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impact of ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS

impact of ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS. S. Carrese, A. Gemma, S. La Spada Roma Tre University – dep . Engineering. Venice , Sept . 19 th 2013 . Content:. Research Objectvies Models for emission estimation Hybrid and electric vehicles

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impact of ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS

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  1. impact of ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS S. Carrese, A. Gemma, S. La Spada Roma Tre University – dep. Engineering Venice, Sept. 19th 2013

  2. Content: • ResearchObjectvies • Modelsfor emissionestimation • Hybrid and electricvehicles • Proposed model for the impactsassessment • Case study in Rome • Results • Conclusion & furtherdevelopments

  3. Researchobjectives: • the impacts of electric & hybridmobility on road pollutantemissions • the impacts on the traditionaltrafficmanagmentsolutions • What do weneed to reachourobjectives? How do the hybrid and electricengine work? Which are the parametersthat can take into account the differencebetween the endothermicengine and hybrid/electricone (from emissionspoint of view)?

  4. Whatdo weneed to reachourobjectives? • 1. We are looking for an emission model with thesefeatures: • Urban network  congestion • Large scale city (not single arterial) • With lowcalibration & computationalcost/time • Can take into account different time slices (time variability) • Can take into account queuephenomena • Can take into account accelerationphase • A way to compute the emission of electricvehicles • trafficmanagmentimpacts from emissionspoint of view • Trafficmanagmentsuchasarterialsignaloptimization,rampmetering, one way optimization, reversablelanes, ITS solution… • Regarding the incoming new fleetcomposition, isthereanychange in traffic flow? • Do weneed to changeourtrafficmanagmentsolutionsaccording to the new fleetcomposition ?

  5. State of art model for road emissionestimation Traffic model (congestion) Emission model Dispersion model (CALPUFF etc) • CORINAIR model based on MACROSCOPICparameters (v, k, q) • Is the reference model for estimatingemissions in Europe [Lumbreras-Valdes-Borge-Rodriguez; European Environment Agency] • In congested network macroscopic model underestimatesemissions[Shukla-Alam; Rakha-Ding; Rouphail-Frey-Colyar-Unal] • 2) MESOSCOPIC model based on MACRO/MICROSCOPICparam. (v, n°stop, delay) • 3) MOVES model based on MICROSCOPICparameters (vist, a, d, delay) • mainlyuseful for emissionestimation in artrials or single intersection [Stevanovic-Zhang-Batterman] • Goodefficacy and efficiency in arterial or single intersectionoptimization[Midnet-Boillot-Pierrelee; Coelho-Farias-Rouphail; Rakha et al]

  6. State of art – mesoscopicemission model • [Gori, La Spada, Mannini, Nigro] proposed a mesoscopicemission model based on DynamicTrafficAssignment(DTA) and mesoscopicspecificemissionfactors.

  7. mesoscopicemission model [Gori et al] • Dynamicanalysis • Wide network For each link the model takesinto account the queue: LA: part of the link in free flow speed LB: part of the link in queue LC: part of the link where vehicles accelerate LC LA LB

  8. Mesoscopicemission model [Gori et al] In case of unsaturated conditions: Qnv= qnvT/C = qT,kg/3600 Qns = qnsT/C and qns= qn(Gs-tr-((1-exp(-mq(Gs-tr)))/mq) LB = (DT,k/C)· L = qT,k[((C(1-g/C))/2)+(xT,k -1)T/2]L ea: emission factors for LA eb: emission factors for LB ec: emission factors for LC [Cantarella] In case of saturated conditions: Qnv= qnv = 0 Qns=qnsT/C and qns=qn(the maximum flow rate discharge) LB=(DT,k/C)L=[(qT,k C (1-g/C)2)/(2(1- qT,k/s))]L [Gori et al – IEEE- ITSC2013]

  9. The specificemissionfactor – Gori et Al. ec: estimation Starting from microscopic approach (MOVES) VSP estimation Instantaneous emission MesoscopicSpecicifemissionfactorsconsideringdifferentaccelerationphases and vehiclesclasses

  10. Hybrid & Electricvehicle Hybrid electric traditional traditional Electric • Hybridengine can work in parralel with the traditionalone (tandem) • Hybridengine can work during the accelerationphase (up to 50 km/h). The engines are alternative

  11. Electricvehicles Electric Smart : • Battery: 17 kWh • Travel distance : 135 km (max) Opel Ampera: • Battery: 16 kWh • Travel distance : 80 km (max) Fiat 500e: • Battery: 20 kWh • Travel distance : 130 km (max) 0.129 KWh/km • EMISSIONS ? 0.20 KWh/km 0.153 KWh/km

  12. Proposed model • Objective: assess the electric & hybridimpacts on air pollutantemissions. Mesoscopic Specificemissionfactors for hybridvehicles DTA (Dynameq) Emissionmodel (Gori et Al) Mesoscopic Specificemissionfactors for electricvehicles Mesoscopic Specificemissionfactors ProposedEmission model

  13. Proposedmodel – specificemissionfactors • Thereisn’tanyemissionduring the acceleration (up to 50km/h) and queuephases • For the otherphases the emissions are computedasbefore Mesoscopic Specificemissionfactors for hybridvehicles • eb, ec= 0 • Anyemission on road • Powerplantemissions Mesoscopic Specificemissionfactors for electricvehicles The emissions are estimate considering the averagetraveldistance on the network and the specificenergyconsumption(KWh/km)

  14. Case study in Rome

  15. Case study in Rome – main input data Dynameq software [INRO] hasbeenused to execute the DTA

  16. Scenario definitions  Itneeds to estimate threedifferentfunctions for the specificemissionfactors  Itneeds to run3 new DTA  Itneeds to estimate threedifferentfunctions for the specificemissionfactors

  17.  Hp1 (increase of electricvehicles) seems the more efficient  in Hp1 the emissionsrelated tot the energy production are notyetcomputed  Hp2 can reduce emissionswhen the network iscongested, otherwiseemissions can increase.  Hp3 can reduce emissions (butlessthen the electricsolution)

  18. Global Results Electricsolution Modalshift Hybridsolution

  19. Results - with electricvehiclesemissions According to the avgtraveldistance (davg), ithasbeenestimated the extra CO emissionasfollow:

  20. Global results: Modalshift Electricsolution Hybridsolution

  21. Results – maps of the emissions CO emissions on the intersections (vehicles in queue) Total CO emissions on the network

  22. Results for different state of trafficconditions

  23. Results – comparison with CORINAIR • The proposed model provides an overestimation of the CO emission (compare to CORINAIR) •  The model can take into account the extra CO related to the acceleration & queuephases

  24. Conclusions furtherdevelopments • Asexpected the electricvehiclesseem more efficient and providelesspollution (CO) • Hybridvehicles and modalshift (to public Transport) can reduce emissions in lessefficient way • The proposed model isable to catch the differencesbewteen the differentengines (Traditional, Hybrid, electric), • Takinginto account the queue • Test the model for differentpollutants (CO2, PM…) • Increase the accuracy and the knowledgeabouthybrid and electricengines (The market isquicklychanging in technologies and dimensions) • Trafic management solutionsassessment?

  25. THANK YOU FOR YOU ATTENTION For anyfurther information: simone.laspada@uniroma3.it stefano.carrese@uniroma3.it agemma@dia.uniroma3.it

  26. SPECIFIC EMISSION FACTOR - Akcelic

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