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A simulation based approach for quantifying CO2 emissions of light duty vehicle fleets. A case study on WLTP introduction
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World World Conference Conference on on Transport Transport Research Research -- WCTR WCTR 2016 2016 Shanghai. Shanghai. 10-15 10-15 July July 2016 2016
A simulation based approach for quantifying CO22 emissions of light duty vehicle fleets. A case study on WLTP introduction** a a† a†, Konstantinos Anagnostopoulosaa, Biagio Stefanos Stefanos Tsiakmakis Tsiakmakisa,, Georgios Georgios Fontaras Fontaras , Konstantinos Anagnostopoulos , Biagio aa aa Ciuffo , Alessandro Marotta Ciuffo , Alessandro Marotta a a Institute
Institute for for Energy Energy and and Transport Transport European European Commission Commission –– Joint Joint Research Research Centre, Centre, Ispra, Ispra, 21027, 21027, Italy Italy
Abstract Abstract This modelling approach This paper paper presents presents aa new new technology-oriented technology-oriented modelling approach for for assessing assessing the the effect effect of of different different technologies technologies and and fleet fleet composition composition on on energy energy consumption consumption // CO CO22 emissions. emissions. The The methodology methodology follows follows aa hybrid hybrid approach approach between between aa statistically statistically founded founded instantaneous instantaneous emission emission model model and and aa complete complete vehicle-simulation vehicle-simulation model. model. It It makes makes use use of of as as limited limited information information as as possible referring mainly to already available data sources. It is split into two modules, the sampling module where possible referring mainly to already available data sources. It is split into two modules, the sampling module where individual individual vehicles are are defined, defined, each each one one corresponding corresponding to to aa real real vehicle vehicle present present in in the the fleet, fleet, and and the the simulation vehicles simulation module module where where each each vehicle vehicle is is run run in in aa predefined predefined mission mission profile. profile. The The vehicle vehicle simulation simulation model model is is based based on on simple simple longitudinal longitudinal dynamics dynamics featuring featuring an an extended-Willans extended-Willans powertrain powertrain simulation simulation module. module. The The fleet fleet “generator” “generator” module module that that selects selects and and assigns assigns vehicle vehicle characteristics characteristics per per vehicle emissions monitoring monitoring database database new new vehicle vehicle registrations registrations in in the the vehicle is is based based on on existing existing databases databases and and the the annual annual CO CO22 emissions European market. market. The The implementation implementation code code is is built built so so that that several several thousands thousands of of simulations simulations are are possible possible in in limited limited time. time. In In this this European example, example, the the methodology methodology is is applied applied for for assessing assessing the the introduction introduction of of the the new new Worldwide Worldwide Harmonized Harmonized Test Test (WLTP) (WLTP) protocol protocol in in the European light duty vehicle type approval procedure. A representative fleet of approx. 4,000 vehicles was defined the European light duty vehicle type approval procedure. A representative fleet of approx. 4,000 vehicles was defined and and run run over emissions over the the existing existing and and forth-coming forth-coming type type approval approval cycles. cycles. Results Results showed showed good good correlation correlation of of fleet-wide fleet-wide predicted predicted CO CO22 emissions against existing existing recorded recorded data, data, used used for for validation. validation. Following, Following, the the WLTP WLTP provisions provisions were were introduced introduced and and calculations calculations were were made made against with with regard regard to to the the expected expected increases increases in in average average CO CO22 emissions emissions of of the the new new registrations registrations with with the the new new protocol. protocol. © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY. Peer-review under under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE Peer-review ON TRANSPORT RESEARCH SOCIETY. Keywords: Keywords: Type Type your your keywords keywords here, here, separated separated by by semicolons semicolons ;;
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The The views views and and opinions opinions expressed expressed in in this this paper paper are are purely purely those those of of the the authors authors and and do do not not under under any any circumstance circumstance reflect reflect the the official official position position of of the the European European Commission Commission Corresponding author. †† Corresponding author. Tel.: Tel.: +39 +39 0332 0332 786425; 786425; fax: fax: +39 +39 0332 0332 786671.E-mail 786671.E-mail address:[email protected] address:[email protected]
2214-241X 2214-241X © © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. Peer-review under Peer-review under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.308
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Georgios Fontaras et al. / Transportation Research Procedia 25C (2017) 3902–3912 Author name / Transportation Research Procedia 00 (2017) 000–000
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1. Introduction Initiatives to reduce CO2 emissions from light duty vehicles have been the cornerstone of the European policy for curbing road transport greenhouse gas emissions. The design of such measures has been based mainly on statistical data, fleet-scale based emissions modeling tools and inventories. Such approaches yield good estimates maintaining a good balance between the flexibility to encompass different boundary conditions and provide robust, verifiable numbers. Nonetheless EU Regulations 443/2009 and 510/2011 have set European targets of average CO2 emissions from light duty vehicles for 2015 (130 gCO2/km) and 2020 (95 gCO2/km) and the performance of each vehicle manufacturer against the corresponding target is assessed with costly penalties for the non-compliant ones (for a thorough and detailed description please refer to (Regulation (EC) No 443/2009 2009, European Commission 2011). Failure to comply with the targets can lead to an OEM paying fines in the order of several millions of Euros. This association of road transport’s energy efficiency policies with measurable economic actions is having an impact on the policy making process itself. As a result detailed analysis and high accuracy from all instruments used in policy design and implementation are increasingly requested. In addition to this, policies targeting the energy efficiency of the transport sector can be implemented at various levels and in different combinations starting from traffic management issues ending to more global emission trading schemes or promotion of certain technologies, a factor that increases the complexity and the level of detail of tools used for such analyses. The impact of new technologies, the synergies which may occur by initiatives aiming at different fields e.g. technology shift at individual vehicle level combined with interventions for optimizing traffic conditions, and similar factors are very difficult to capture with traditional tools. The combined effect of various technologies on vehicle CO2 emissions or fuel consumption has to a large extent been addressed on a qualitative or additive basis (Smokers et al. 2006). In fact different technologies may yield different benefits under different operating conditions, while their effects are not necessarily transferable from one condition to the other creating a distinctive line between official certification-monitoring procedures and what drivers’ really experience. In an effort to better support the policy design process of the European Commission in the field of light duty vehicle CO2 emissions a technology-oriented modelling approach has been adopted, the main points of which are presented in this paper. The approach attempts to consider both the effect of different technologies and fleet composition on energy consumption / CO2 emissions of passenger cars. The methodology follows a hybrid approach between a statistically founded instantaneous emission model and a complete vehicle-simulation model. It makes use of as limited information as possible referring mainly to already available data sources and using empirical models and information collected from measurements at the Joint Research Centre of the European Commission. The case study of the new Worldwide Harmonized Test Procedure (WLTP) in the European type approval scheme (Ciuffo et al. 2015) has been used in the present study as for demonstrating the functionality and potential advantages of such an approach. 2. Methodology 2.1. Vehicle simulation module Dedicated energy consumption – CO2 emissions calculation component, for passenger cars and light commercial vehicles, has been developed to support the simulation activity. Core of the simulation module is a physical-based model based on standard vehicle longitudinal dynamics and energy consumption simulation. Initial investigations indicated that the 5 most important factors affecting CO2 emissions over given driving conditions are: • • • • •
Accurate calculation of power; Driving behavior (gear-shifting, acceleration patterns); Powertrain operation and efficiency; Cold start – temperature conditions; Controls of secondary systems – power sources and/or sinks.
