Development and performance of a new vehicle emissions and fuel consumption software (PΔP) with a high resolution in time and space

©Author(s)2013.ThisworkisdistributedundertheCreativeCommonsAttribution3.0License.

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Development and performance of a new vehicle emissions and fuel

consumption software (P

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P) with a high resolution in time and space

Robin

Smit

TransportEmissionResearch,16MayleenStreet,Clontarf,QLD4019,Australia

ABSTRACT

Thispaperreportsonthedevelopmentandperformanceofanewsimulationtoolforroadvehicles.TheP'Pmodel

predictssecond–by–secondfuelconsumption,airpollution(NOX)andgreenhousegasemissions(CO2)withahigh resolutionintimeandspace.Itusesenginepowerandthechangeinenginepowerasthemainmodelvariablesto

simulatevehiclefuelconsumptionandemissionsforallrelevantvehicleclassesincludingcars,SUVs,light–commercial

vehicles,rigidtrucksandarticulatedtrucks.Atotalof73vehicleclassesaremodeledaccountingformainvehicletype,

fueltypeandtechnologylevel.ThemodelusesdatafromalargeverifiedAustralianemissionsdatabasecontaining

around2500modalemissiontests(1Hz)andabout12500individualbagmeasurements.Theminimuminput

requirementsforthemodelarespeed–timedata(1Hz)andvehicletypes.Thiskindofinformationistypicallyavailable

frommicroscopictrafficsimulationmodels,on–roadmeasurementsorexpertjudgment.Theuserofthemodelcan

alsospecifytheroadgradient,thevehicleloadingandtheuseofairconditioning.Defaultvaluesareprovidedforeach

ofthesewherelocation–specificdataareunavailable.TheP'Pmodelaimsforanoptimumbalancebetweenmodel

complexityandpredictionaccuracy.TheperformanceresultsfortheP'Pmodelresultsaregoodwith,forinstance,

averageR2valuesof0.65and0.93forNOXandCO2/fuelconsumption,respectively.Thisperformancecompareswell withthatreportedforothermodelswithdifferentcomplexity.Theemissionalgorithmsareshowntoberobustwith

respecttopredictionerrors.Aggregationofthe1Hzpredictionresultsintime/space(e.g.100mroadsegments)and

acrossvehicleclasses(e.g.passengercar,SUV,articulatedtruck,etc.)furtherimprovespredictionperformance. Keywords:Roadtransport,emission,fuelconsumption,highresolution,roadtraffic CorrespondingAuthor:

Robin Smit

:+61Ͳ7Ͳ3885Ͳ2914 :[email protected]

ArticleHistory: Received:17March2013 Revised:07June2013 Accepted:09June2013 doi:10.5094/APR.2013.038

1.

Introduction

Road transport is a major source of air pollution and greenhousegasemissionsaroundtheworld.ModelsarecommonͲ lyusedtoestimatefuelconsumptionandairemissionsforroad transport.Thisisbecausemeasurementsareoftennotfeasible, frombothatechnicalandcostperspective,duetothelarge numberofvehiclesthatoperateonourroadsandthemanyfactors thatinfluenceemissionlevels.Modelsarealsorequiredtomake projectionsintothefuture.

Similartotransportmodels,ahierarchyofvehicleemission modelscanbedistinguishedbasedonthelevelofcomplexityand typesofapplication(Smitetal.,2010).Theseinclude“average– speed” models(e.g. COPERT, MOBILE),whereemissionrates (g/vehkm) areafunctionofmeantravellingspeed,“traffic– situation”models(e.g.HBEFA,ARTEMIS),whereemissionfactors (g/vehkm)correspondtoparticulartrafficsituations(e.g.“stop– and–go–driving”,“freeflow”)and“modal”models(e.g.PHEM, CMEM,MOVES),whereemissionfactors(g/sorg/drivingmode) correspondtospecificengineorvehicleoperatingconditions. Whereasaveragespeedandtrafficsituationmodelsaredesigned tooperateatthenationalorcitynetworklevel,modalmodelsare designedforlocalassessments.

Thereareanumberofdevelopmentsthatareexpectedto leadtoanincreaseduseofmodalmodels.Firstly,thereisan increasingfocusonthereductionofpopulationexposuretoair pollutionand(health)risk.Asaconsequence,itwillbeimportant toknowexactlywhichpartsofthepopulationareexposedto

relativelyhighairpollutionlevels(e.g.nearbusyroads),whatthe levelofimpactis,andwhenthisoccurs.Thistypeofassessment requiresafinespatialandtemporalallocationofvehicleemissions instudyareas,whichcanbeachievedwithmodalmodels.

Secondly,thereisincreasinginterestaroundtheworldinthe effects of local traffic conditions on traffic emissions, fuel consumptionandexposuretoairpollution.Forinstance,applicaͲ tionofadaptivetrafficcontrolmeasuresisgrowingtoimprove trafficflow(toalleviatecongestion),improvereliabilityandreduce accidents(Akcelik,2006;NolandandQuddus,2006).Itisessential toknowifthesemeasuresadverselyaffectorimproveairpollution andgreenhousegasemissions.Sensitivemodelsaretherefore neededtoaccuratelypredictthecorrectdirectionandmagnitude ofthese effects asthis kind ofmeasure typicallygenerates relativelysmallbutstillsignificantimpacts.

Thirdly,substantialimprovementsareexpectedwithrespect tothequantity,qualityandlevelofdetail(resolution)oftraffic data,whichareessentialinputstovehicleemissionmodels.For instance,widescaleautomatedcollectionofreal–timefielddata onvehiclemovementintimeandspace(usinge.g.GPS,video imagingtechnology)isnowbecomingarealityduetoongoing developmentsin,andapplicationof,intelligentsensor,communiͲ cationsandcomputingtechnologyinvehiclesandattheroadside (Hooseetal.,2008).

