IMPACT OF ELECTRIC FLEET ON AIR POLLUTANT EMISSIONS

S. Carrese, A. Gemma, S. La Spada

Roma Tre University – dep. Engineering

Venice, Sept. 19th 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 (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]

[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 speedLB: part of the link in queueLC: part of the link where vehicles accelerate

Dynamic analysis Wide network

For each link the model takes into account the queue:

LC LA LB

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]

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

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 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 ?

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

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)

Case study in Rome

Case study in Rome – main input data

Dynameq software [INRO] has been used to execute the DTA

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

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

CO reductionsc0 100%sc1 76%sc2 59%sc3 52%sc4 98%sc5 92%sc6 94%sc7 77%sc8 61%sc9 55%

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

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

THANK YOU FOR YOU ATTENTION

For any further information:simone.laspada@uniroma3.itstefano.carrese@uniroma3.it

agemma@dia.uniroma3.it

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