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In the power – driving submodule vehicle energy demand is calculated via simple vehicle longitudinal dynamics. A gear-shifting model based on the WLTP gear-shifting rules is included in order to back calculate engine rpm and torque based on the velocity and acceleration of the vehicle. Some common energy saving technologies such as start-stop and brake energy recuperation system are simulated based on a generic operating strategy which takes into account vehicle motion status and other vehicle characteristics (battery state of charge, engine coolant temperature, etc.). The main equations of the vehicle energy balance taken into consideration are described below. = + + . + .
(1)
= ∙ ∙ , ℎ = ∙ + ∙ +
(3)
= ( ∙ () + ∙ + ∙ + ∙ + ∙ ∙ ()) × . =
. ( ⁄ ∙ , )
. = , ℎ =
(2)
(4) (5)
The power required from the engine (Pengine) is the sum of the power demand at the wheel (Pwheel), losses at the transmission (Ptransmisssion), power demand of the electrical system (Pelec) and power losses due to other mechanical systems (Pmech). Factors F0 F1 F2, are typical for characterizing the road loads of vehicles and express the constant part of a vehicle’s resistances (tyre rolling resistances), the part that is proportional to velocity (partly tyre rolling resistance partly drivetrain losses) and the part that is proportional to the square of the vehicle’s velocity (aerodynamic component). Variables m and g denote the mass and acceleration of gravity respectively while φ stands for the road gradient which for this study was set to zero. Gearbox torque losses (Tloss) are assumed to be a function of the torque (Tin) and the rotational speed (RPM) at the powered shaft. Factors k1, k2 and k3 are empirical constants derived from detailed gearbox models available to the JRC. Variable ω denotes the angular speed of the powered shaft (equation 3) or the engine (equation 5). The battery and alternator efficiencies (nbat and nalt/or respectively) were considered constant as were the additional mechanical torque losses (Tmech). Fuel consumption is calculated by the powertrain module. A fitting approach for simplifying traditional engine mapping used in the form of efficiency look-up table is used in this case. An extended Willans model is fitted via regression to normalized steady state fuel maps. The normalization takes place according to engine capacity so mean effective pressure values are used to express fuel consumption and brake power, while mean piston speed is used as a normalized quantity for engine speed (Guzzela and Onder 2010) (Sorrentino et al. 2015). As a result Fuel Mean Effective Pressure (FMEP) can be expressed as a function of Brake Mean Effective Pressure (BMEP) and Mean Piston Speed (CM) based on the following equation. (, ) = (2 × ) × [−( + × + × ) − (( + × + × ) − 4 × × ( + × − ))⁄ ]
(6)
Where for four-stroke engines: () =
() ∗ 2 ()
(7)
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Georgios Fontaras et al. / Transportation Research Procedia 25C (2017) 3902–3912 Author name / Transportation Research Procedia 00 (2017) 000–000
() = () ∗ 2 ∗ 60
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(8)
Practically parameters a, b, c, a2 define the thermodynamic efficiency of the engine while values l and l2 correspond to the engine’s losses (friction, pumping, etc.). The values of parameters a, b, c, a2, l and l2 for specific engine categories have been pre-calculated. An in-house statistical analysis over 20 real engine maps has indicated limited variability of these six parameters for engines of the same fuel type and aspiration technology (e.g. turbo or non-turbo gasolines). Using the normalized equation allows for engine rescaling to different engine capacities and RRM operating ranges. In case where cold start – engine temperature should be taken into account, this occurs via a temperature model, which assumes direct influence of temperature on engine friction (parameters l and l2). A simple heat equilibrium model for calculating instantaneous engine heating up is included in the powertrain submodule as well as an empirical equation to calculate an equivalent engine heat capacity. The target operation temperature (engine temperature at warm conditions) can be either retrieved from test data or alternatively a fix value of 85oC is assumed. The complete fuel consumption model including cold start parameters can be expressed as follows:
() =
∗ ()∗ () (∗ ()∗ () ) ∗ ∗() ∗
∗( ∗ () )()
(9)
Where:
∆() =
() ∗ , () = .
() = () ∗ ∗
(10) (11)
Where FLHV is the Fuel Lower Heating Value in kJ/kg, and η is a fixed factor expressing the amount of heating energy actually transferred to the engine components. Due to the non-continuous nature of the problem the above differential equation cannot be analytically solved. Using measurement data however it is possible to calibrate the model and back-calculate the values for the engine parameters and the cold start model via a heuristic optimizer. This approach has been validated to several vehicles tested at the JRC’s chassis dyno. 2.2. Data sources and analysis As a reference for this study, the official European Monitoring database of CO2 from passenger cars established and maintained by the European Environmental Agency (EEA 2014) was used. The database collects, per each year, the information necessary to the European Commission to assess vehicle manufacturers’ compliance with respect to the targets defined by the regulation (Regulation (EC) No 443/2009 2009), for approximately 13 million vehicles in the 27 member states. In particular, per each vehicle type, variant, version, it reports: CO2 emissions (g/km), mass in running order (kg), displacement (cm3), engine power (kW), type of fuel, number of registrations in Europe for the specific year and vehicle footprint. The EEA publish the database for a specific year with one-year delay. Data for year 2013 were used for the present analysis. The information included in the monitoring database is not sufficient to properly run the model e.g. the database provides no information on the vehicle characteristics, engine characteristics, road loads and on the type of transmission, pieces of information that deeply affect the model’s performance. Existing information from the official EEA database was combined with information retrieved from on-line publicly available sources which was
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used to formulate a second, more detailed database, (henceforward WEB) containing vehicle specific information of approximately 4,500 vehicles. This second database included information regarding gearbox (gear ratios and final drive), engine (capacity, bore x stroke, drive system, fuel, nominal power and engine speed, etc.), vehicle body dimensions (width, height, length), additional technologies (start-stop and aspiration), tyres, mass, type approved fuel consumption and CO2 emission, for various vehicle models all necessary for running the simulations. Vehicle manufacturer, model, mass and CO2 emissions were used to directly link the database models to specific registrations in the EEA database. Fig. 1 presents the mass – CO2 emission points for the two databases, along with the mean figures for each one.
