Presentation is loading. Please wait.

Presentation is loading. Please wait.

Leon Ntziachristos Dimitrios Gkatzoflias Charis Kouridis Giorgos Mellios Savvas Geivanidis Zissis Samaras COPERT 4 Copenhagen, 2008-06-19.

Similar presentations

Presentation on theme: "Leon Ntziachristos Dimitrios Gkatzoflias Charis Kouridis Giorgos Mellios Savvas Geivanidis Zissis Samaras COPERT 4 Copenhagen, 2008-06-19."— Presentation transcript:

1 Leon Ntziachristos Dimitrios Gkatzoflias Charis Kouridis Giorgos Mellios Savvas Geivanidis Zissis Samaras COPERT 4 Copenhagen,

2 Methodology and Comparison with the Guidebook
Contents Background & History Users and Uses Methodology and Comparison with the Guidebook New Elements compared to Spring/Summer 2007 Activity data (results of the Fleets project) Important, less important data Exhaust PM and airborne particle emission factors Non-exhaust PM

3 Background & History

4 Status of COPERT – Administrative Info
The name stands for COmputer Programme to calculate Emissions from Road Transport Now in its COPERT 4 Version (fourth update of the original COPERT 85) It incorporates results of several research and policy assessment projects It is basically funded by the European Environment Agency through the ETC budget It is scientifically and technically supported by the Lab of Applied Thermodynamics It has recently attracted much attention from the Joint Research Centre in Ispra who are willing to support its further development

5 Status of COPERT – Technical Info
Calculates emissions of all (important) pollutants from road transport Covers all (important) vehicle classes Can be applied in all European countries and in several Asian ones Can be used to produce total emission estimates from 1970 to 2020 (up to 2030 in TREMOVE) Provides a user-friendly (MS-Office like) GUI to introduce and view data

6 History - Early Generations

7 History - COPERT II and III
COPERT II was the first one with a GUI, built on MS Access 2 (1996). It provided emission factors up to Euro 1 COPERT III was based on menus, similar to MS Office (2000) and it was built on VBA for MS Access 97. Compared to version II: New hot emission factors for Euro 1 passenger cars New reduction factors over Euro 1 according to AutoOil Impact on emissions from 2000, 2005 fuel qualities Cold-start methodology for post Euro 1 PCs Emission degradation due to mileage Effect of leaded fuel ban in Europe Alternative evaporation methodology Detailed NMVOC speciation (PAHs, POPs, Dioxins and Furans) Updated hot emission factors for non regulated pollutants

8 History - COPERT 4 COPERT 4 is the ‘official’ version since Nov Main differences with Copert III include: Software-wise Possibility for time-series in one file Possibility of more than one scenarios in one file Enhanced import/export capabilities (mainly Excel) Configuration of fleet (local/regional vehicle technologies) Data can be changed at methodological level (emission functions) Methodology-wise Hot EFs for PCs and PTWs at post Euro 1 level Hybrid vehicle fuel consumption and emission factors N2O/NH3 Emission Factors for PCs and LDVs Particulate Matter and airborne particle emission factors Non-exhaust PM New evaporation methodology New corrections for emission degradation due to mileage HDV methodology (emission factors, load factors, road-gradient

9 Uses & Users

COPERT Usage Auto Oil II STEERS (CONCAWE) TERM ACEA TREMOVE COPERT TRENDS EMEP Guidebook National Inventories Individual Use Auto - Oil II: Forecast scenarios on behalf of ACEA to estimate emission evolution up to 2015 EMEP/CORINAIR: COPERT methodology is the road transport and off-road machinery emission chapter in the EMEP/CORINAIR Emission Inventory Guidebook EEA Activities: National and Central Estimates for Air Emissions from Road Transport TERM : Transport and Environment Reporting Mechanism (EEA) TRENDS: Development of a Database system for the Calculation of Indicators of Environmental Pressure Caused by Transport (DG TrEn study) supported by EEA