Finally, vehicle testing programs in which emissions are measuredatahighresolutionintime(typically1–10Hz)are increasingly common. This creates opportunities for the

manydifferentvehicles)thatcanthenbeusedfordevelopmentof accuratemodalmodels.

Thispaperreportsonthedevelopmentofanewmodalmodel, theP'Pmodel,whichisbasedonhighresolutionAustraliantest data.Theobjectivesofthisresearcharetodevelopamodelthatis comprehensive,accurate,reliableandrobust,easytouseand which interfaces readily with appropriate traffic models and (emerging)trafficfielddata.

2.

Modal

Vehicle

Emission

Models

Modalmodelsvaryinlevelofcomplexityanddemandfor inputdata.Themostcomplexmodalemissionmodels(e.g.Barth etal.,2000;Atjayetal.,2005)aredeterministicandcompute instantaneousemissionrates(g/s)asafunctionofvariousengine variables (e.g. engine speed and load, injection timing, oil temperature,air–to–fuelratio).Algorithmscanbeincludedto simulate the effects of emission–control technology such as catalysts.Thesemodelsrequireasubstantialamountofdetailed inputdata,whichmaynotalwaysbeavailabletomodelusersat trafficstreamlevel,ortoodifficultorevenimpossibletoobtain (e.g.gearshiftbehaviorintrafficstreams).Inordertoaddressthis problem,partoftherequiredinputdatamaybesimulatedwithin thesoftware.Gearshiftbehavior,forinstance,isusuallysimulated tocomputeinstantaneousengineloadandenginespeed.This, however,introducesunknownerrorstothemodelpredictions, whichmayoffsetaccuracygainsfromdetailedmodeling.

Aparticularissueisthenumberoftestvehiclesonwhichthe modelisbased,andtheextenttowhichthemodelrepresentsthe on–roadvehiclefleet.Withrespecttotheemissionalgorithms,itis clearthataccurateemissionmodelsneedtobebasedonmeasureͲ mentsonalargenumberofvehiclesinvariousdrivingconditions toadequatelycaptureandreflectthelargevariabilityinemissions behaviorofdifferentvehicles(evenamongvehiclesofthesame brandandmodel).Giventhetimerequiredtodevelopcomplex modalmodels,thenumberofvehiclesandvehicletypesincludedis typicallylimited.

Thisraisesthequestionifmodelcomplexitycanbereduced withoutcompromisingpredictionaccuracy.Thiswouldmakea modeleasierandlesscostlytouse.Afewvalidationstudies (Lacouretal.,2001;Smitetal.,2010)haveshownthatmore complexemissionmodelsdonotnecessarilyleadtomoreaccurate predictions,whichseemstosupportthisnotion.

Simplifiedmodalmodelshavebeendeveloped,rangingfroma relativelysimplefundamentaldrivingmodemodel(Midenetetal., 2004),aninstantaneousspeed/accelerationmodel(Rakhaetal., 2004),toapower–basedmodel(SonntagandGao,2007).TheP'P modelaimsforanoptimumbalancebetweenmodelcomplexity andpredictionaccuracy.Withrespecttothelastcriterion,itis notedthatpredictionerrorsdependonbothaccuracyofmodel inputandaccuracyofmodelalgorithms.Thequalityoftrafficinput data(e.g.trafficvolume,roadlength,speed–timeprofile)isa relevantissuewithrespecttopredictionaccuracy,andithasbeen foundthataccurateinputdataareatleastasimportantas accurateemissionalgorithms(Smit,2008).

3.

P

'

P

Model

Approach

Previous investigations haveshown thatvehicleemission models need to reflect local fleet composition and driving characteristicstoprovideadequatevehicleemissionpredictions. Largeerrorswerefoundwhenoverseasmodelsweredirectly appliedtoAustralianconditionswithoutcalibration(e.g.Smitand McBroom,2009),becausethesemodelsdonotreflectAustralian vehicles,fuels,climate,fleetcompositionanddrivingconditions. Indeed,thiswasthemainreasonforthedevelopmentofa

“COPERTAustralia”(SmitandNtziachristos,2012).

TheP'Pmodelusesenginepower(P,kW)andthechangein enginepower('P,kW)asthemainmodelvariablestosimulate vehiclefuelconsumptionandCO2andNOXemissions.Themodel usesasimilarvehicleclassificationasCOPERTAustralia,whichis basedonthecombinationoffueltype(petrol,diesel),mainvehicle typeandADRcategory.ADRsreferto“AustralianDesignRules”, whicharetheemissionstandardsadoptedinAustralia.ADRshave beenalignedwithEUstandardsfromabout2003(before2003US standardswereused).Atotalof73vehicleclassesaremodeled. Themainvehicletypesaredefinedassmallpassengercar(PC–S, enginecapacity<2L),mediumpassengercar(PC–M,2–3L),large passengercar(PC–L,>3L),compactandlargeSUVs(SUV,4WDs), light–commercialvehicle(LCV,GVMd3.5t),rigidmediumcommerͲ cialvehicle(MCV,GVM3.5–12.0t),rigidheavycommercialvehicle (HCV,GVM12.0–25.0t),articulatedtruck(AT,GVM>25t),lightbus (BUSͲL,GVMd8.5t)andheavybus(BUSͲH,GVMd8.5t).ElevenADR categoriesareincludedrangingfromuncontrolledtoADR79/02 (Euro4)andADR80/02(EuroIV).

The input to themodelisspeed–timedata(1Hz)and information on road grade, vehicle loading and use of air conditioning(on/off).Thisinformationisusedtocomputethe required(changein)enginepowerforeachsecondofdriving.The vehicleemissionalgorithmsweredevelopedinthreedistinctsteps:

x Creation of a verified empirical database for model development.

x Developmentofmathematicalrelationshipsbetweenempirical emissionsdataandenginepower.

x DevelopmentoftheP'Psimulationtoolforon–roaddriving conditions.

Thesestepsarediscussedinthefollowingsections.

4.