Fig. 1 Mass – CO2 emission of vehicles contained in the two databases (dots), green and black circle denote average values of the samples
Vehicles from the WEB database were clustered based on their mass, CO2 emissions and engine capacity in order to calculate a weighing factor, in terms of their representation in the actual fleet, and in this way construct a representative pool of vehicle models to be simulated. The sum of registrations in the EEA database for each of the clusters identified was divided by the total number of vehicles in the online database falling within the same cluster. This fictive number was assigned to each vehicle model as equivalent to the number of registrations, and was thus used for the calculation of the equivalent fleet CO2 emissions and fleet mass, based on the following equations: = =
∑( ∗ )
∑( ∗ )
(11) (12)
The definition of the clusters was done separately for the gasoline and diesel vehicles of the two databases. Starting with bins of 10 g CO2 /km for the CO2 emissions, while keeping constant the bins of capacity and the bins
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of mass, the CO2 emissions bin size was optimized to provide the minimum difference between the WEB database fleet CO2 and the 2013 – the latest full database – EEA database fleet CO2. The resulting bin sizes for each fuel are presented in Table 1, while the points of mass and CO2 emissions for the two fuels and the two databases, along with the relative fleet CO2 emissions are given in Fig. 2. Table 1 Bin sizes for mass, CO2 emissions and capacity, used for the definition of segments. Vehicle type
CO2 Emissions [g/km]
Mass [kg]
Capacity [cc]
Gasoline Vehicles
40
Equal to Inertia Classes
< 1,200 | 1,200 – 1,600 | 1,600 – 2,000 | > 2,000
Diesel Vehicles
22
Equal to Inertia Classes
< 1,300 | 1,300 – 1,700 | 1,700 – 2,100 | > 2,100
As seen in the figure, actual fleet CO2 emissions are well represented by the WEB database vehicles. The fleet CO2 emissions figure for gasoline vehicles is equal to 128.46 g/km for the EEA database and 128.32 g/km for the WEB database, an error of approx. 0.1%. The corresponding values for the diesel vehicles are 126.92 g/km and 125.43 g/km respectively, an error of approx. 1.17%.
Fig. 2 EEA and apportioned WEB averages (triangles). Each dot corresponds to an individual vehicle.
2.3. Input Assumptions for NEDC Missing input, necessary for the model to run, was defined, as described below, based on empirical formulas and standardized methods, along with default values originating either from the literature or based on the authors’ experience. The following sections describe the calculation of the main missing parameters of the model, namely the inertia and road loads, and the engine map parameters. Inertias & Road Loads
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Reference Mass: Reference mass is calculated as the sum of mass in running order plus 25kg, where mass in running order is defined as the empty mass (provided in the WEB database) plus 75kg (Regulation (UN) No. 83 2011). = + 25 = + 100
(13)
Inertia NEDC: The inertia for the NEDC test / simulation is based on the legislation (Regulation (UN) No. 83 2011) which defines inertia classes calculated from the reference mass reported in equation 14. Road Loads NEDC: The road load coefficients for NEDC are provided by the following equations: 0 = ( ∗ 9.81 ∗ ) ∗ 1.03 − 1 = 1 ∗ 1.03 −
2
1 ∗ 1.2 ∗ ∗ = 2 3.6 ∗ 1.03 − Where, = 0.0292 ∗ + 0.264
(14) (15) (16) (17)
where: • Wheel Rolling Resistance is considered equal to an average value of 0.009; • Correction factor is a fixed factor equal to the Driven Axle Effect (assumed equal to 2.2N)+ Precon Effect (assumed equal to 20N). The first represents a correction to the rotating inertia considered by the model (4 wheels spinning) while the common practice is to perform measurements in an 1-axle dyno (2 wheels spinning) while the second represents the effect of the preconditioning process to the chassis dyno set up. • F1 NEDC base is chosen randomly as between -0.2 and 0.2 N/(km/h); The Driven Axle Effect in this case is equal to 0.0046N. • Cw is the aerodynamic drag coefficient [-] derived from the empirical formula in eq.18 and A is the vehicle’s frontal area (reported height * reported width); Driven Axle Effect in this case is equal to 0.00059N. Engine Parameters Engine parameters have been produced from a pool of 20 engine maps (diesel, gasoline charged and gasoline naturally aspirated) available to JRC.
2.4. Input Assumptions for WLTP In order to simulate the WLTP cycle, additional parameters are needed to the model. The following equations describe the test mass and the new road loads to be used for the WLTP TMH simulations. It has to be noted, that at this preliminary stage, only simulations on the “worst-case-vehicle-H” were performed. That should be taken into account in the translation of the following figures, since those results represent the worst case expected outcome. Test Mass WLTP: The test mass for the WLTP test / simulation is based on the following equation – empirical formula for the WLTP TMH test.
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= + 100 + 0.15 ∗ ( − − 100)
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(19)
, where: • Unladen Max Mass is equal to the Empty Mass plus 150kg; • Max Mass is the vehicle’s maximum permissible mass, given as an input from the WEB database. Road Loads WLTP: The road load coefficients for WLTP TMH are provided by the following equations:
= ( ∗ 9.81 ∗ ℎ ) + 2 ∗ 0.0001 ∗ ∗ 9.81 ∗ 1.03 0.0 =
∗ + ∗ ∗ 9.81
ℎ = 2 ∗ 0.1 ∗ 9.81 ∗ 1000
(20)
(21) (22)
0 = 0.0 + ℎ +
(23)
1 = 1 ∗ 1.03
(24)
, where: • DRR is the difference in the rolling resistance between the “best” and “worst” tire, equal to 0.00133; • Precon Effect is chosen as a random from 5 to 15 N.