11 Field of applications - National level

12 Field of Applications – Literature 1(2)
Evaluation of COPERT Robin Smit, Muriel Poelman, Jeroen Schrijver, Improved road traffic emission inventories by adding mean speed distributions, Atmospheric Environment, Volume 42, Issue 5, February 2008, Pages Fabio Murena, Giuseppe Favale, Continuous monitoring of carbon monoxide in a deep street canyon, Atmospheric Environment, Volume 41, Issue 12, April 2007, Pages Spyros P. Karakitsios, Vasileios K. Delis, Pavlos A. Kassomenos, Georgios A. Pilidis, Contribution to ambient benzene concentrations in the vicinity of petrol stations: Estimation of the associated health risk, Atmospheric Environment, Volume 41, March 2007, Pages Ioannis Kioutsioukis, Stefano Tarantola, Andrea Saltelli, Debora Gatelli, Uncertainty and global sensitivity analysis of road transport emission estimates, Atmospheric Environment, Volume 38, Contains Special Issue section on Measuring the composition of Particulate Matter in the EU, December 2004, Pages M. Ekstrom, A. Sjodin, K. Andreasson, Evaluation of the COPERT III emission model with on-road optical remote sensing measurements, Atmospheric Environment, Volume 38, Contains Special Issue section on Measuring the composition of Particulate Matter in the EU, December 2004, Pages M. Pujadas, L. Nunez, J. Plaza, J. C. Bezares, J. M. Fernandez, Comparison between experimental and calculated vehicle idle emission factors for Madrid fleet, Science of The Total Environment, Volumes , Highway and Urban Pollution, December 2004, Pages R. Smit, A.L. Brown, Y.C. Chan, Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow?, Environmental Modelling & Software, Volume 23, October-November 2008, Pages Robert Joumard, Michel Andre, Robert Vidon, Patrick Tassel, Characterizing real unit emissions for light duty goods vehicles, Atmospheric Environment, Volume 37, Issue 37, 11th International Symposium, Transport and Air Pollution, December 2003, Pages Morten Winther, Petrol passenger car emissions calculated with different emission models, The Science of The Total Environment, Volume 224, Issues 1-3, 11 December 1998, Pages

13 Field of Applications – Literature 2
Leonidas Ntziachristos, Marina Kousoulidou, Giorgos Mellios, Zissis Samaras, Road-transport emission projections to 2020 in European Urban environments, Atmospheric Environment, October 2008, accepted. Rajiv Ganguly, Brian M. Broderick, Performance evaluation and sensitivity analysis of the general finite line source model for CO concentrations adjacent to motorways: A note, Transportation Research Part D: Transport and Environment, Volume 13, May 2008, Pages Hao Cai, Shaodong Xie, Estimation of vehicular emission inventories in China from 1980 to 2005, Atmospheric Environment, Volume 41, December 2007, Pages B.M. Broderick, R.T. O'Donoghue, Spatial variation of roadside C2-C6 hydrocarbon concentrations during low wind speeds: Validation of CALINE4 and COPERT III modelling, Transportation Research Part D: Transport and Environment, Volume 12, December 2007, Pages Seref Soylu, Estimation of Turkish road transport emissions, Energy Policy, Volume 35, Issue 8, Pages R. Bellasio, R. Bianconi, G. Corda, P. Cucca, Emission inventory for the road transport sector in Sardinia (Italy), Atmospheric Environment, Volume 41, February 2007, Pages Pavlos Kassomenos, Spyros Karakitsios, Costas Papaloukas, Estimation of daily traffic emissions in a South-European urban agglomeration during a workday. Evaluation of several 'what if' scenarios, Science of The Total Environment, Volume 370, November 2006, Pages G. Lonati, M. Giugliano, S. Cernuschi, The role of traffic emissions from weekends' and weekdays' fine PM data in Milan, Atmospheric Environment, Volume 40, Issue 31, 13th International Symposium on Transport and Air Pollution (TAP-2004), October 2006, Pages R. Berkowicz, M. Winther, M. Ketzel, Traffic pollution modelling and emission data, Environmental Modelling & Software, Volume 21, Issue 4. Jose M. Buron, Francisco Aparicio, Oscar Izquierdo, Alvaro Gomez, Ignacio Lopez, Estimation of the input data for the prediction of road transportation emissions in Spain from 2000 to 2010 considering several scenarios, Atmospheric Environment, Volume 39, Pages Jose M. Buron, Jose M. Lopez, Francisco Aparicio, Miguel A. Martin, Alejandro Garcia, Estimation of road transportation emissions in Spain from 1988 to 1999 using COPERT III program, Atmospheric Environment Volume 38, February 2004, Pages Roberto M. Corvalan, David Vargas, Experimental analysis of emission deterioration factors for light duty catalytic vehicles Case study: Santiago, Chile, Transportation Research Part D: Transport and Environment Volume 8, July 2003, Pages Salvatore Saija, Daniela Romano, A methodology for the estimation of road transport air emissions in urban areas of Italy, Atmospheric Environment Volume 36, Issue 34, November 2002, Pages C. Mensink, I. De Vlieger, J. Nys, An urban transport emission model for the Antwerp area, Atmospheric Environment, Volume 34, Issue 27, 2000, Pages