Empirical

Data

Alargenumberofvehicleemissionstestsareavailablefrom variousAustraliantestprograms.Theseemissionsdatahavebeen collatedinaverifiedemissionsdatabasewithabout2500modal emissiontests(1Hz)andabout12500individualbagmeasureͲ ments.Themodaldatafilescontaintypicallyaround30minutesof laboratory–gradesecond–by–secondemissionsandspeeddata basedonreal–worlddrivingcyclesthatweredevelopedfromon– roaddrivingpatterndatainAustraliancities.Thereal–world drivingcyclesweredevelopedforfourdistincttrafficsituations– congested(Con),residential(Res),arterial(Art)andfreeway(Fwy)– andfivemainvehicleclasses–light–dutyvehicles(LDVs),medium commercialvehicles(MCVs),heavycommercialvehicles(HCVs), articulatedtrucks(ATs)andbuses(BUSs)–toreflectthedifferent speed–acceleration characteristicsdue to different power–to– massratios.ThecyclesareshowninFigure1.

Inadditiontothereal–worldcycles,modaldatafromthe DT80testcyclehavebeenused.TheDT80testisanAustralianin– serviceemissionstest that is conductedto assessemissions performanceofon–roaddieselvehicles.Thetestsimulatesworst– casedrivingconditions(e.g.fullopenthrottleacceleration,high cruisespeeds)inordertocaptureworst–caseemissionlevels.This isusefulasitensuresthatemissionsdataareavailableoverthefull rangeofoperatingconditions,includingextremeaccelerations.

Allmodalemissionstestdataweresubjectedtoaverification and correction protocol (Smit and Ntziachristos, 2012). This included time re–alignment, verification of emission traces (analyzerdrift,clipping)andcomputationandverificationoftest statistics(e.g.BSFC,meanthermalefficiency).

Figure1.Australianreal–worlddrivingcyclesbytrafficsituationandmainvehicletype.

Foreachofthevehicleclasses,onerepresentativevehiclewas selectedformodeldevelopmenttakingintoconsiderationmean fuelconsumptionandemissionlevelsascomparedtotheclass averagevalues.Theempiricaldatafilescontainedemissionsand speed–timedataforthereal–world driving cycle and,when available,theDT80drivingcycle.Thesefileswerethensplitinto twodatafiles,oneformodeldevelopment(“Con”,“Res”,“Fwy”, “DT80”)andoneformodelvalidation(“Art”).

5.

Model

Development

Thefirststepwastodevelopamathematicalrelationship betweenenginepowerandemissionmeasurementsduringthe tests.Allmeasuredsecond–by–second(1Hz)speed–timedata weresmoothedandallspeedslessthan0.5km/hweresettozero. On–roadspeedmeasurementsandrecordeddrivingpatternsare discreteintimeandvalueduetomeasurementmethodsor numericalimprecision.Pre–processingofdiscretespeed–timedata (smoothing)wasrequiredtoaccountfornoiseinthespeed–time dataandtopreventsignificanterrorsinthecalculationofthe computed time–series of acceleration and engine power, in particularathigherspeedswhereunsmoothedspeedscanleadto largedifferencesinpredictedenginepower.Smoothedspeed– timeprofileswerecreatedusingaT4253H(runningmedianand Hanningfilter)smoothingalgorithm(Velleman,1980),whichis recommendedfordrivingcycleanalysisanddevelopment(UNECE, 2008).Theeffectofsmoothingonaveragedrivingcyclepoweris generallysmallwithamaximumofafewpercent.However,the effectscanbesubstantialinspecificpartsofthetestcyclewhere extremepeaksinpowerareremoved.Thesmoothedprofileswere usedtocomputeandaddtime–seriesofacceleration,engine powerandnormalizedenginepowertothemodaltestfiles. Acceleration(at,m/s2)iscomputedas:

2 1 2 1 1 1 1 1 1 1

1

2

1

t t t t t n n n n

v

t

t

t

a

v

t

t

t n

v

t

t

t n

Q

Q

Q

y

y

d d

y

­

°

®

°¯

(1)

whereQtrepresentsinstantaneous(smoothed)vehiclespeed(m/s)

attimet,whichvariesfromthecyclestarttimet=1tothecycle endtimet=n.Inordertocomputeenginepower,dynamometer power(Pt*,kW)wascomputedforeachmodaltestusingthe

dynamometerloadalgorithms,whichvariedwiththedifferenttest programs. For instance,one of the test programs used the dynamometeralgorithmsforpetrolLDVsasspecifiedinADR79/01 (Euro2):

2

D

E Q

Q

* t t t t

P

M a

(2)

whereDandErepresentpowerabsorptioncoefficients[Nand N/(km/h)2,respectively]andMisthevehicletestmass(kg).Typical valuesforpassengercars,SUVsandLCVsare7.10,10.53,11.44N forD,0.04810,0.07241,0.07865N/(km/h)2forEand1360,1810 and2150kgfortestmass,respectively.Othertestprogramsused proprietary dynamometer loading algorithms where Pt* is a

functionofQt,at,M,aswellasaerodynamicdragcoefficientand

frontalarea.Enginepower(Pt,kW)wascomputedforeachsecond

ofdrivingusingthesestudy–specificdynamometeralgorithmsin combinationwithadditionalalgorithmstosimulateinternalvehicle lossesduetodrivetrainandtirerollingresistancesthatarenot accountedforinthedynamometeralgorithms(Rexeisetal.,2005). Thevehicleemissionrate(et,g/s)wasthenfittedtothefollowing

