exchanges that control cabin temperature are due to the global heat exchange coefficient, UA (W.m-2.K-1), the untreated air flow rate due to permeability, mp (kg.s-1), the internal heat gain due to occupants and electrical equipment, Aint. A physical model of excess fuel consumption due to AC appears to be too complex to be implemented in a stock software like Artemis.
We proposed to use the same model by replacing the excess fuel consumption of AC with the excess fuel consumption of accessories. The various analyzes show that the excess fuel consumption expressed in l/h is quite independent of the speed or the traffic situation. The excess fuel consumption due to air conditioning is well known in hot conditions due to the large number of experiments.
C OLD START EMISSIONS OF PASSENGER CARS
- New method to calculate the absolute cold start excess emission
- Data considered
- Cold excess emission for a start
- The different cold start Artemis models
- Conclusion
This time tkoe is linked to the distance dkoe by the average speed of the driving cycle during the cold period. A linear regression model is then fitted to the cumulative emission data from the hot part of the cycle only, and the regression value at zero distance gives the cold-start emission value. Then we calculate the hot emission, the standard deviation and the linear regression of the cumulative hot emission.
The excess emission during a cold start is calculated as the difference between the value of a cycle starting in cold conditions and the value of the same cycle starting in warm conditions. The third model allows us to take into account the distribution of cold starts over the day. To see the relative influence of different parameters, Figure 46 shows the influence of the average speed on the cold start emission.
E VAPORATIVE EMISSIONS
Other illustrations are given in Annex 38 for ambient temperature, vehicle category, season and time. Nevertheless, ambient temperature, average speed and time of day play a major role. When the model user does not have access to the necessary statistical data, it is recommended that the most aggregated model (i.e. the third model) be used, which parallels the hot emission modeling with the same design.
Reasons for this can mainly be found in the stricter emissions legislation and the advanced testing procedure. This leads to the introduction of more advanced and sustainable technologies, which are monitored by on-board diagnostic systems. Thus, the main remaining sources of evaporative emissions in road traffic are old cars without a carbon canister and newer cars with fuel system failures.
CONCLUSION
A quantity of two milliliters of the diluted exhaust gas was injected directly into the gas chromatograph. The extracted material was then concentrated by evaporating the solvent in a rotary evaporator under vacuum to 1 cm3. Sampling: at the end of the CVS dilution tunnel using an Anasarb CSC cartridge filled with activated carbon.
Sampling: at the end of the dilution tunnel of the CVS using a two-zone cartridge filled with silica gel impregnated with 2.4 DPNH. Sampling: at the end of the dilution tunnel in the CVS using filtration (teflon wool) Analysis: Gas chromatography (HP 5890). Description of the traffic situation corresponding to Artemis driving cycles or sub-cycles and weighting factors for macro traffic situations according to these cycles.
Mid-latitude temperate, humid Cfa with an average temperature of the warmest month above 22°C Cfb similar to Cfa with a warmer colder month. Csa Mediterranean climate, with an average temperature of the warmest month above 22°C Csb similar to Csa with a warmer colder month.
Model of CO excess emission at full load
Model of HC excess emission at full load
Model of NOx excess emission at full load
Model of particulates excess emissions