14 Notes: Information in this presentation collected from people that downloaded COPERT 4 in the period Jun 2006 – Nov 2007 In total, 1131 individual downloads (without doubles) were registered The registration is only for people that have actually downloaded COPERT, not just visiting the site. COPERT 4 Statistics The following form needs to be filled by users every time COPERT 4 is downloaded (example with artificial data is given). User's info: Name: John Smith Country: Italy Organization: University of Emissions Found out from: EEA Usage: Calculate pollutants emissions The following charts were produced by processing the information contained in these forms

15 Continent Distribution

16 Distribution of users from Europe

17 Distribution of users from Africa

18 Distribution of users from Asia

19 Distribution of users from America

20 Monthly Distribution of Downloads

21 Daily Distribution of Downloads

22 User Affiliation Private sector includes consultants, construction companies, emission and transport research, etc. International organizations include fuel, insurance and transport companies and authorities Local authorities mainly include regional environmental offices

23 Applications Academic use is for lectures, courses, theses
Evaluation / research : General application not specified in more detail by the users Emissions / emission factors: Application on particular studies necessitating total estimates or just derivation of emission factors

24 Summary of Copert (III) application – 1(3)
There is a great interest for national inventories Requires simplicity in interface and limited input from the user There is a great interest for GHGs emissions They require a link to higher-level software (i.e. IPCC tables, CollectER, etc.) Several new MSs and NIS countries still consider that input data are difficult to collect How to allocate technology classes How to estimate mileage and road shares Sometimes use “rule of thumb” methods of questionable quality

25 Summary of Copert (III) application – 2(3)
Several “advanced” countries hesitate using a common methodology Have developed own tools and are familiar with Trust own methods provide more accurate results than a generic model Politics and priorities may also play a role As a result: Countries’ absolute contribution may be misjudged Time-series reporting is less uncertain Introduction of a new model will require re-estimation in time series Such a model is a very elegant tool for centralised emission estimates

26 Summary of Copert (III) application - 3
Number of specialised uses is rather infinite In South Africa, it has been applied to a road 550 km between Durban and East London. Problem was level of maintenance In Chile and Mexico, it is used for urban inventories in high altitude Eurocontrol considers its use for estimating road transport contribution to local air quality in airport areas Particular cases (Greek taxis, small vehicle categories in Italy < 800 cc, technology classes in Eastern Europe, etc.)