1 ∗ 1.2 ∗ 1.189 ∗ 2 = 2 2 3.6 ∗ 1.03 − 3.6
(25)
where: • DCDA is the difference on the Drag A between the “best-case-vehicle-L” and “worst-case-vehicle-H” as specified in the regulation for WLTP and is equal to 0.0426. 3. Results The main results of the calculations are summarized in Fig. 3and Fig. 4 and in Table 2. In order to assess the accuracy and the good operation of the model, a first run of simulations was performed using 2013 data. Each vehicle of the database was simulated over the NEDC cycle, and the simulated emissions were compared with the values provided in the WEB database. As demonstrated in Fig. 3 the simulations yielded results of relatively good accuracy accurately capturing the influence of vehicle mass on CO2 emissions both for gasoline and diesel vehicles. As indicated by the trendlines in the figures the model tends to overestimate the emissions of gasoline vehicles of higher sizes and underestimate the emissions of diesel vehicles of higher sizes. However the overall trends are well captured.
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Fig. 3 Simulated vs reference NEDC CO2 emissions.
Since all CO2 targets are assessed according to the number of sales of each model, a sales-based weighing assessment is also important (Fig. 4). As presented the correlation between the sales weighted CO2 emissions for the different model is very high (0.98) with deviations remaining in a limited range. In this case however the errors between reference and calculated CO2 emissions appear to be more systematic resulting in an underestimation of the sales-weighted emissions.
Fig. 4 Sales-based weighted calculated CO2 emissions compared to reported sales-based weighted CO2 emissions of the sample.
In order to further reduce possible bias a filter was applied to results for which the absolute calculation error exceeded 15% of the reported NEDC value. The resulting average values expressed both in absolute and weighted terms are demonstrated in Table 2. The error in this case remains limited in the order of 3g/km for the average emissions and 1.7g/km for the sales weighted average emissions.
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Following the same principles the values for the new world harmonized test protocol (WLTP) were calculated to be about 150g/km on average and 143.7 when considering the weighted average value. Hence the resulting difference between the two test protocols can be estimated to be in the order of 17g/km. However it should be noted that this value is calculated for the worst, in terms of energy efficiency and fuel consumption, configuration of the vehicles. The final WLTP fuel consumption will result from both the higher (as in the present study) and the lower energy consumption configuration. Table 2 Summary of results over NEDC and WLTP cycles
Reported Emissions (g/km)
Calculated Emissions (g/km)
NEDC
NEDC
WLTP
Difference (WLTP-NEDC)
Average
130.3
132.9
149.5
16.6
Sales Weighted Average
124.7
126.4
143.7
17.3
The resulting emissions from the simulations conducted over both cycles are graphically presented in Fig. 5a. The results appear to be well correlated while the linear relationship between the two cycles appears to brake for high CO2 emitting vehicles, an observation which can be attributed to the non-continuous nature of the NEDC’s inertia class definition.
a Fig. 5 Simulated relationship between WLTP CO2 emissions and NEDC CO2 emission (a) and the evolution of the difference between the two cycles as a function of NEDC reference mass (b)
Fig. 5b demonstrates graphically the calculated difference between the two cycles as a function of vehicle mass (NEDC mass). A clear trend towards lower differences between the two cycles appears with the increasing of the vehicle mass although the range of variation is quite high. It appears that for vehicles with masses higher than 18002000 the introduction of WLTP may not significantly alter the reported CO2 emissions.
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4. Conclusions A first attempt to evaluate the effect of introducing the WLTP on CO2 emissions at type-approval from passenger cars has been carried out in the present study. Due to the lack of detailed information on the vehicles currently on the market in Europe, a simulation approach has been used. The analysis relied on the reported CO2 emissions from the European new registrations for year 2013 and a collection of technical characteristics from web sources which were connected to real vehicle models. First results have shown the capability of the method to reproduce in a fairly accurate and robust way the global CO2 levels of current vehicle fleet. Preliminary results achieved show that the difference in CO2 emissions between NEDC and WLTP is likely to be in the range 5-20g/km depending on the specific vehicle characteristics. A very important factor that will influence the difference between the two test procedures will be the configurations of each vehicle model put in the market by each manufacturer. It is noted that the calculations presented in this study correspond to the high energy consumption configuration of the WLTP so it is possible that differences in reality are more limited. Considering a sales weighted calculation the introduction of WLTP would result in an increase of around 10-17g/km in the average CO2 emissions from passenger cars. If this difference was expected, the question now is how the political process with deal with it in setting the future CO2 emission targets. In any case, the political willingness of the European Commission to introduce the WLTP in the shortest possible time frame seems a fundamental step forward to achieve a cleaner and more efficient road transport in Europe. References Ciuffo, B., A. Marotta, M. Tutuianu, K. Anagnostopoulos, G. Fontaras, J. Pavlovic, S. Serra, S. Tsiakmakis and N. Zacharof (2015). "The development of the World-Wide Harmonized Test Procedure for Light Duty Vehicles (WLTP) and the Pathway for its Implementation into the EU Legislation." Transportation Research Record: Journal of the Transportation Research Board No. 2503: 110-118. EEA (2014) "Monitoring CO2 emissions from passenger cars and vans in 2013." EEA Technical Report DOI: 10.2800/23352. European Commission (2011). "REGULATION (EU) No 510/2011 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL setting emission performance standards for new light commercial vehicles as part of the Union's integrated approach to reduce CO 2 emissions from light-duty vehicles." Official Journal of the European Union 145/1. Guzzela, L. and C. Onder (2010). Introduction to modelling and control of internal combustion engines Springer. Regulation (EC) No 443/2009 (2009). Regulation (EC) No 443/2009 of the European Parliament and of the Council of 23 April 2009 setting emission performance standards for new passenger cars as part of the Community's integrated approach to reduce CO2 emissions from lightduty vehicles (Text with EEA relevance) E. Commission. Regulation (UN) No. 83 (2011). "Addendum 82: Regulation No. 83. Uniform provisions concerning the approval of vehicles with regard to the emission of pollutants according to engine fuel requirements. E/ECE/324/Rev.1/Add.82/Rev.4−E/ECE/TRANS/505/Rev.1/Add.82/Rev.4." Smokers, R. T. M., R. Vermeulen, R. van Mieghem, R. Gense, I. Skinner, M. Fergusson, E. MacKay, P. ten Brink, G. Fontaras and Z. Samaras.(2006,Review and analysis of the reduction potential and costs of technological and other measures to reduce CO2-emissions from passenger cars,SI2.408212 Final,http://ec.europa.eu/enterprise/automotive/pagesbackground/pollutant_emission/index.htm#co2) Sorrentino, M., F. Mauramati, I. Arsie, A. Cricchio, C. Pianese and W. Nesci (2015). "Application of Willans Line Method for Internal Combustion Engines Scalability towards the Design and Optimization of Eco-Innovation Solutions." SAE Technical Paper 2015-24-2397. UNECE.(2015,Global Technical Regulation No. 15. Worldwide Harmonized Light Vehicles Test Procedure. ,http://www.unece.org/fileadmin/DAM/trans/main/wp29/wp29r-1998agr-rules/ECE-TRANS-180a15e.pdf)
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World World Conference Conference on on Transport Transport Research Research -- WCTR WCTR 2016 2016 Shanghai. Shanghai. 10-15 10-15 July July 2016 2016
A simulation based approach for quantifying CO22 emissions of light duty vehicle fleets. A case study on WLTP introduction** a a† a†, Konstantinos Anagnostopoulosaa, Biagio Stefanos Stefanos Tsiakmakis Tsiakmakisa,, Georgios Georgios Fontaras Fontaras , Konstantinos Anagnostopoulos , Biagio aa aa Ciuffo , Alessandro Marotta Ciuffo , Alessandro Marotta a a Institute
Institute for for Energy Energy and and Transport Transport European European Commission Commission –– Joint Joint Research Research Centre, Centre, Ispra, Ispra, 21027, 21027, Italy Italy