equation: 0 50 100 150 200 250 300 350 04 0 80 LDV | Con Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 500 04 0 80 LDV | Res Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 LDV | Art Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 500 04 0 80 LDV | Fwy Elapsed Time (s) S peed [ km /h] 0 50 100 150 200250 300 04 0 80 MCV | Con Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 MCV | Res Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 MCV | Art Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 500 600 04 0 80 MCV | Fwy Elapsed Time (s) S peed [ km /h] 0 50 100 150 200 250 300 04 0 80 BUS | Con Elapsed Time (s) S peed [ km /h ] 0 100 200 300 400 500 04 0 80 BUS | Res Elapsed Time (s) S peed [ km /h ] 0 100 200 300 400 04 0 80 BUS | Art Elapsed Time (s) S peed [ km /h ] 0 100 200 300 400 04 0 80 BUS | Fwy Elapsed Time (s) S peed [ km /h ] 0 50 100 150 200 250 300 04 0 80 HCV | Con Elapsed Time (s) S pee d [ km /h] 0 100 200 300 400 500 04 0 80 HCV | Res Elapsed Time (s) S pee d [ km /h] 0 100 200 300 400 04 0 80 HCV | Art Elapsed Time (s) S pee d [ km /h] 0 100 200 300 400 500 04 0 80 HCV | Fwy Elapsed Time (s) S pee d [ km /h] 0 100 200 300 04 0 80 AT | Con Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 AT | Res Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 AT | Art Elapsed Time (s) S peed [ km /h] 0 100 200 300 400 04 0 80 AT | Fwy Elapsed Time (s) S peed [ km /h]

2 2 0 1 2 3 4 5 0 E E E ' E E ' E ' H Q !

­

®

¯

t t t t t t t t P P P P P P e (3)

Foridlingconditions(Qt=0km/h)aconstantaveragevalue

(g/s) is used. For non–stationary (moving vehicle) driving conditionsamultivariateregressionfunctionhasbeenfittedusing theordinaryleast–squaresmethod,whereE0,…,E5representthe

regressioncoefficients.Residualanalysis(Neteretal.,1996)was usedtoverifythattheassumptionsoftheregressionanalysiswere notviolated.Thevariable'Ptquantifiesthechangeinpowerover

thelastthreesecondsofdrivingandiscomputedas: 2 t t t

P

P

P

'

(4)

'Ptaimstoinclude“historyeffects”intothemodel.Thisis

importantbecausevehicleoperatinghistorycanplayasignificant roleinaninstantaneousemissionsvalue,forinstanceduetothe useofatimertodelaycommandenrichmentoroxygenstoragein thecatalyticconverter(e.g.Barthetal.,2000),butalsodueto inertiaeffectsthatspanoverseveralsecondsofdriving.Table1 showsanexampleofmodelandvehicleparametersthatareused intheemissionssimulation.NotethatsomeparametersinTable1 willbediscussedlaterinthispaper.

6.

Model

Performance

Thissectiondiscussesbothmodelverificationandmodel validation.Modelverificationassesseshowwellamodelpredicts thedataonwhichitisbased,whereasmodelvalidationassesses howwellamodelpredictswithrespecttoindependentdata.The empiricaldataweresplittoenablebothassessments.

TheP'Pmodelgenerallypredictsfuelconsumptionratesand CO2emissionrates(g/s)quitewellwithacoefficientofdeterminaͲ tion(R2)rangingbetween0.80–0.98,andanaveragevalueof0.93. Thismeansthatapproximately80%to98%ofthevariationin instantaneousemissionscanbeexplainedwiththealgorithms.For NOXtheresultsaremorevariablewithR2valuesvaryingfrom 0.17–0.90,andanaveragevalueof0.65.Figure2showsfour goodness–of–fitplotswiththebestandworstmodelswithrespect toR2foreachpollutant,whereeachdotpointsrepresentsone secondofdataofthetestcycles.

Root–Mean–Square–Error (RMSE) is a frequently used measureofthedifferencesbetweenpredictionsandobservations. Itaggregatesthesecond–by–seconderrorsintoasinglemeasure ofpredictivepower.NormalizedRMSEisusedtomakeRMSE scale–independentanditiscomputedbydividingRMSEbythe rangeofobservedvalues.NormalizedNRMSEvariesfrom2–12% forfuelconsumptionpredictionsand3–21%forNOXemissions predictions.

Itisinstructivetoshowtime–seriesplotsofpredictedand observed emissions and the speed–time profile used during emissionstesting.Figure3showsanexampleforanADR79/01 (Euro4) dieselpassengercar. The blackline representsthe observationsandthereddotpointspredictions.Themodelforthis vehicleclasspredictsfuelrates(andhenceCO2emissions)well withanR2of0.94andaroot–mean–squarederror(RMSE)of 0.26g/s.Thisisalsothecasefortheemissionpeaks,whichare importanttoassesslocaleffectsofchangesindrivingbehavior (e.g.duetochangesinsignalsettingsatanintersection).Notethat the extreme DT80 test is included (t=1417–1668 seconds), showingthehighestfuelratesinthecombinedtest.

Table1.ModelandvehicleparametersforanADR80/00(EuroIII)heavydieselbus(BUS–H–Diesel)

Parameter Value Unit

VehicleParameters

Make Volvo

Model B12B

Type Bus

A–Frontalarea 6.5 m2

Cd–Aerodynamicdragcoefficient 0.62

R0–R5–Rollingresistancecoefficients 0.000000–0.00715

Taremass 14500 kg

GVM 23500 kg

Ratedenginepower 313 kW

Enginecapacity 12.0 L D 0.94 g/s ModelParameters E0 2.14 g/s E1 0.04045 g/skW E2 0.00077 g/skW E3 0.00006 g/s/kW2 E4 0.00002 g/s/kW2 E5 0.00006 g/s/kW2 M 1.001 Emax.obs 18.41 g/s I 1.05

FC/CO2,RigidDieselTruck(MCV), Uncontrolled,MY1989(R2 =0.80)

FC/CO2,ArticulatedDieselTruck, ADR70,MY2000(R2=0.98)

NOX,PetrolSUV,ADR79/01,MY2006(R2=0.17)

NOX,PetrolCar,ADR37/00,MY1995(R2=0.90)

Figure2.Goodness–of–fitplots(R2)foraselectionofvehicles.

