Joint Research Centre - European Commission · • CO2MPAS: more detailed version of the WLTP-GTR...
Transcript of Joint Research Centre - European Commission · • CO2MPAS: more detailed version of the WLTP-GTR...
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Joint Research Centre the European Commission's in-house science service
Serving society Stimulating innovation Supporting legislation
Introduction to the
CO2MPAS tool
Belgrade, 20/10/2017
S. Tsiakmakis,
G. Fontaras, B. Ciuffo, V. Arcidiacono, V. Valverde,
K. Anagnostopoulos, J. Pavlovic, D. Komnos
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Contents
• The introduction of the WLTP in the EU type-approval of LDVs
• Introduction to the CO2MPAS tool ü Introduction ü The CO2MPAS Tool ü Validation & Results ü Further Applications
• CO2MPAS practicum
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Introduction Why Simulations?
Experimentally testing many vehicles has 2 drawbacks: 1. It doesn’t cover all possible configurations of the vehicle and many
operating conditions 2. It is expensive and time consuming
Selected Approach: Combine vehicle simulation & measurements à Models of existing vehicles à Calculate CO2 & emissions over different conditions
Approach extensively used in the industry, while several considerations are of crucial importance: • Selection of fleet representative vehicle models • Input data collection • Accurate simulation of the vehicle’s operation • Validation / quality control of results
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Introduction Basic Energy Flow in a Vehicle
100J 20J
7J 6J
6J
Well to Tank CO2 reduction eg.
biofuels
Powertrain tech’s eg. downsizing,
hybridization, etc.
Vehicle / Driver CO2 measures (eg. mass
reduction, eco-driving)
Vehicle Simulation CO2 Reduction Options
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Introduction Basic Vehicle’s Free Body Diagram
Where: Ftr: tractive force m: vehicle mass V: vehicle velocity Rr: rolling resistance
𝑭𝒕𝒓=𝒎𝒅 𝒗/𝒅𝒕 +𝑹𝒓+𝑹𝒂+𝑹𝒈+𝑹𝒑 Ra: aerodynamic resistance Rg: road’s grade resistances Rp: other resistances
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Introduction Simulation of Engine Power & Speed
Source: VECTO user’s manual
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Introduction Consumption / Efficiency Maps • A map (engine, gearbox, motor, other) is practically a table containing fuel
consumption/component efficiency values for pairs of RPM – Torque
• The vehicle model at each calculation step (e.g. each sec), retrieves the fuel
consumption/efficiency value depending on calculated RPM, Torque or Power
• There is no physical model of engine/component to react in certain technology
changes; new tables need to be provided
RPM\ Torque 10 50 100 150 200
1000 1 3 4 5 6
1200 2 4 6 8 10
1400 5 7 9 11 13
1600 6 8 10 12 14
1800 8 10 12 14 16
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Introduction Forward vs. Backward Models
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Introduction Vehicle Simulation Models Can & Cannot Do’s
There are technologies/combinations that can be accurately and effectively simulated in
common vehicle simulation software:
• Vehicle resistances (tyres, aerodynamics, mass influence)
• Gearbox ratios, driveline components, increased gear number, clutches
• Efficiency of powertrain and drivetrain components
• Auxiliary units
• Engine relevant technologies (some)
• Control logics (some)
There are CO2 reduction technologies which cannot be directly simulated with common
vehicle simulation software:
• Component specific technologies (e.g. effect of particular injector, heat recovery, etc.)
• Complex control logics and respective functions
• After treatment system technologies
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Introduction Vehicle Simulation Models Can & Cannot Do’s
There are technologies/combinations that can be accurately and effectively simulated in
common vehicle simulation software:
• Vehicle resistances (tyres, aerodynamics, mass influence)
• Gearbox ratios, driveline components, increased gear number, clutches
• Efficiency of powertrain and drivetrain components
• Auxiliary units
• Engine relevant technologies (some)
• Control logics (some)
There are CO2 reduction technologies which cannot be directly simulated with common
vehicle simulation software:
• Component specific technologies (e.g. effect of particular injector, heat recovery, etc.)
• Complex control logics and respective functions
• After treatment system technologies
Dedicated & more complicated component simulation software or measurements need to be used
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Introduction Simulation Tools used for Regulatory Purposes
Below, a non exhaustive list: • GEM, US EPA for HDVs • HILs, UNECE/Japan for Hybrid HD Powertrains • VECTO, European Commission – HD Vehicles • KEES, Korea – HD Vehicles • JSM, NTSEL/Japan – HD Vehicles And many other commercial brands that may already be in use… Since Sept. 2017, additionally to the previous:
• CO2MPAS, European Commission – LDV Vehicles
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The CO2MPAS Tool What is it?
