impact of electric fleet on air pollutant emissions s. carrese, a. gemma, s. la spada roma tre...
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IMPACT OF ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS
S. Carrese, A. Gemma, S. La Spada
Roma Tre University – dep. Engineering
Venice, Sept. 19th 2013
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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
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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)?
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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?
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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 (vist, 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]
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[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
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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:
LC LA LB
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Mesoscopic emission model [Gori et al]
cCnsbBnsaCnvBnvAkTkT eLQeLQeLQLQLqE *)*(*)*(*)***( ,,
In case of unsaturated conditions:
Qnv = qnvT/C = qT,k g/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
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
ea: emission factors for LA
eb: emission factors for LB
ec: emission factors for LC
[Gori et al –IEEE- ITSC2013]
[Cantarella]
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The specific emission factor – Gori et Al.
Light vehicles [g/(km*vehicle)]
Heavy vehicles [g/(km*vehicle)]
Link type
CO NOx CO NOx
50km/h 8.03 0.79 19.1 42.1
60km/h 6.38 0.64 16.2 35.5
110km/h 10.3 0.56 14.6 30.2
Mesoscopic Specicif emission factors considering different acceleration phasesand vehicles classes
ec: estimation
Starting from microscopic approach (MOVES)
VSP estimation
Instantaneous emission
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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
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Electric vehiclesElectric 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
0.20 KWh/km
0.153 KWh/km
EM
ISS
ION
S ?
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Proposed model• Objective: assess the electric & hybrid impacts on air
pollutant emissions.
DTA (Dynameq) Emission
model(Gori et Al)Mesoscopic
Specific emission factors
MesoscopicSpecific emission
factorsfor hybrid vehicles
MesoscopicSpecific emission
factorsfor electric vehicles
Proposed Emission
model
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Proposed model – specific emission factorsMesoscopic
Specific emission factors
for hybrid vehicles
MesoscopicSpecific emission
factorsfor 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
fuel CO [g/GJ]
NOx[g/GJ]
Hard coal 150 310
Natural gas 39 89
eb, ec = 0
The emissions are estimate considering the average travel distance on the network and the specific energy consumption (KWh/km)
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Case study in Rome
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Case study in Rome – main input data
Dynameq software [INRO] has been used to execute the DTA
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Scenario definitionsHp1: increase of electric mobility
Scenario 1 Sc1 + 5%
Scenario 2 Sc2 +10%
Scenario 3 Sc3 +15%
Hp2: increase of public transport
Scenario 3 Sc4 + 2%
Scenario 4 Sc5 + 4%
Scenario 6 Sc6 + 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 7 Sc7 + 5%
Scenario 8 Sc8 +10%
Scenario 9 Sc9 +15%
It needs to estimate three different functions for the specific emission factors
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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)
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Global Results
Electric solution
Modal shift
Hybrid solution
CO reductionsc0 100%sc1 76%sc2 59%sc3 52%sc4 98%sc5 92%sc6 94%sc7 77%sc8 61%sc9 55%
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Results - with electric vehicles emissionsAccording to the avg travel distance (davg), it has been estimated the extra CO emission as follow:
𝐸𝑡 ,𝑘=𝑞𝑡 ,𝑘∗𝑑𝑎𝑣𝑔∗𝑒𝑐𝑘∗𝑒𝑠𝑝𝑒𝑐
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Global results:
Electric solution
Modal shift
Hybrid solution
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Results – maps of the emissions
CO emissions on the intersections(vehicles in queue)
Total CO emissions on the network
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Results for different state of traffic conditions
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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
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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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THANK YOU FOR YOU ATTENTION
For any further information:
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