Figure3.Topchart:speed–timeprofile,middlechart:fuelconsumptiontime–seriesplotsfordieselpassengercar(PC–M),MY2007,ADR79–01(Euro4), bottomchart:NOXtime–seriesplotsforpetrolSUV,MY2006,ADR79–01(Euro3),second–by–secondpredictions(reddots)andobservations(blackline). 0 1 2 3 4 5 6 01234 56 Observations [ g/s ] P redic tions [ g/ s ] 0 5 10 15 0 5 10 15 Observations [ g/s ] P redic tions [ g/ s ] 0.000 0.001 0.002 0.003 0.004 0. 000 0. 001 0. 00 2 0. 003 0. 004 Observations [ g/s ] P redic tions [ g/ s ] 0.00 0.02 0.04 0.06 0.08 0.10 0. 00 0. 02 0. 04 0. 06 0. 08 0. 10 Observations [ g/s ] P redic tions [ g/ s ]

theworstperformingmodelforNOX.Themodelforthisvehicle classpredictsNOXemissionspoorlywithanR2of0.17andaroot– mean–squarederror(RMSE)of0.004g/s.Itisinterestingtonote thatthemodelisunabletoreproducetheemissionpeaks,which meansthattheyarenotrelatedtopowerandthechangein power.Giventhelowemissionratesforthisvehicle,itislikelythat thepeaksarecausedbysmalldeviationsintheair–to–fuelratio, whichaffectcatalystefficiency,butwhichwillnotbereflectedin thePand'Pvalues.Thiseffectbecomesmoreimportantand morepronouncedwhenmodernvehicleswithlowNOXemissions arecomparedwitholdertechnologyvehicleswithhigherbaseNOX emissionlevels.ThisisclearfromFigure2wherea1995petrolcar withanaverageemissionrateof9.41mg/sshowsasubstantially bettermodelfitthena2006petrolSUVwithanaverageemission rateof0.17mg/s.Thisresultexposesalimitationinpowerbased modelingatahighresolutionforsomemodernpetrolvehicles. Thisproblemhasalsobeenreportedbyotherresearchersaround theworld,aswillbediscussedinSection7.DeHaanandKeller (2000),forinstance,reporteddifficultieswithconstructingamodal emission model that could accurately simulate the irregular emissionsbehaviorof(atthetime)moderncars.

Therearehowevertwoaspectsthatwillreduceprediction errors.Firstly,emissionsfromindividualvehicleclassesarenotof particularinterestintermsofmodelapplication.Theamountof travel(expressedasvehiclekilometerstravelledorVKT)foreach vehicleclasschangeswithtime,asnewvehiclesenterthefleetand oldvehiclesareprogressivelyremovedfromthefleet.SotheVKT– weightedsumofemissionsfromallvehiclesclassesisneededto

greenhouse gas emissions. As a consequence, overall model performancetendstowardsanaverageoftheperformanceof individualvehicleclasses.

Secondly, spatial/temporalaggregationof predictions will reducepredictionerrors.ThisisillustratedinFigure4forone vehicleclassfor fourdifferent spatialresolutions, i.e. 100m segments,500msegments,1000msegmentsandthecycles “Con”, “Res” and“Fwy”.It isclearthatmodelperformance improvesandpredictionerrorsarereducedwithdecreasingspatial resolution,althoughthesmallesterrorisobservedfor1000m segments.

Finally,withrespecttomodelbias,theemissionsprofileover theentiredrivingcycle(combinationof“Con”,“Res”,“Fwy”and “DT80”)isreplicatedwelleventhoughthereisadifferencein modelperformancefortheindividualvehicleclasses.Forfuel consumptionandCO2emissions,totaldrivingcycleemissions(g) arewithinr1%ofobservedvalues,whereasforNOXtheseare withinr3%.

6.2.Modelvalidation

The emission algorithms were used to predict fuel consumptionandemissionsforthevalidationdataset,i.e.the “Arterial”drivingcycle,whichwasnotusedinmodeldevelopment. Acomparisonbetweenthemodelvalidationandmodelverification resultswithrespecttomodelperformanceisshowninFigure5. Figure4.Exampleoftheeffectofspatialaggregationonmodelperformance(fuelconsumption). Figure5.Comparisonofmodelperformanceforverificationandvalidationdata(eachdotpointrepresentsavehicleclass). 0 200 400 600 0 200 40 0 60 0 100m Observations [ g/km ] P re d ic tio n s [ g /k m ] R = 0.952 RMSE = 31.2 g 0 100 200 300 400 500 0 100 20 0 300 400 50 0 500m Observations [ g/km ] P re d ic tio n s [ g /k m ] R2 = 0.97 RMSE = 15.9 g 0 100 200 300 400 500 0 100 20 0 300 400 50 0 1000m Observations [ g/km ] P re d ic tio n s [ g /k m ] R = 0.982 RMSE = 12.6 g 0 100 200 300 400 0 100 20 0 300 400 Cycles Observations [ g/km ] P re d ic tio n s [ g /k m ] R = 12 RMSE = 22.9 g 0.0 0.2 0.4 0.6 0.8 1.0 0. 0 0. 2 0.4 0.6 0.8 1. 0 FC/CO2 R2 Verification R2 V a lid a tio n 0.0 0.5 1.0 1.5 0. 0 0.5 1.0 1.5 FC/CO2 RMSE Verification RM S E Va lid at io n 0.0 0.2 0.4 0.6 0.8 1.0 0. 0 0. 2 0.4 0.6 0.8 1. 0 NOx R2 Verification R2 V a lid a tio n 0.00 0.05 0.10 0.15 0.00 0. 05 0.10 0.15 NOx RMSE Verification RM S E Va lid at io n

Withrespecttopredictionerrors(RMSE)thevalidationand verificationshowquitesimilarresultsforbothfuelconsumpͲ tion/CO2andNOX.Thisdemonstratesthattheemissionalgorithms arerobustwithrespecttopredictionperformance.Thesame appliesforR2forfuelconsumptionandCO2,buttheresultsare morevariableforNOX.