WLTP Phasing-in (2017-2020) WLTP-based CO2 emissions (measured at type-approval) will be translated in the equivalent NEDC-based ones, and then used to assess the compliance towards CO2 emission targets
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The CO2MPAS Tool What is it?
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The CO2MPAS Tool Development Boundaries
Boundary conditions, as set in the beginning of the WLTP/NEDC Correlation: 1. Simple 2. Minimum number of input variables (available in TA) 3. Accuracy of the ΔWLTP-NEDC in the order of 2-2.5g 4. Minimize statistical approaches and calculations 5. Allow for the assessment of as many future technologies as possible
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The CO2MPAS Tool The Modules
The tool comprises of 2 main calculation modules:
Power – RPM module • Simple longitudinal dynamics (WLTP-GTR) • Engine power and RPM calc’d @ 1hz • Inclusion of Mech or Elec. loads where
needed • Generic start-stop logic • A/T and CVT RPM prediction model • Alternator logic calibrated over WLTP
FC module
• Calculation of FC Indicative instantaneous approach
• Based on an extended Willans model • Semi-physical empirical cold start model • Calibration - Optimization based on WLTP
results • Specific engine technologies included
Accurate calculation of average / instantaneous power demand
Very good accuracy when compared with results obtained from the AVL Cruise simulations and real test data
+Parallel work for HEV control module and optimization
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The CO2MPAS Tool Power – RPM Module Overview
Most important module of any model for accurate ΔCO2 calculation • Calculation of Engine Power demand and RPM (inst. or mean values) • CO2MPAS: more detailed version of the WLTP-GTR approach
Pengine = Pwheel / ηdri’train + Pelec. + Pmech. Pwheel = (F0 + F1 x v + F2 x v² + m x a) x v Pelec. = Pelec. dem. / (η alt/or x ηbatt. ) Pmech. = Tconst x RPM RPM is simply calculated from speed and total gear ratio
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The CO2MPAS Tool FC Module Overview
The fuel consumption calculation function:
• Engine speed, temperature, and engine power are considered as knowns from the measurement / other CO2MPAS modules
The constant parameters are calculated by “solving” the above equation on the four sub-cycles of WLTP Low & High
∫↑▒𝐹𝑀𝐸𝑃(𝑡) 𝑑𝑡= ∫↑▒−(𝑎+𝑏∗𝐶↓𝑚 (𝑡)+𝑐∗𝐶↓𝑚 (𝑡)↑2 )+√(𝑎+𝑏∗𝐶↓𝑚 (𝑡)+𝑐∗𝐶↓𝑚 (𝑡)↑2 )↑2 −4∗𝑎↓2 ∗((𝑇(𝑡)⁄𝑇↓𝑡𝑎𝑟𝑔𝑒𝑡 )↑−𝑘 ∗(𝑙+ 𝑙↓2 ∗𝐶↓𝑚 (𝑡)↑2 )−𝐵𝑀𝐸𝑃(𝑡)) /2∗𝑎↓2 𝑑𝑡
, where:
• 𝐶↓𝑚 (𝑡)[𝑚/𝑠]=2∗𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑 [𝑟𝑝𝑚]⁄60 ∗𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑡𝑟𝑜𝑘𝑒 [𝑚]
• 𝐵𝑀𝐸𝑃(𝑡)[𝑃𝑎]= 2∗𝐸𝑛𝑔𝑖𝑛𝑒 𝑃𝑜𝑤𝑒𝑟 [𝑊]⁄(𝐸𝑛𝑔𝑖𝑛𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 [𝑚↑3 ]∗𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑 [𝑟𝑝𝑚]⁄60 )
• 𝐹𝑢𝑒𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛(𝑡)[𝑔/𝑠]= 𝐹𝑀𝐸𝑃(𝑡)[𝑃𝑎]∗𝐸𝑛𝑔𝑖𝑛𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 [𝑚↑3 ]∗𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑 [𝑟𝑝𝑚]⁄60 ⁄2∗𝐹𝑢𝑒𝑙 𝐿𝑜𝑤𝑒𝑟 𝐻𝑒𝑎𝑡𝑖𝑛𝑔 𝑉𝑎𝑙𝑢𝑒 [𝐽⁄𝑔 ]
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The CO2MPAS Tool Data Flow Overview
• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
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The CO2MPAS Tool Data Flow Overview – Data Entry
• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
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The CO2MPAS Tool Data Flow Overview
• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
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• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
The CO2MPAS Tool Data Flow Overview – GUI & Processing
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The CO2MPAS Tool Data Flow Overview
• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
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• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