27 Methodology and Comparison to the Guidebook

28 Pollutants which are estimated based on fuel consumption
Pollutants for which a detailed methodology exists, based on specific emission factors Pollutants which are estimated based on fuel consumption Group 1 Carbon monoxide (CO) Nitrogen oxides (NOx: NO and NO2) Volatile organic compounds (VOCs) Methane (CH4) Non-methane VOCs (NMVOCs) Nitrous oxide (N2O) Ammonia (NH3) Particulate matter (PM) PM number and surface area Group 2 Carbon dioxide (CO2) Sulphur dioxide (SO2) Lead (Pb) Cadmium (Cd) Chromium (Cr) Copper (Cu) Nickel (Ni) Selenium (Se) Zinc (Zn)

29 Pollutants which are derived as a fraction of total NMVOC emissions.
Pollutants for which a simplified methodology is applied, mainly due to the absence of detailed data Pollutants which are derived as a fraction of total NMVOC emissions. Group 4 Alkanes (CnH2n+2): Alkenes (CnH2n): Alkynes (CnH2n-2): Aldehydes (CnH2nO) Ketones (CnH2nO) Cycloalkanes (CnH2n) Aromatic compounds Group 3 Polycyclic aromatic hydrocarbons (PAHs) and persistent organic pollutants (POPs) Polychlorinated dibenzo dioxins (PCDDs) and polychlorinated dibenzo furans (PCDFs)

30 General Concept for Exhaust Emissions/Consumption
ECOLD [g/veh] = β x M [km] x EFHOT [g/km] x (eCOLD/eHOT-1) EHOT [g/veh] = M [km] x EFHOT [g/km] β = lCOLD/lTOTAL

31 What are exhaust emissions dependent on?
Activity Number of vehicles [veh.] Distance travelled [km/period of inventory] Hot Emissions Technology / Emission Standard Mean travelling speed [km/h] Cold Emissions Ambient temperature [Celsius] Mean trip distance [km]

32 Non-exhaust emissions (evaporation)
Breathing Losses Canister Vent Fuel Line Vapour Liquid Engine Fuel Tank Permeation / Leakages Mechanisms causing evaporation emissions Diurnal emissions Hot soak emissions Running losses Only relevant for Gasoline! Parked vehicle Engine running

33 What is evaporation dependant on
Vehicle technology Tank (vehicle) size Canister (vehicle) size Vehicle mileage (adsorption potential) Temperature variation Fuel vapour pressure (kPa) Fuel tank fill level Parking time distribution Trip duration

34 Particulate Matter due to road transport is also produced by:
Non – Exhaust PM Particulate Matter due to road transport is also produced by: Tyre abrasion Brake abrasion Road wear Emission rates depend on: Vehicle category (car, truck, motorcycle) Number of axles/wheels (trucks) Vehicle load Vehicle speed

35 Vehicle Categories – Heavy Duty Vehicles

36 Vehicle Categories – Rigid Trucks (Lorries)

37 Vehicle Categories – Articulated Vehicles
= + Tractor Semi-Trailer

38 New Elements (Compared to Spring/Summer 2007)
COPERT 4 VX.Y X… Methodology update Y… Software update

39 COPERT 4 V4.0 – October 2007 Consistent with the following EMEP/CORINAIR Guidebook chapters: B710: Road Transport (Activities – ) Ver. 6.0 B760: Fuel Evaporation (Activity ) Ver 2.1 Methodology issues added/updated in this version: Emissions from CNG Buses Emissions with use of Biodiesel Distinction of primary NOx emissions to NO2 and NO Emission factors of Euro 4 Diesel Passenger Cars Reductions for future emission standards Euro 5, Euro 6 and Euro V, Euro VI Revised CO2 calculation equations (biofuels and alternative fuels) Revised CH4 emission factors Corrected N2O and NH3 emission factors Revised calculation algorithm for CH4, N2O and NH3 hot/cold emissions

40 COPERT 4 Version 5.0 - December 2007
Consistent with the following EMEP/CORINAIR Guidebook chapters: B710: Road Transport (Activities – ) Ver. 6.0 with modified N2O emission factors for HDV B760: Fuel Evaporation (Activity ) Ver 3.0 B770: Road vehicle tyre and break wear (Activity ) Ver 1.0 Methodology issues added/updated in this version: Determination of the fraction of Elemental and Organic Carbon in exhaust PM New methodology to calculate evaporation emissions Inclusion of non-exhaust (tyre & break wear) PM Updated N2O emission factors for HDV