Interestingly, the more stringent model validation step exhibitsimprovedmodelperformanceinseveralcases.Figure6 showsthiseffectforonevehicleclass,i.e.LargeADR79/01(Euro3) PetrolPassengerCar,wheretheR2valuesare0.31and0.59for verificationandvalidation,respectively,andtheRMSEvaluesare similar(0.010g/sforboth).

Figure7showstheresultswithrespecttomodelprediction bias.Ascomparedwiththeverificationstep,thereisasignificant increaseinsystematicpredictionerrors,withtypicalvaluesofr5% forfuelconsumptionandCO2emissionsandofr50%forNOX emissions.Itis,however,clearthatlargebiasonlyoccursfor vehicleclasseswithrelativelylowemissionlevels.Onarterialroads thereappearstobeatendencyforunder–predictionoffuel consumption and CO2 emissions, with an average value of approximately–5%.TheaveragebiasforNOXemissionsissmallat –1%,despitethelargebiasforsomevehicleclasses.Thismeans thatatthefleetlevellargesystematicpredictionerrorstendto canceleachotherout.

7.

Development

of

the

Simulation

Tool

The emission algorithms discussed in Section 5 predict second–by–secondfuelconsumptionandemissions.However,a fewmorestepsarerequiredtocreatetheP'Psimulationtool:

x Calibrationtoaveragevehicleclassemissions.

x Simulationalgorithmsforon–roadenginepower.

x Setting model prediction boundaries (100m minimum distance,capmaximumprediction/extrapolation).

7.1.Calibrationtovehicleclassaveragedemissions

Itisimportantthattotaldrivingcycleemissionsforthe vehiclesusedinmodeldevelopmentmatchthoseoftheaverage valuesofsimilarvehiclesintheempiricaldatabase.Acalibration factorMisthereforeintroducedandcomputedastheratiooftotal cycleemissions(g)forthevehiclesusedinmodeldevelopmentto averagetotalcycleemissionsofalltestedvehiclesofaparticular vehicleclass,inthesametestconditions(drivecycle,etc.).Vehicle emissionratesinthesimulationtool(et*,g/s)arethencomputed

as: t * t

e

e

M

(5)

Figure6.NOXtime–seriesplotsforlargepetrolcar,MY2006,ADR79–01(Euro3),topchart:speed–timeprofile,bottomchart:

second–by–secondpredictions(reddots)andobservations(blackline).

Figure7.Biasinthevalidationstepasafunctionofobservedcycleemissions (eachpointrepresentsavehicleclass). 0 200 400 600 800 -1 0 -5 0 5 1 0

FC/CO2

Observed Cycle [ g/km ] Bi as (% ) 0 5 10 15 20 25 -1 50 -1 00 -50 0 50 10 0 150

NOx

Observed Cycle [ g/km ] Bi as (% ) Verification

Validation

TypicalvaluesforMare0.9–1.2forfuelconsumptionandCO2 emissions,and0.6–1.7forNOXemissions.

7.2.On–roadpoweralgorithms

Forthesimulationtool,anestimateofsecond–by–secondon– roadenginepowerdemandisrequired,whichreflectstheimpacts ofvehicleloading,roadgradeanduseofauxiliaries.Theon–road enginepowerpredictionmodelconsistsofasetofalgorithmsthat quantifytheresistiveforcesthatareexertedonthevehiclewhile driving.Amotorvehiclerequiresenginepowertoovercomeall theseresistiveforcesandtorunitsaccessories(e.g.airconditionͲ ing).FortheP'Pmodel,poweralgorithmshavebeenadopted fromRexeisetal.(2005).Thetotalsecond–by–secondengine power(Pt,kW)thatisrequiredtopropelavehiclealongaroad,can bebrokendownintosixpowercomponents:

, , , , , ,

t rolres t air t inert t grade t transm t aux t

P

P

P

P

P

P

P

(6)

whereProlres is the power required to overcome tirerolling

resistance (kW), Pair is the power required to overcome

aerodynamicresistance(kW),Pinertispowerrequiredtoovercome

inertialresistance(kW),Pgradeisthepowerrequiredtoovercome

gravitationalresistance(kW),Ptransmispowerrequiredtoovercome

drivetrainresistance(kW)andPauxisthepowerrequiredtorun

auxiliaries(kW).Thepowercomponentsarepredictedforeach secondofdrivingandrequireinputonspeed,acceleration,road grade,vehiclemass(includingloading)anduseofairconditioning. Thesealgorithmsalsorequirevehiclespecificinformationsuchas aerodynamicdragcoefficient,frontalareaandrollingresistance coefficients.Thisvehiclespecificinformationwascollectedforall vehiclesusedinthisresearchprojectandhardcodedintothe software.

7.3.Operationalboundaries

Finally,a fewoperationalboundariesareapplied tothe emissionsimulation.Firstly,instantaneousPtand'Ptvaluescannot

exceed110%oftheminimumandmaximumvaluesencountered during the tests. Secondly, emission rates are capped at a maximum value which is dependent on the vehicle test (Emax=Emax.obsuI).Iftheratioofthemaximumenginepowerinthe

testtotheratedenginepowerislessorequalto50%,thenthe maximumrateissetto1.50(I=1.50)timesthemaximumobserved valueEmax.obs.Iftheratioisbetween50–75%,orlargerthan75%,

thenthemaximumemissionrateissetto1.25and1.05timesthe maximumobservedvalue,respectively.ItisnotedthatIvaluesare setarbitrarily,butareexpectedtobereasonable.

The simulation will also check for the occurrence of unrealisticallyhighenginepowerduringthesimulation.Thiscould occur,forinstance,whenaLDVdrivingcycleisusedforan articulatedtruck.InthiscasethetruckcannotdelivertheacceleͲ rationratesrequiredtofollowthespeed–timeinputdataandthe ratedpowerofthetruckwillbeexceeded.Thesimulationwillnot reporttheresultsforthesesituationsifratedenginepoweris exceededmorethan5%ofthetime.