Abstract Abstract This modelling approach This paper paper presents presents aa new new technology-oriented technology-oriented modelling approach for for assessing assessing the the effect effect of of different different technologies technologies and and fleet fleet composition composition on on energy energy consumption consumption // CO CO22 emissions. emissions. The The methodology methodology follows follows aa hybrid hybrid approach approach between between aa statistically statistically founded founded instantaneous instantaneous emission emission model model and and aa complete complete vehicle-simulation vehicle-simulation model. model. It It makes makes use use of of as as limited limited information information as as possible referring mainly to already available data sources. It is split into two modules, the sampling module where possible referring mainly to already available data sources. It is split into two modules, the sampling module where individual individual vehicles are are defined, defined, each each one one corresponding corresponding to to aa real real vehicle vehicle present present in in the the fleet, fleet, and and the the simulation vehicles simulation module module where where each each vehicle vehicle is is run run in in aa predefined predefined mission mission profile. profile. The The vehicle vehicle simulation simulation model model is is based based on on simple simple longitudinal longitudinal dynamics dynamics featuring featuring an an extended-Willans extended-Willans powertrain powertrain simulation simulation module. module. The The fleet fleet “generator” “generator” module module that that selects selects and and assigns assigns vehicle vehicle characteristics characteristics per per vehicle emissions monitoring monitoring database database new new vehicle vehicle registrations registrations in in the the vehicle is is based based on on existing existing databases databases and and the the annual annual CO CO22 emissions European market. market. The The implementation implementation code code is is built built so so that that several several thousands thousands of of simulations simulations are are possible possible in in limited limited time. time. In In this this European example, example, the the methodology methodology is is applied applied for for assessing assessing the the introduction introduction of of the the new new Worldwide Worldwide Harmonized Harmonized Test Test (WLTP) (WLTP) protocol protocol in in the European light duty vehicle type approval procedure. A representative fleet of approx. 4,000 vehicles was defined the European light duty vehicle type approval procedure. A representative fleet of approx. 4,000 vehicles was defined and and run run over emissions over the the existing existing and and forth-coming forth-coming type type approval approval cycles. cycles. Results Results showed showed good good correlation correlation of of fleet-wide fleet-wide predicted predicted CO CO22 emissions against existing existing recorded recorded data, data, used used for for validation. validation. Following, Following, the the WLTP WLTP provisions provisions were were introduced introduced and and calculations calculations were were made made against with with regard regard to to the the expected expected increases increases in in average average CO CO22 emissions emissions of of the the new new registrations registrations with with the the new new protocol. protocol. © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY. Peer-review under under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE Peer-review ON TRANSPORT RESEARCH SOCIETY. Keywords: Keywords: Type Type your your keywords keywords here, here, separated separated by by semicolons semicolons ;;
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The The views views and and opinions opinions expressed expressed in in this this paper paper are are purely purely those those of of the the authors authors and and do do not not under under any any circumstance circumstance reflect reflect the the official official position position of of the the European European Commission Commission Corresponding author. †† Corresponding author. Tel.: Tel.: +39 +39 0332 0332 786425; 786425; fax: fax: +39 +39 0332 0332 786671.E-mail 786671.E-mail address:[email protected] address:[email protected]
2214-241X 2214-241X © © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. Peer-review under Peer-review under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.308
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1. Introduction Initiatives to reduce CO2 emissions from light duty vehicles have been the cornerstone of the European policy for curbing road transport greenhouse gas emissions. The design of such measures has been based mainly on statistical data, fleet-scale based emissions modeling tools and inventories. Such approaches yield good estimates maintaining a good balance between the flexibility to encompass different boundary conditions and provide robust, verifiable numbers. Nonetheless EU Regulations 443/2009 and 510/2011 have set European targets of average CO2 emissions from light duty vehicles for 2015 (130 gCO2/km) and 2020 (95 gCO2/km) and the performance of each vehicle manufacturer against the corresponding target is assessed with costly penalties for the non-compliant ones (for a thorough and detailed description please refer to (Regulation (EC) No 443/2009 2009, European Commission 2011). Failure to comply with the targets can lead to an OEM paying fines in the order of several millions of Euros. This association of road transport’s energy efficiency policies with measurable economic actions is having an impact on the policy making process itself. As a result detailed analysis and high accuracy from all instruments used in policy design and implementation are increasingly requested. In addition to this, policies targeting the energy efficiency of the transport sector can be implemented at various levels and in different combinations starting from traffic management issues ending to more global emission trading schemes or promotion of certain technologies, a factor that increases the complexity and the level of detail of tools used for such analyses. The impact of new technologies, the synergies which may occur by initiatives aiming at different fields e.g. technology shift at individual vehicle level combined with interventions for optimizing traffic conditions, and similar factors are very difficult to capture with traditional tools. The combined effect of various technologies on vehicle CO2 emissions or fuel consumption has to a large extent been addressed on a qualitative or additive basis (Smokers et al. 2006). In fact different technologies may yield different benefits under different operating conditions, while their effects are not necessarily transferable from one condition to the other creating a distinctive line between official certification-monitoring procedures and what drivers’ really experience. In an effort to better support the policy design process of the European Commission in the field of light duty vehicle CO2 emissions a technology-oriented modelling approach has been adopted, the main points of which are presented in this paper. The approach attempts to consider both the effect of different technologies and fleet composition on energy consumption / CO2 emissions of passenger cars. The methodology follows a hybrid approach between a statistically founded instantaneous emission model and a complete vehicle-simulation model. It makes use of as limited information as possible referring mainly to already available data sources and using empirical models and information collected from measurements at the Joint Research Centre of the European Commission. The case study of the new Worldwide Harmonized Test Procedure (WLTP) in the European type approval scheme (Ciuffo et al. 2015) has been used in the present study as for demonstrating the functionality and potential advantages of such an approach. 2. Methodology 2.1. Vehicle simulation module Dedicated energy consumption – CO2 emissions calculation component, for passenger cars and light commercial vehicles, has been developed to support the simulation activity. Core of the simulation module is a physical-based model based on standard vehicle longitudinal dynamics and energy consumption simulation. Initial investigations indicated that the 5 most important factors affecting CO2 emissions over given driving conditions are: • • • • •
Accurate calculation of power; Driving behavior (gear-shifting, acceleration patterns); Powertrain operation and efficiency; Cold start – temperature conditions; Controls of secondary systems – power sources and/or sinks.
