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 Proposed model for the impacts assessment Case study in Rome Results Conclusion & further developments

Research objectives: the impacts of electric & hybrid mobility on road pollutant emissions the impacts on the traditional traffic managment solutions  What do we need to reach our objectives? 1)How do the hybrid and electric engine work? 2)Which are the parameters that can take into account the difference between the endothermic engine and hybrid/electric one (from emissions point of view)?

1. 1. We are looking for an emission model with these features: Urban network  congestion Large scale  city (not single arterial) With low calibration & computational cost/time Can take into account different time slices (time variability) Can take into account queue phenomena Can take into account acceleration phase A way to compute the emission of electric vehicles 2. traffic managment impacts from emissions point of view Traffic managment such as arterial signal optimization, ramp metering, one way optimization, reversable lanes, ITS solution… Regarding the incoming new fleet composition, is there any change in traffic flow? Do we need to change our traffic managment solutions according to the new fleet composition ?  What do we need to reach our objectives?

State of art model for road emission estimation Traffic model (congestion) Emission model Dispersion model (CALPUFF etc) 1)CORINAIR model based on MACROSCOPIC parameters (v, k, q) Is the reference model for estimating emissions in Europe [Lumbreras-Valdes- Borge-Rodriguez; European Environment Agency] In congested network macroscopic model underestimates emissions [Shukla- Alam; Rakha-Ding; Rouphail-Frey-Colyar-Unal] 2) MESOSCOPIC model based on MACRO/MICROSCOPIC param. (v, n°stop, delay) 3) MOVES model based on MICROSCOPIC parameters (v ist, a, d, delay) mainly useful for emission estimation in artrials or single intersection [Stevanovic-Zhang-Batterman] Good efficacy and efficiency in arterial or single intersection optimization [Midnet-Boillot-Pierrelee; Coelho-Farias-Rouphail; Rakha et al]

[Gori, La Spada, Mannini, Nigro] proposed a mesoscopic emission model based on Dynamic Traffic Assignment (DTA) and mesoscopic specific emission factors. State of art – mesoscopic emission model

mesoscopic emission model [Gori et al] LA: part of the link in free flow speed LB: part of the link in queue LC: part of the link where vehicles accelerate  Dynamic analysis  Wide network For each link the model takes into account the queue: LCLA LB

Mesoscopic emission model [Gori et al] In case of unsaturated conditions: Q nv = q nv T/C = q T,k g/3600 Q ns = q ns T/C and q ns = q n (G s -t r -((1-exp(-m q (G s -t r )))/m q ) L B = (D T,k /C)· L = q T,k [((C(1-g/C))/2)+(x T,k -1)T/2]L In case of saturated conditions: Q nv = q nv = 0 Q ns =q ns T/C and q ns =q n (the maximum flow rate discharge) L B =(D T,k /C)L=[(q T,k C (1-g/C) 2 )/(2(1- q T,k /s))]L e a : emission factors for LA e b : emission factors for LB e c : emission factors for LC [Gori et al – IEEE- ITSC2013] [Cantarella]

The specific emission factor – Gori et Al. Mesoscopic Specicif emission factors considering different acceleration phases and vehicles classes e c: estimation Starting from microscopic approach (MOVES) VSP estimation Instantaneous emission

Hybrid & Electric vehicle traditional Electric Hybrid Hybrid engine can work in parralel with the traditional one (tandem) Hybrid engine can work during the acceleration phase (up to 50 km/h). The engines are alternative electric traditional

Electric vehicles 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) KWh/km 0.20 KWh/km KWh/km EMISSIONS ?

Proposed model Objective: assess the electric & hybrid impacts on air pollutant emissions. DTA (Dynameq) Emission model (Gori et Al) Mesoscopic Specific emission factors Mesoscopic Specific emission factors for hybrid vehicles Mesoscopic Specific emission factors for electric vehicles Proposed Emission model

Proposed model – specific emission factors Mesoscopic Specific emission factors for hybrid vehicles Mesoscopic Specific emission factors for electric vehicles There isn’t any emission during the acceleration (up to 50km/h) and queue phases For the other phases the emissions are computed as before Any emission on road Power plant emissions fuelCO [g/GJ] NOx [g/GJ] Hard coal Natural gas3989 e b, e c = 0 The emissions are estimate considering the average travel distance on the network and the specific energy consumption (KWh/km)

Case study in Rome

Case study in Rome – main input data Dynameq software [INRO] has been used to execute the DTA

Scenario definitions Hp1: increase of electric mobility Scenario 1Sc1+ 5% Scenario 2Sc2+10% Scenario 3Sc3+15% Hp2: increase of public transport Scenario 3Sc4+ 2% Scenario 4Sc5+ 4% Scenario 6Sc6+ 6%  It needs to run 3 new DTA  It needs to estimate three different functions for the specific emission factors Hp3: increase of public hybrid mobility Scenario 7Sc7+ 5% Scenario 8Sc8+10% Scenario 9Sc9+15%  It needs to estimate three different functions for the specific emission factors

 Hp1 (increase of electric vehicles) seems the more efficient  in Hp1 the emissions related tot the energy production are not yet computed  Hp2 can reduce emissions when the network is congested, otherwise emissions can increase.  Hp3 can reduce emissions (but less then the electric solution)

Global Results Electric solution Modal shift Hybrid solution

Results - with electric vehicles emissions According to the avg travel distance (d avg ), it has been estimated the extra CO emission as follow:

Global results: Electric solution Modal shift Hybrid solution

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

Results for different state of traffic conditions

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 & queue phases

further developments Test the model for different pollutants (CO2, PM…) Increase the accuracy and the knowledge about hybrid and electric engines (The market is quickly changing in technologies and dimensions) Trafic management solutions assessment ? Conclusions As expected the electric vehicles seem more efficient and provide less pollution (CO) Hybrid vehicles and modal shift (to public Transport) can reduce emissions in less efficient way The proposed model is able to catch the differences bewteen the different engines (Traditional, Hybrid, electric), Taking into account the queue

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SPECIFIC EMISSION FACTOR - Akcelic