8.

Example

of

Simulation

Output

Figure8showsanexampleofsimulationinputandoutputfor threeinputdrivingcycles(“Congested”,“Arterial”,and“Freeway”). Itisnotedthattheresultareaggregatedtofourteenmainvehicle typesusingbaseyeardependenttravelͲbasedweightingfactorsfor thevariousADRcategories.Theemissionfactors(g/vehkm)can thenbecombinedwithdataontrafficvolumeandroadlengthto computetotalemissionsforeachmainvehicletypeinthesethree trafficconditions.

9.

Discussion

Thispaperdiscussedthedevelopmentandperformanceofa new high resolution emission simulation model based on Australiantestdata.Theapproachhasafewinnovativeaspects suchastheuseofdeltapowerinmodelpredictions,calibrationto and connection with a larger emissions database, explicit considerationofanappropriatespatialandtemporalscaleanda comprehensivecoverageofon–roadvehicleclasses.Theobjectives of the research project were to develop a model that is comprehensive,accurate,reliableandrobust,easytouseand which interfaces readilywith appropriate trafficmodelsand (emerging)trafficfielddata.

9.1.Comprehensiveness

Vehicleemissionmodelscanbe“incomplete”becausethey predict emissions for specific vehicle categories only (e.g. passengercars)orbecausetheyareoutdated(e.g.basedontest datathat do not reflectthelatest developments invehicle technology),effectivelyrestrictingpredictionstoaspecificpartof theon–roadfleet.TheP'PModeliscomprehensivebecauseit includesallrelevantvehicleclassesanditisbasedonalarge empiricaldatabasethatincludesrecenttechnologyvehicles.The currentmodelincludesADRcategoriesuptoADR79/02(Euro4) andADR80/02(EuroIV).Newerandfuturestandardscanbe readilyincorporatedbyusingtechnologyspecificscalingfactors (NtziachristosandSamaras,2001)anddevelopmentofadditional algorithmsoncenewemissionsdatabecomeavailable.

9.2.Accuracy

The performance resultsfor the P'P model results are generallygoodcomparedtoothermodels.Forinstance,Silvaetal. (2006)comparedthreehighresolutionemissionmodelstoon– boardtestdataandconcludedthatR2valuesforCO,HCandNOX were“typicallylessthan0.40”,whereasfuelconsumptionwas slightlylessthan0.75.Thesethreemodelsweredevelopedinthe USAandEuropeandhaveamorecomplexstructure(andhence largerinputdatarequirements)thantheP'Pmodel.

Ajtayetal.(2005)usedbrakemeaneffectivepressureand enginespeedandshowedthatthisledtomoreaccuratemodelsas comparedwithasimplifiedspeed–accelerationmodel,although thisdependedonthevehicletechnology.Forinstance,foraEuro2 dieselcarboththecomplexandsimplifiedmodelsproduced excellent results for NOX with R2 values of 0.99 and 1.00, respectively.Incontrast,forEuro3petrolcars,theresultswere notgoodforbothmethodswithR2valuesof0.19and0.25, respectively.Thisresultissimilartotheobservedperformancefor theEuro3petrolcarsinthisstudy(R2valuesof0.17–0.56).

9.3.Scopeofapplication

TheP'Pmodelisdesignedforuseinresearchstudiesor projectswheredetailedinformationisavailableregardingvehicle drivingbehaviorandpotentiallyotherfactorssuchasroadgrade andvehicleloading,andwherea highspatial and temporal resolutioninemissionsandfuelconsumptionisrequired.Itis expectedthatthesoftwarecanbeusedinmostcases,butthere maybeafewinstanceswhereuseofthemodelisnotsuitable. OneconsiderationisthattheP'Psoftwaredoesnotexplicitly simulateengineloadandenginespeed.Asaconsequence,gear shiftbehaviorisimplicitlyincludedbecausepredictionsreflectthe gearshiftbehaviorduringtheemissionstests.However,thislevel ofdetailisnotusefulinthemajorityofapplicationsduetoalackof inputdata(e.g.informationonactualin–trafficgearshiftbehavior isscarcely–ifever–available).Neverthelesstherearesituations wheretheeffectsofgearshiftbehaviorareofparticularinterest,

Figure8.ExampleofP'Pmodelsimulationoutput(topleft:partofinputfile,topright:time–seriesofspeedandmodelpredictions,

bottom:graphicalsummaryoutput).

e.g.toexaminetheimpactsofspecificdrivetrainingprograms (e.g.ecoͲdriving)onemissionsandfuelconsumption.Inthese cases,theP'Psoftwareisoflimiteduseandwillonlypredictthe effectsof changes inspeeds,andnot changes ingear shift behavior.MoredetailedmodelssuchasPHEM(Zalingeretal., 2008)andCMEM(Barthetal.,2000)shouldbeemployedinthese cases.

9.4.Reliabilityandrobustness

ThevalidationshowedthattheP'Pemissionalgorithmsare robustwith respect to prediction errors (RMSE/NRMSE) and goodness–of–fit(R2)astheyshowsimilarresultstotheverification step,andsometimesevenexhibitimprovedperformance.Thereis, however,anincreaseinpredictionbias,whichcanbereducedby inclusionofthevalidationdatainasecondroundofmodelfitting toaddressthesystematicunder–prediction(about5%)forfuel consumption and CO2 emissions in arterial conditions, and aggregationofpredictionresults tofleet leveltocancelout systematicerrorsforNOX.Anotherwaytoreducepredictionerrors istoapplyaspatialaggregationtothesecond–by–secondemission predictions.Thismeansthatpredictionsaremadeforaspecific lengthofroad.Aminimumlengthof100mwillbeusedinthe modelsimulations.