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In the power – driving submodule vehicle energy demand is calculated via simple vehicle longitudinal dynamics. A gear-shifting model based on the WLTP gear-shifting rules is included in order to back calculate engine rpm and torque based on the velocity and acceleration of the vehicle. Some common energy saving technologies such as start-stop and brake energy recuperation system are simulated based on a generic operating strategy which takes into account vehicle motion status and other vehicle characteristics (battery state of charge, engine coolant temperature, etc.). The main equations of the vehicle energy balance taken into consideration are described below. = + + . + .
(1)
= ∙ ∙ , ℎ = ∙ + ∙ +
(3)
= ( ∙ () + ∙ + ∙ + ∙ + ∙ ∙ ()) × . =
. ( ⁄ ∙ , )
. = , ℎ =
(2)
(4) (5)
The power required from the engine (Pengine) is the sum of the power demand at the wheel (Pwheel), losses at the transmission (Ptransmisssion), power demand of the electrical system (Pelec) and power losses due to other mechanical systems (Pmech). Factors F0 F1 F2, are typical for characterizing the road loads of vehicles and express the constant part of a vehicle’s resistances (tyre rolling resistances), the part that is proportional to velocity (partly tyre rolling resistance partly drivetrain losses) and the part that is proportional to the square of the vehicle’s velocity (aerodynamic component). Variables m and g denote the mass and acceleration of gravity respectively while φ stands for the road gradient which for this study was set to zero. Gearbox torque losses (Tloss) are assumed to be a function of the torque (Tin) and the rotational speed (RPM) at the powered shaft. Factors k1, k2 and k3 are empirical constants derived from detailed gearbox models available to the JRC. Variable ω denotes the angular speed of the powered shaft (equation 3) or the engine (equation 5). The battery and alternator efficiencies (nbat and nalt/or respectively) were considered constant as were the additional mechanical torque losses (Tmech). Fuel consumption is calculated by the powertrain module. A fitting approach for simplifying traditional engine mapping used in the form of efficiency look-up table is used in this case. An extended Willans model is fitted via regression to normalized steady state fuel maps. The normalization takes place according to engine capacity so mean effective pressure values are used to express fuel consumption and brake power, while mean piston speed is used as a normalized quantity for engine speed (Guzzela and Onder 2010) (Sorrentino et al. 2015). As a result Fuel Mean Effective Pressure (FMEP) can be expressed as a function of Brake Mean Effective Pressure (BMEP) and Mean Piston Speed (CM) based on the following equation. (, ) = (2 × ) × [−( + × + × ) − (( + × + × ) − 4 × × ( + × − ))⁄ ]
(6)
Where for four-stroke engines: () =
() ∗ 2 ()
(7)
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() = () ∗ 2 ∗ 60
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(8)
Practically parameters a, b, c, a2 define the thermodynamic efficiency of the engine while values l and l2 correspond to the engine’s losses (friction, pumping, etc.). The values of parameters a, b, c, a2, l and l2 for specific engine categories have been pre-calculated. An in-house statistical analysis over 20 real engine maps has indicated limited variability of these six parameters for engines of the same fuel type and aspiration technology (e.g. turbo or non-turbo gasolines). Using the normalized equation allows for engine rescaling to different engine capacities and RRM operating ranges. In case where cold start – engine temperature should be taken into account, this occurs via a temperature model, which assumes direct influence of temperature on engine friction (parameters l and l2). A simple heat equilibrium model for calculating instantaneous engine heating up is included in the powertrain submodule as well as an empirical equation to calculate an equivalent engine heat capacity. The target operation temperature (engine temperature at warm conditions) can be either retrieved from test data or alternatively a fix value of 85oC is assumed. The complete fuel consumption model including cold start parameters can be expressed as follows:
() =
∗ ()∗ () (∗ ()∗ () ) ∗ ∗() ∗
∗( ∗ () )()
(9)
Where:
∆() =
() ∗ , () = .
() = () ∗ ∗
(10) (11)
Where FLHV is the Fuel Lower Heating Value in kJ/kg, and η is a fixed factor expressing the amount of heating energy actually transferred to the engine components. Due to the non-continuous nature of the problem the above differential equation cannot be analytically solved. Using measurement data however it is possible to calibrate the model and back-calculate the values for the engine parameters and the cold start model via a heuristic optimizer. This approach has been validated to several vehicles tested at the JRC’s chassis dyno. 2.2. Data sources and analysis As a reference for this study, the official European Monitoring database of CO2 from passenger cars established and maintained by the European Environmental Agency (EEA 2014) was used. The database collects, per each year, the information necessary to the European Commission to assess vehicle manufacturers’ compliance with respect to the targets defined by the regulation (Regulation (EC) No 443/2009 2009), for approximately 13 million vehicles in the 27 member states. In particular, per each vehicle type, variant, version, it reports: CO2 emissions (g/km), mass in running order (kg), displacement (cm3), engine power (kW), type of fuel, number of registrations in Europe for the specific year and vehicle footprint. The EEA publish the database for a specific year with one-year delay. Data for year 2013 were used for the present analysis. The information included in the monitoring database is not sufficient to properly run the model e.g. the database provides no information on the vehicle characteristics, engine characteristics, road loads and on the type of transmission, pieces of information that deeply affect the model’s performance. Existing information from the official EEA database was combined with information retrieved from on-line publicly available sources which was
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used to formulate a second, more detailed database, (henceforward WEB) containing vehicle specific information of approximately 4,500 vehicles. This second database included information regarding gearbox (gear ratios and final drive), engine (capacity, bore x stroke, drive system, fuel, nominal power and engine speed, etc.), vehicle body dimensions (width, height, length), additional technologies (start-stop and aspiration), tyres, mass, type approved fuel consumption and CO2 emission, for various vehicle models all necessary for running the simulations. Vehicle manufacturer, model, mass and CO2 emissions were used to directly link the database models to specific registrations in the EEA database. Fig. 1 presents the mass – CO2 emission points for the two databases, along with the mean figures for each one.