41 COPERT 4 Version 5.1 - February 2008
Mainly a software update (bug corrections and additions) Mileage used for N2O and NH3 emission degradation changed (was annual - > became cumulative) Different RVP and Temperature values per year can be imported from Excel Corrected mileage import from Excel Warning message on evaporation emissions removed Evaporation method now works also for negative temperature values

42 Activity Data (Results of the Fleets project)

43 Important, less important data

44 Guide to national road-transport inventory compilation - 1
List open to suggestions Obtain fuel consumption from national statistics Estimate effects of tank tourism, black market From 1 and 2 estimate true consumption of road transport Collect data on total fleet in operation per category National registers (cars, light trucks, heavy trucks, busses, motorcycles) Police (mopeds) Collect data on vehicle distribution per fuel and sub-category National registers Data from countries with similar structure (data from the Fleets project)

45 Guide to national road-transport inventory compilation - 2
Use age distributions to allocate vehicles to emission standards pre ECE vehicles up to 1971 ECE & to 1977 ECE to 1980 ECE to 1985 ECE to 1992 Euro to 1996 Euro to 2000 Euro to 2004 Euro to 2010 Use information on sales/new registrations Watch out for second-hand registrations Obtain average min and max monthly temperatures for major cities and produce average. Data can be found on websites (e.g as well. Estimate travelling speeds for urban areas (e.g. 25 km/h), rural areas (e.g. 60 km/h) and highways (e.g. 90 km/h). Estimation needs to be reasonable but not exact.

46 Guide to national road-transport inventory compilation - 3
Estimate mileage shares in the three modes. The sum should make up 100%. Reasonable but not exact estimation is required. Assume mileage values in the order of PCs: 11 – 15 Mm/year LDVs: 15 – 25 Mm/year HDVs: 50 – 80 Mm/year (national km only!) Busses: 50 – 70 Mm/year Mopeds: 2 – 5 Mm/year Motorycles: 4 – 8 Mm/year One could adjust mileage per age based on the ‘Fleets’ data Perform COPERT run Compare statistical with calculated fuel consumption per year Total fuel consumption Fuel consumption per fuel Adjust mileage to equalize calculated with statistical values

47 Hot Emission Factors of Regulated Pollutants from Conventional PCs – Example Comparison COPERT III and 4 – Euro 2 Diesel NOx

48 Hot Emission Factors of Regulated Pollutants from Conventional PCs – Example Comparison COPERT III and 4 – Euro 3 Gas CO

49 Typical Variability of Measured Data - CO

50 Typical Variability of Measured Data – CO2

51 Performance of Individual Vehicles

52 Importance of Input Variables
Parameter Importance Availability of statistics Notes /Particular Issues Total number of vehicles per class Question is the scooter and mopeds registration availability Distinction of vehicle to fuel used Question is the availability of records for vehicles retrofitted for alternative fuel use Distribution of cars/motorcycles to engine classes Not important for conventional pollutants, more important for CO2 emission estimates Distribution of heavy duty vehicles to weight classes Vehicle size important both for conventional pollutant and CO2 emissions Distinction of vehicles to technology level Imported, second-hand cars and scrappage rates are an issue Annual mileage driven Can be estimated from total fuel consumption. The effect of mileage with age requires attention. Urban driving speed Affects the emission factors Rural, highway driving speeds Little affect the emission factors, within their expected range of variation Mileage share in different driving modes Little affect emissions, within their expected range of variation

53 Exhaust Particulate Matter and Airborne Particle Emission Factors

54 Airborne Particle Information
Total Particle Number (7 nm – 1 μm) (negligible particle number above this range) Integrated active surface concentration of total particle population (7 nm – 1 μm) Number of solid particles of three different size ranges (value equivalent to PMP protocol) 7 – 50 nm 50 – 100 nm 10 nm – 1 μm Distinguished according to: Vehicle category Technology Aftertreatment Fuel Sulphur content (for non solid particles)