The CO2MPAS Tool Data Flow Overview – Reporting
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The CO2MPAS Tool Data Flow Overview
• Veh. characteristics
• WLTP Bag CO2 • WLTP time series
Data Entry
• Sub models calibration
• NEDC simulation
Processing
• Simulated time-series
• Summary Report
• Dice report
Reporting
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Gearshi(ing/EngineRPMpred.Logic
CurrentsAlternator
CurrentsBa=ery
FuelConsumpAon
ModelFCM
OpAmizaAon
EngineTemperaturemodel
EngineRPMModel
WLTPCO2(Ini-ales-mate)
WLTPCO2(Calibrated)
Electricpowerdemand
Powerdemand@thewheel
ElectricalSystemLogic
EngineTorqueLosses
CyclePredicAon
Cycle,Roadloads&VehicleCharacterisAcs
WLTPmeasurements
(BagCO2&Timeseriessignals)
LossesGBLosses
DifferenAalLossesTC/Clutch
TotalEnginePowerDemandModel
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Validation & Results Overview
EssenAally,theaccuracyoftheCO2MPAStoolisanalyzedbycomparingtheNEDCCO2emissionpredicAonofthemodel(predictedCO2)againsttheCO2emissionmeasuredduringaNEDCphysicaltest(targetCO2):𝐶𝑂2𝑀𝑃𝐴𝑆 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛= (𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝐶𝑂2 −𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝐶𝑂2)/𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝐶𝑂2 ∗100WhenCO2MPASdeviaAonisposiAve,themodeloveresAmatesthetargetvalue,whilewhenthedeviaAonisnegaAve,themodelunderesAmatesthetargetvalue.
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Validation & Results Datasets
TwocomplementarydatasetshavebeenusedthroughouttheCO2MPASdevelopmentandvalidaAonphases:- RealCarsdata:
GatheredalongAme(JRClabs,LAT,mock-upacAviAes,etc.)Setof48realvehicles
- Synthe-cCarsData: DerivedwithAVLCruisemodelusingOEM-approvedinputdataLargesetofvehicleswithdifferentRLs,masses,availabletechconfiguraAons-ManualTransmissionVehicles:~2,150cases-Automa4cTransmissionVehicles:~1,400cases
ThepublichistoryofCO2MPASvalidaAonresults,alongthedifferentversionsofthetool,isavailablehere:h5p://jrcstu.github.io/co2mpas/DataareanonymizedtoensureconfidenAality.
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Validation & Results Results - Real Cars (v.1.7.x)
NEDC-H [%] NEDC-L [%]
Averages -0.31 -1.34
StdDev 2.51 2.65
Cases in ±4% 93.6 87.5
Cases in ±2.5% 61.7 68.8
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Validation & Results Real Life Usage Statistics - 194 Cars as of 21/09
Engine capacity list Number of DICEs < 1000 5
1000 - 1400 1 1400 - 1800 28 1800 - 2200 124
> 2200 36 Fuel type Number of DICEs Gearbox type Number of DICEs Diesel 181 Manual 57 Gasoline 13 Automatic 137
CO2 error [%]
Vehicle High
Vehicle Low
Count 192 175 Mean 2.42 0.077 STD 4.24 5.88 Min -11.86 -37.2 25% 0.056 -0.87 50% 2.58 0.78 75% 4.23 2.66 Max 19.3 10.78 ±4% 64 80.57
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Further Applications What else?
Building on the work performed for the development of the CO2MPAS tool, two additional tools have already been developed: ü The Green Driving Tool (https://green-driving.jrc.ec.europa.eu/)
An interactive tool for evaluating CO2 emissions and costs for different types of vehicles, users selected in-use options and routes
ü The PyCSIS Tool A simulation-based quantification methodology for the detailed calculation of passenger cars fleet test-based & real-life CO2 emissions
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Further Applications The Green Driving Tool
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Further Applications The PyCSIS Tool
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Further Applications The PyCSIS Tool – 2015 EU Fleet CO2 Emissions
Examples of analysis & results calculated with PyCSIS
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Thank you for your attention Questions?
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Stay in touch
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