Fig. 1 Mass – CO2 emission of vehicles contained in the two databases (dots), green and black circle denote average values of the samples
Vehicles from the WEB database were clustered based on their mass, CO2 emissions and engine capacity in order to calculate a weighing factor, in terms of their representation in the actual fleet, and in this way construct a representative pool of vehicle models to be simulated. The sum of registrations in the EEA database for each of the clusters identified was divided by the total number of vehicles in the online database falling within the same cluster. This fictive number was assigned to each vehicle model as equivalent to the number of registrations, and was thus used for the calculation of the equivalent fleet CO2 emissions and fleet mass, based on the following equations: = =
∑( ∗ )
∑( ∗ )
(11) (12)
The definition of the clusters was done separately for the gasoline and diesel vehicles of the two databases. Starting with bins of 10 g CO2 /km for the CO2 emissions, while keeping constant the bins of capacity and the bins
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of mass, the CO2 emissions bin size was optimized to provide the minimum difference between the WEB database fleet CO2 and the 2013 – the latest full database – EEA database fleet CO2. The resulting bin sizes for each fuel are presented in Table 1, while the points of mass and CO2 emissions for the two fuels and the two databases, along with the relative fleet CO2 emissions are given in Fig. 2. Table 1 Bin sizes for mass, CO2 emissions and capacity, used for the definition of segments. Vehicle type
CO2 Emissions [g/km]
Mass [kg]
Capacity [cc]
Gasoline Vehicles
40
Equal to Inertia Classes
< 1,200 | 1,200 – 1,600 | 1,600 – 2,000 | > 2,000
Diesel Vehicles
22
Equal to Inertia Classes
< 1,300 | 1,300 – 1,700 | 1,700 – 2,100 | > 2,100
As seen in the figure, actual fleet CO2 emissions are well represented by the WEB database vehicles. The fleet CO2 emissions figure for gasoline vehicles is equal to 128.46 g/km for the EEA database and 128.32 g/km for the WEB database, an error of approx. 0.1%. The corresponding values for the diesel vehicles are 126.92 g/km and 125.43 g/km respectively, an error of approx. 1.17%.
Fig. 2 EEA and apportioned WEB averages (triangles). Each dot corresponds to an individual vehicle.
2.3. Input Assumptions for NEDC Missing input, necessary for the model to run, was defined, as described below, based on empirical formulas and standardized methods, along with default values originating either from the literature or based on the authors’ experience. The following sections describe the calculation of the main missing parameters of the model, namely the inertia and road loads, and the engine map parameters. Inertias & Road Loads
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Reference Mass: Reference mass is calculated as the sum of mass in running order plus 25kg, where mass in running order is defined as the empty mass (provided in the WEB database) plus 75kg (Regulation (UN) No. 83 2011). = + 25 = + 100
(13)
Inertia NEDC: The inertia for the NEDC test / simulation is based on the legislation (Regulation (UN) No. 83 2011) which defines inertia classes calculated from the reference mass reported in equation 14. Road Loads NEDC: The road load coefficients for NEDC are provided by the following equations: 0 = ( ∗ 9.81 ∗ ) ∗ 1.03 − 1 = 1 ∗ 1.03 −
2
1 ∗ 1.2 ∗ ∗ = 2 3.6 ∗ 1.03 − Where, = 0.0292 ∗ + 0.264
(14) (15) (16) (17)
where: • Wheel Rolling Resistance is considered equal to an average value of 0.009; • Correction factor is a fixed factor equal to the Driven Axle Effect (assumed equal to 2.2N)+ Precon Effect (assumed equal to 20N). The first represents a correction to the rotating inertia considered by the model (4 wheels spinning) while the common practice is to perform measurements in an 1-axle dyno (2 wheels spinning) while the second represents the effect of the preconditioning process to the chassis dyno set up. • F1 NEDC base is chosen randomly as between -0.2 and 0.2 N/(km/h); The Driven Axle Effect in this case is equal to 0.0046N. • Cw is the aerodynamic drag coefficient [-] derived from the empirical formula in eq.18 and A is the vehicle’s frontal area (reported height * reported width); Driven Axle Effect in this case is equal to 0.00059N. Engine Parameters Engine parameters have been produced from a pool of 20 engine maps (diesel, gasoline charged and gasoline naturally aspirated) available to JRC.
2.4. Input Assumptions for WLTP In order to simulate the WLTP cycle, additional parameters are needed to the model. The following equations describe the test mass and the new road loads to be used for the WLTP TMH simulations. It has to be noted, that at this preliminary stage, only simulations on the “worst-case-vehicle-H” were performed. That should be taken into account in the translation of the following figures, since those results represent the worst case expected outcome. Test Mass WLTP: The test mass for the WLTP test / simulation is based on the following equation – empirical formula for the WLTP TMH test.
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= + 100 + 0.15 ∗ ( − − 100)
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(19)
, where: • Unladen Max Mass is equal to the Empty Mass plus 150kg; • Max Mass is the vehicle’s maximum permissible mass, given as an input from the WEB database. Road Loads WLTP: The road load coefficients for WLTP TMH are provided by the following equations:
= ( ∗ 9.81 ∗ ℎ ) + 2 ∗ 0.0001 ∗ ∗ 9.81 ∗ 1.03 0.0 =
∗ + ∗ ∗ 9.81
ℎ = 2 ∗ 0.1 ∗ 9.81 ∗ 1000
(20)
(21) (22)
0 = 0.0 + ℎ +
(23)
1 = 1 ∗ 1.03
(24)
, where: • DRR is the difference in the rolling resistance between the “best” and “worst” tire, equal to 0.00133; • Precon Effect is chosen as a random from 5 to 15 N.
1 ∗ 1.2 ∗ 1.189 ∗ 2 = 2 2 3.6 ∗ 1.03 − 3.6
(25)
where: • DCDA is the difference on the Drag A between the “best-case-vehicle-L” and “worst-case-vehicle-H” as specified in the regulation for WLTP and is equal to 0.0426. 3. Results The main results of the calculations are summarized in Fig. 3and Fig. 4 and in Table 2. In order to assess the accuracy and the good operation of the model, a first run of simulations was performed using 2013 data. Each vehicle of the database was simulated over the NEDC cycle, and the simulated emissions were compared with the values provided in the WEB database. As demonstrated in Fig. 3 the simulations yielded results of relatively good accuracy accurately capturing the influence of vehicle mass on CO2 emissions both for gasoline and diesel vehicles. As indicated by the trendlines in the figures the model tends to overestimate the emissions of gasoline vehicles of higher sizes and underestimate the emissions of diesel vehicles of higher sizes. However the overall trends are well captured.