55 Examples of emission factors of active surface concentration and total particle number (solid and volatile particles)

56 Emission factors for solid particle number in the size ranges 7-50 nm, nm and 100 nm-1 μm (aerodynamic diameter)

57 PM Speciation (OC/EC) Definitions
Elemental Carbon (EC): It appears in PM samples mainly as graphitic particles formed in combustion. It is determined by thermal optical methods where carbon is converted to CO2. Black Carbon (BC): It corresponds to the light attenuation elements of carbon and it is determined by aethalometers. Black carbon is mainly EC. However, it also includes highly refractory elements of organic carbon (such as OCX2). Also, EC from different sources may exhibit different light absorption efficiencies, hence there is no global equivalence between BC and EC. Organic Carbon (OC): It is the carbon desorbed when PM is heated at high temperature (i.e °C) in inert atmosphere. Some of the organic species present in PM pyrolyse, instead of desorbing, and this falsely allocates them to EC instead of OC. Organic Material (OM): It is the total mass of organic material (including the mass of hydrogen) that corresponds to the organic carbon. The organic mass corresponding to the organic carbon depends on the species profile. Usually, an empirical correction of ~1,2-1,4 is applied to OC to derive OM.

58 Results from tunnel measurements

59 Emission Factors from tunnel measurements

60 Emission factors from dynamometer measurements (excerpt)

61 Conclusions from dyno studies
In Diesel heavy duty engines, the EC fraction increases and the OC/EC ratio decreases with engine load. At full load over 80% of total PM is EC In Diesel light duty vehicles, EC is over 80% regardless of operation condition, due to the oxidation catalyst which significantly reduces OC. In Gasoline light duty vehicles (non GDI), EC is a less than 30% fraction of total PM. The OC/EC ratio exceeds 100% (can reach up to 500% or higher)

62 Values proposed in the software (excerpt)
In cases where advanced aftertreatment is used (such as catalysed DPFs) then the EC and OM does not sum up to 100%. The remaining fraction is assumed to be ash, nitrates, sulphates, water and ammonium, that can be a significant fraction of total PM.

63 Non-Exhaust PM

64 TEi,j = Nj ∙ Mj ∙ (EF)j ∙ fi ∙ S(V)
General Methodology Sources Tyre wear Brake wear Road surface wear TEi,j = Nj ∙ Mj ∙ (EF)j ∙ fi ∙ S(V) TE... Total Emissions [g] N… Number of vehicles [veh.] M… Mileage driven by “average” vehicle [km/veh.] EF… TSP mass emission factor [g/km] fi… Mass fraction attributed to particle size class i S(V)… Correction factor for speed V (for road wear S(V)=1) and indices, i… TSP, PM10, PM2.5, PM1 and PM0.1 size classes, j… Vehicle category

65 Example PM10 Non-Exhaust Emission Factors from different sources and comparison with exhaust PM

66 Tyre wear rates

67 Tyre wear vs tyre PM emissions
Not all wear becomes airborne! Particle size class (i) Mass fraction (fT,i) of TSP TSP 1.000 PM10 0.600 PM2.5 0.420 PM1 0.060 PM0.1 0.048

68 Wish List - 1 User Request Ferreiro Antonio
IPCC Uncertainty Calculations Ricardo de Lauretis Mopeds Martin Adams Correction for CO2 (based on weight classes or more detailed capacity classes) Antonella Bernetii Include slope correction as a geographical parameter and not a vehicle specific parameter Helen Heintalu Send information on updates / new versions to national experts Provide export files to communicate to new CollectER and XML formats

69 Wish List - 2 User Request Antonella Bernetti
Make possible importing different fuel specifications per year from the Excel spreadsheets Andrei Pilipchuk Include the effect of idling (in particular cold idling) on road transport emissions