9.5.Easeofuseandinterfacingwithtrafficdata

Theminimuminputrequirementsfortheemissionsimulation arespeed–timedata(1Hz),drivingcycleordrivingpatternname ofIDandaselectionforwhichvehicletypesthesimulationshould be run. This kind of information should be available from

microscopic traffic simulation models (e.g. AIMSUN, VISSIM, PARAMICS),expertjudgment(e.g.modificationofdrivingcycles)or on–roadmeasurementsusinge.g.GPS.Additionalinputonroad grade,vehicleloadingandair–conditioninguseareoptionalbut canbesettodefaultvalues(roadgrade=zero,vehicleloading=50% andair–conditioninguse=off)intheabsenceofinformation.

Detailedspatialandtemporalattributionofvehicleemissions isofincreasedimportancebecauseofanincreasingfocusonthe reductionofpopulationexposuretoairpollutionand(health)risk. Inaddition,detailedsimulationoftheimpactsofchangesindriving behavior on emissions through a variety of potential traffic managementmeasuresisrequiredtoaddressthedesiretoreduce greenhousegasemissionsandimprovefuelefficiency.Integration ofP'Pemissionalgorithmswithe.g.microscopictrafficsimulation modelswillgeneratetimeandspaceresolvedtrafficemissions information,whichcanbefedintoairqualitymodelsthatsimulate dispersion and chemical conversion processes to predict air pollutionconcentrationlevels,exposureandhealthrisksinurban areas.Thistypeofanalysiscanbeusedtoaccuratelyidentifyair pollution“hotspots”orevengreenhousegasemissionhotspots andassesstheimpactsofspecificmeasuresinurbanareas.This waytheP'Psoftwarewillsupportdecisionmakinginurban environmentsthrough scenario modeling, enhanced land use planning,policydevelopmentandimprovedtrafficmanagement.

9.6.RepresentativenessanduseinothercountriesthanAustralia

TheP'PemissionalgorithmsarerepresentativeforAustralian conditionsastheyreflecttheAustralianfleet,drivingconditions anddrivingbehavior,aswellasotherrelevantaspectssuchas

0 100 200 300 400 04 0 80 Freeway | SUV-L-petrol Elapsed Time (s) S pe ed [ k m /h ] 0 100 200 300 400 0. 0 1. 5 3. 0 Elapsed Time (s) Fu el R ate [ g /s ] PC S -P PC M -P PCL -P SUVC -P SUVL -P LC V -P PC-D SUV-D LC V -D BU SL -D BU S H -D MC V -D HC V -D AT -D Congested F u el C on sum p tio n [g /k m ] 0 200 40 0 600 800 100 0 PC S -P PC M -P PCL -P SUVC -P SUVL -P LC V -P PC-D SUV-D LC V -D BU SL -D BU S H -D MC V -D HC V -D AT -D Arterial F u el C on sum p tio n [g /k m ] 0 200 40 0 600 800 100 0 PC S -P PC M -P PCL -P SUVC -P SUVL -P LC V -P PC-D SUV-D LC V -D BU SL -D BU S H -D MC V -D HC V -D AT -D Freeway F u el C on sum p tio n [g /k m ] 0 200 40 0 600 800 100 0

basedonAustraliandata,itcaninprinciplebeusedtogenerate firstorderestimatesofemissionsandfuelconsumptioninother countries.However,itisrecommendedthatsomecalibrationis conductedto betterreflect local conditions. As a minimum, ADR/Euroemissionstandardequivalencytablesandinformation onthelocalfleetcompositionarerequiredtolinktheseventy threeP'Pvehicleclassestothecorrespondingvehicleclassesin thecountryofinterest.Ifmoreaccuratepredictionsarerequired, aggregatedlocalemissionsdata(e.g.totalcycleemissionsingrams perkm)canbeusedtocalibratethemodelusingthevariableM (Section7.1).AcountryspecificversionoftheP'Psoftwarecan evenbedevelopedifmoredetailedlocalmodalemissionsdataare available.

10.

Further

Work

Themodelwillbeextendedtoincludeotherairpollutant emissionssuchasparticulatematter(PM),hydrocarbons(THC)and carbonmonoxide(CO)andnewandfuturevehicletechnologies. ResearchwillbeconductedtofurtherimprovemodelperformͲ ance.Firstly,modalemissionsdatafrommoretestvehicleswillbe incorporatedintothemodelintime.Secondly,theimpactofother

'Pdefinitionsandotherstatisticalapproaches(e.g.time–series models)willbeexamined.Applicationofthesoftwareinpractical applicationswillbeusefultoseehowthemodelperformsandhow itcomparestoothermodels.

Acknowledgment

The Australian Government and the SA Department of Transport Energy and Infrastructure are acknowledged for commissioning vehicle emission test programs that have collectivelyprovidedthe(unverified)empiricalemissionsdataused inthisresearch.Theseincludethefollowingtestprograms:the FirstandSecondNationalIn–ServiceVehicleEmissionsstudies (NISE1 and NISE2), the Diesel Vehicle Emissions National EnvironmentProtectionMeasurePreparatoryWork(DNEPM)and theSouthAustralianTestandRepairProgram(SATR).

Appendix

Abbreviations

ADR:Australiandesignrule AT:Articulatedtrucks

BSFC:Brakespecificfuelconsumption GVM:Grossvehiclemass

HCV:Heavycommercialvehicle(GVM12.0–25.0tonne,rigidtruck) HDV:Heavy–dutyvehicle(rigidtruck,articulatedtruckorbus) LCV:Light–commercialvehicle(GVMч3.5tonne)

LDV:Light–dutyvehicle(passengercar,SUVorLCV)

MCV:Mediumcommercialvehicle(GVM3.5–12.0tonne,rigid truck)

P:Enginepower

'P:Changeinenginepower PC:Passengercar

SUV:Sportutilityvehicle VKT:Vehiclekilometerstravelled

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