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Fig. 3 Simulated vs reference NEDC CO2 emissions.
Since all CO2 targets are assessed according to the number of sales of each model, a sales-based weighing assessment is also important (Fig. 4). As presented the correlation between the sales weighted CO2 emissions for the different model is very high (0.98) with deviations remaining in a limited range. In this case however the errors between reference and calculated CO2 emissions appear to be more systematic resulting in an underestimation of the sales-weighted emissions.
Fig. 4 Sales-based weighted calculated CO2 emissions compared to reported sales-based weighted CO2 emissions of the sample.
In order to further reduce possible bias a filter was applied to results for which the absolute calculation error exceeded 15% of the reported NEDC value. The resulting average values expressed both in absolute and weighted terms are demonstrated in Table 2. The error in this case remains limited in the order of 3g/km for the average emissions and 1.7g/km for the sales weighted average emissions.
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Following the same principles the values for the new world harmonized test protocol (WLTP) were calculated to be about 150g/km on average and 143.7 when considering the weighted average value. Hence the resulting difference between the two test protocols can be estimated to be in the order of 17g/km. However it should be noted that this value is calculated for the worst, in terms of energy efficiency and fuel consumption, configuration of the vehicles. The final WLTP fuel consumption will result from both the higher (as in the present study) and the lower energy consumption configuration. Table 2 Summary of results over NEDC and WLTP cycles
Reported Emissions (g/km)
Calculated Emissions (g/km)
NEDC
NEDC
WLTP
Difference (WLTP-NEDC)
Average
130.3
132.9
149.5
16.6
Sales Weighted Average
124.7
126.4
143.7
17.3
The resulting emissions from the simulations conducted over both cycles are graphically presented in Fig. 5a. The results appear to be well correlated while the linear relationship between the two cycles appears to brake for high CO2 emitting vehicles, an observation which can be attributed to the non-continuous nature of the NEDC’s inertia class definition.
a Fig. 5 Simulated relationship between WLTP CO2 emissions and NEDC CO2 emission (a) and the evolution of the difference between the two cycles as a function of NEDC reference mass (b)
Fig. 5b demonstrates graphically the calculated difference between the two cycles as a function of vehicle mass (NEDC mass). A clear trend towards lower differences between the two cycles appears with the increasing of the vehicle mass although the range of variation is quite high. It appears that for vehicles with masses higher than 18002000 the introduction of WLTP may not significantly alter the reported CO2 emissions.
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4. Conclusions A first attempt to evaluate the effect of introducing the WLTP on CO2 emissions at type-approval from passenger cars has been carried out in the present study. Due to the lack of detailed information on the vehicles currently on the market in Europe, a simulation approach has been used. The analysis relied on the reported CO2 emissions from the European new registrations for year 2013 and a collection of technical characteristics from web sources which were connected to real vehicle models. First results have shown the capability of the method to reproduce in a fairly accurate and robust way the global CO2 levels of current vehicle fleet. Preliminary results achieved show that the difference in CO2 emissions between NEDC and WLTP is likely to be in the range 5-20g/km depending on the specific vehicle characteristics. A very important factor that will influence the difference between the two test procedures will be the configurations of each vehicle model put in the market by each manufacturer. It is noted that the calculations presented in this study correspond to the high energy consumption configuration of the WLTP so it is possible that differences in reality are more limited. Considering a sales weighted calculation the introduction of WLTP would result in an increase of around 10-17g/km in the average CO2 emissions from passenger cars. If this difference was expected, the question now is how the political process with deal with it in setting the future CO2 emission targets. In any case, the political willingness of the European Commission to introduce the WLTP in the shortest possible time frame seems a fundamental step forward to achieve a cleaner and more efficient road transport in Europe. References Ciuffo, B., A. Marotta, M. Tutuianu, K. Anagnostopoulos, G. Fontaras, J. Pavlovic, S. Serra, S. Tsiakmakis and N. Zacharof (2015). "The development of the World-Wide Harmonized Test Procedure for Light Duty Vehicles (WLTP) and the Pathway for its Implementation into the EU Legislation." Transportation Research Record: Journal of the Transportation Research Board No. 2503: 110-118. EEA (2014) "Monitoring CO2 emissions from passenger cars and vans in 2013." EEA Technical Report DOI: 10.2800/23352. European Commission (2011). "REGULATION (EU) No 510/2011 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL setting emission performance standards for new light commercial vehicles as part of the Union's integrated approach to reduce CO 2 emissions from light-duty vehicles." Official Journal of the European Union 145/1. Guzzela, L. and C. Onder (2010). Introduction to modelling and control of internal combustion engines Springer. Regulation (EC) No 443/2009 (2009). Regulation (EC) No 443/2009 of the European Parliament and of the Council of 23 April 2009 setting emission performance standards for new passenger cars as part of the Community's integrated approach to reduce CO2 emissions from lightduty vehicles (Text with EEA relevance) E. Commission. Regulation (UN) No. 83 (2011). "Addendum 82: Regulation No. 83. Uniform provisions concerning the approval of vehicles with regard to the emission of pollutants according to engine fuel requirements. E/ECE/324/Rev.1/Add.82/Rev.4−E/ECE/TRANS/505/Rev.1/Add.82/Rev.4." Smokers, R. T. M., R. Vermeulen, R. van Mieghem, R. Gense, I. Skinner, M. Fergusson, E. MacKay, P. ten Brink, G. Fontaras and Z. Samaras.(2006,Review and analysis of the reduction potential and costs of technological and other measures to reduce CO2-emissions from passenger cars,SI2.408212 Final,http://ec.europa.eu/enterprise/automotive/pagesbackground/pollutant_emission/index.htm#co2) Sorrentino, M., F. Mauramati, I. Arsie, A. Cricchio, C. Pianese and W. Nesci (2015). "Application of Willans Line Method for Internal Combustion Engines Scalability towards the Design and Optimization of Eco-Innovation Solutions." SAE Technical Paper 2015-24-2397. UNECE.(2015,Global Technical Regulation No. 15. Worldwide Harmonized Light Vehicles Test Procedure. ,http://www.unece.org/fileadmin/DAM/trans/main/wp29/wp29r-1998agr-rules/ECE-TRANS-180a15e.pdf)