70 N2O/NH3 Emission Factors

71 Data organised according to: Mileage (vehicle/aftertreatment)
LAT Database on N2O/NH3 Database with 3500 measurements from 40 literature sources (mainly US input) Data organised according to: Mileage (vehicle/aftertreatment) Emission factor as a function of mileage Aftertreatment temperature (driving profile) Cold urban, hot urban, rural, highway Vehicle category PCs, LDVs, no info on HDVs & PTWs Vehicle technology Pre Euro, Euro 1 to Euro 4 Fuel Gasoline, Diesel, CNG, LPG, Methanol Blends Fuel sulphur content 5 ppm, 30 ppm, ppm, >150 ppm

72 N2O/NH3 Example of N2O Emission Factors

73 N2O/NH3 Example of NH3 Emission Factors

74 Emission Degradation

75 Measurements were performed before and after maintenance
Emission Degradation Measurements were performed before and after maintenance Daewoo Matiz – EURO 3 Daewoo Lanos – EURO 3 NEDC –> 2 x EUDC –> 3 x Artemis cycles MCC,i = AM × MMEAN + BM where, MMEAN: the mean fleet mileage of vehicles for which correction is applied MCC,i: the mileage correction for a given mileage (Mav), pollutant i and a specific cycle AM: the degradation of the emission performance per kilometer BM: the emission level of a fleet of brand new vehicles

76 New Emission degradation correction factor parameters for EURO 1 & EURO 2

77 Emission degradation correction factor as a function of speed
New Emission degradation correction factor parameters for EURO 3 & EURO 4 Emission degradation correction factor as a function of speed

78 Heavy Duty Vehicle Methodology

79 Heavy Duty Vehicle Coverage
Average Speed Model – Artemis Fuel consumption CO HC NOx PM Categories Heavy Goods Vehicles (Copert weight categories) Buses Coaches Effects Vehicle Load Road Gradient

80 Heavy Duty Vehicles – Example of Emission Factor – Rigid Truck <=7,5t Euro 3
Vehicle Technologies 1980’s Euro 1 to 5 Taking into account the unexcected behaviour of Euro 2 NOx

81 Heavy Duty Vehicles – Example of effect of vehicle load on emissions – Urban bus midi <15t Euro 3

82 Heavy Duty Vehicles – Example of road gradient on emissions – Truck-trailer artic. truck 50-60t Euro 2

83 Heavy Duty Vehicles – Remaining Issues
The influence of fuel quality The influence of engine deterioration with time The effect of maintenance The effect of particle traps (diesel particulate filters – DPFs) Alternative engine concepts Compressed natural gas (CNG) Bio-diesel

84 Heavy Duty Vehicles – Comparison with Copert 3
CO Euro 4 and 5 lower than COPERT HC Euro 4 and 5 lower than COPERT NOx Euro 2-5 higher than COPERT PM generally similar with small differences

85 CO Emissions Examples of Functions Coach Std <=18t 1980s
y=((e+(a*exp(((-1)*b)*x)))+(c*exp(((-1)*d)*x))) Coach Std <=18t Euro 4 y=(a+(b/(1+exp((((-1)*c)+(d*ln(x)))+(e*x))))) Ubus Std >15-18t Euro 1 y=(1/(a+(b*x))) Ubus Artic >18t Euro 2 y=exp((a+(b/x))+(c*ln(x)))

86 Cold Start

87 Cold Start Approach in Artemis Level 1: Excess Emission per Start – Vehicle Level
EE: Excess emission of pollutant p for a trip (g/veh.) V: Mean speed during the cold period (km/h) T: Ambient temperature (°C) t = Parking time (h) d: Actual travelled distance (km) ω20,20: Reference excess emission (20 °C & 20 km/h) for a trip distance longer or equal to the cold distance dc: Cold distance (km) δ: Dimensionless distance = d/dc(p,V,T) f: ω correction for speed V and the temperature T h: Fraction of cold overemission over distance δ g: Percentage of reference excess emission for parking time less than 12 h

88 Cold Start Approach in Artemis Level 2: Extension of Level 1 into fleet level
Ec = traffic excess emissions corresponding to a traffic tfi,h (g/time unit) t = parking time (h) cm(s, vi) = % of mileage recorded under cold start or intermediate temperature conditions for season s and speed vi of vehicle type i i = vehicle type s = season (winter, summer, middle, year) vi = traffic overall average speed for the vehicle type i (km/h) h = hour (1 to 24, day) tfi,h = traffic flow for the studied vehicle type i and the hour h(km.veh) ph = relative cold start number for the hour h (average=1) ptfi,h = relative traffic for the studied vehicle type i and the hour h (average=1) j = speed class with a cold engine m = trip length class n = class of stops (0 -1, 1 - 2, ... , >12h) pi,j = % of the distance travelled at speed j with a cold engine, for the average speed considered, and for the studied vehicle type i (%) pm,j = % of the distance started with a cold engine and distance dm, for speed Vj with a cold engine ph,n= % of the distance travelled after a stop with a duration of tn, for hour h dm = average distance of the trips under cold start conditions of class m (km) Vj = average speed with a cold engine corresponding to class j (km/h) T = ambient temperature (°C) wi(p) = reference excess emission for the vehicle type i (g) f(p,Vj,T) = plane function of the speed Vj and the temperature T, for the pollutant p h(p,d) = (1-ea(p,T).d)/ (1-ea(p,T)) d = dimensionless distance = dm/dc(p,Vj,T) dc(p,Vj,T) = cold distance for the pollutant p (km) g(p,tn) = % of excess emission at 12h of parking as a function of the parking time tn for the pollutant p

89 Cold Start Approach in Artemis Level 3: Level 2 with assumptions/estimations for most parameters
Excess Emission [g/km]=f(pollutant, vehicle technology/category, season, ambient temperature, mean speed, [hour of day]) Notes: Emissions in g/km difficult to perceive for a cold-start model Are directly multiplied with total mileage No differentiation for few starts/long trips No effect of different trip distributions (model assumes default ones) Model based on default parking-time profiles. This may be different for different applications/countries Additional work will be required to transform this model into Copert approach.

90 Excess emissions (g) versus ambient temperature (°C)
Diesel cars without catalyst

91 Cold distance (km) as a function of the vehicle speed (km/h)
Gasoline cars without catalyst

92 Evaporation Losses

93 Hot Emission Factors of Regulated Pollutants from Conventional PCs – Summary of Comparison COPERT and ARTEMIS Diesel Vehicles CO Euro 2 and 3 lower than COPERT HC Euro 3 lower than COPERT NOx similar results PM Euro 2 and 3 lower than COPERT Fuel Consumption higher than COPERT ay high engine capacities Gasoline Vehicles CO lower than COPERT at low speeds ranges HC similar results NOx Euro 3 and Euro 4 lower than COPERT at high speed ranges Fuel Consumption similar results

94 Hybrid Vehicles

95 Hybrid Vehicles – Measurements 1/2
The measurements were conducted on the LAT chassis dynamometer between 26-31/05/2005 Scope of the measurements was to obtain experience on hybrid technology and to study the vehicle performance in “real world” driving conditions We followed the specifications of the relevant European Directives Specific setup and conditioning guidelines given to us by Toyota Belgium on the Prius II vehicle tested

96 Hybrid Vehicles – Measurements 2/2
Emissions measurements The measurement protocol included the standard type-approval NEDC test and real world driving cycles (ARTEMIS cycles) 2 repetitions were conducted, during which all legislated gaseous pollutants and fuel consumption were measured ΔSOC was measured using the vehicle SOC indicator Fuel consumption and state-of-charge ΔSOC was again measured using the vehicle SOC indicator Repetitions of UDC, EUDC, Artemis urban and Artemis road in order to study the effect of ΔSOC and evaluate the measurements

97 Hybrid Vehicles – How do measurements compare against conventional PCs

98 Leon Ntziachristos +30 2310 996031
Dimitris Gkatzoflias

Download ppt "Leon Ntziachristos Dimitrios Gkatzoflias Charis Kouridis Giorgos Mellios Savvas Geivanidis Zissis Samaras COPERT 4 Copenhagen, 2008-06-19."

Similar presentations

Ads by Google