tate april feb2015 · 2017-04-11 · 19 frequency diagrams of the observed speed andaccelerations...
TRANSCRIPT
Outline
Vehicle emission Remote Sensing Method
Results Leeds Autumn/ Winter 2014
Zurich Summer 2014
Vehicle emission modelling PHEM (version 11.7.2, released 16/12/2014)
Outlook
2
MethodRemote Sensing Vehicle Emissions
3
Sheffield, May 2013
MethodRemote Sensing Vehicle Emissions
4
Leeds, December 2014
Camera(Number plate)
Vehicle Detector(Speed andAcceleration)
Source/Detector
Mirror Box
Source
Detector
Emissions Analyser(Common
Configurations)
Camera(Number plate)
Vehicle Detector(Speed andAcceleration)
Source/Detector
Mirror Box
Source
Detector
Emissions Analyser(Common
Configurations)
MethodRemote Sensing Vehicle Emissions
ESP RSD-4600 instrument (www.esp-global.com)
UVIR
Camera(Number plate)
Vehicle Detector(Speed andAcceleration)
Source/Detector
Mirror Box
Source
Detector
Emissions Analyser(Common
Configurations)
Camera(Number plate)
Vehicle Detector(Speed andAcceleration)
Source/Detector
Mirror Box
Source
Detector
Emissions Analyser(Common
Configurations)
Emission ratios
From peak exhaust plume conc.
NO / CO2
Estimate NO2 / CO2 & NOX / CO2
CO / CO2
HC / CO2 & PM10
Baseline condition taken from front ofthe vehicle
Valid measurement
5 – 60 km/h Sufficient CO2 plume Number plate record
►Site selection important
MethodRemote Sensing Vehicle Emissions
Vehicle Fleet Information
Number plate record ANPR
→ Detailed vehicle registration info.→ Annoymised DVLA type data
• vehicle type & euro category• fuel type and engine capacity• manufacturer & model• CO2 performance etc.
6
MethodRemote Sensing Vehicle Emissions
PROS: “Real-world” or “on road” or “RDE”
Understand the emissionperformance of the vehicle fleet
Trends
Degradation/ ageing technologies
High-emitters
Detailed knowledge of the vehiclefleet composition
Fast turn-round (one month)
Survey emerging technologies
e.g. EuroVI compliant vehicles
Rich research resource
CONS: “Snap-shot” or “single-point”
measurement
Vehicle operating condition unknown
Loading
Cold-start or Hot-running
Elevated exhausts – NULL
Lorries “open” chassis
Practical limitations
Dry weather conditions
Relatively labour intensive
Suitable survey locations limited
7
ResultsLeeds Autumn/ Winter 2014
Die
sel_
E0
Die
sel_
E1
Die
sel_
E2
Die
sel_
E3
Die
sel_
E4
Die
sel_
E5
Die
sel_
E6
Petr
ol_
E0
Petr
ol_
E1
Petr
ol_
E2
Petr
ol_
E3
Petr
ol_
E4
Petr
ol_
E5
Petr
ol_
E6
Petr
olH
ybrid_E
3
Petr
olH
ybrid_E
4
Petr
olH
ybrid_E
5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
NO
X(g
ram
s.k
m1
)
[2]
[2]
[24]
[299]
[970]
[1108]
[39]
[14]
[26]
[156]
[868]
[1131]
[694]
[32]
[2]
[0]
[24]
8
ResultsLeeds Autumn/ Winter 2014
Die
sel_
E0
Die
sel_
E1
Die
sel_
E2
Die
sel_
E3
Die
sel_
E4
Die
sel_
E5
Die
sel_
E6
Petr
ol_
E0
Petr
ol_
E1
Petr
ol_
E2
Petr
ol_
E3
Petr
ol_
E4
Petr
ol_
E5
Petr
ol_
E6
Petr
olH
ybrid_E
3
Petr
olH
ybrid_E
4
Petr
olH
ybrid_E
5
0
20
40
60
80
PM
Index
[2]
[2]
[24]
[299]
[970]
[1108]
[39]
[14]
[26]
[156]
[868]
[1131]
[694]
[32]
[2]
[0]
[24]
MethodRemote Sensing Vehicle Emissions
10
Zurich, July 2014And 2000, 2001, 2002…… 2013
MethodZurich Remote Sensing Site
11
Zurich, July 201482m elevation gain1.2 kmAv. gradient 6.8 %
ResultsZurich, Summer 2014
12
Die
sel_
E0
Die
sel_
E1
Die
sel_
E2
Die
sel_
E3
Die
sel_
E4
Die
sel_
E5a
Die
sel_
E5b
Die
sel_
E6a
Die
sel_
E6b
Die
selH
ybrid_E
5
Petr
ol_
E0
Petr
ol_
E1
Petr
ol_
E2
Petr
ol_
E3
Petr
ol_
E4
Petr
ol_
E5a
Petr
ol_
E5b
Petr
ol_
E6b
Petr
olH
ybrid_E
4
Petr
olH
ybrid_E
5a
Petr
olH
ybrid_E
5b
Petr
olH
ybrid_E
6b
0
1000
2000
3000
4000
NO
X/
CO
2ra
tio
[13]
[33]
[164]
[1220]
[4250]
[3315]
[3165]
[27]
[228]
[19]
[113]
[629]
[1805]
[2584]
[9141]
[3730]
[4360]
[520]
[233]
[85]
[307]
[3]
ResultsZurich, Summer 2014
13
Die
sel_
E0
Die
sel_
E1
Die
sel_
E2
Die
sel_
E3
Die
sel_
E4
Die
sel_
E5a
Die
sel_
E5b
Die
sel_
E6a
Die
sel_
E6b
Die
selH
ybrid_E
5
Petr
ol_
E0
Petr
ol_
E1
Petr
ol_
E2
Petr
ol_
E3
Petr
ol_
E4
Petr
ol_
E5a
Petr
ol_
E5b
Petr
ol_
E6b
Petr
olH
ybrid_E
4
Petr
olH
ybrid_E
5a
Petr
olH
ybrid_E
5b
Petr
olH
ybrid_E
6b
0.0
0.1
0.2
0.3
0.4
PM
Index
[13]
[33]
[164]
[1220]
[4250]
[3315]
[3165]
[27]
[228]
[19]
[113]
[629]
[1805]
[2584]
[9141]
[3730]
[4360]
[520]
[233]
[85]
[307]
[3]
PHEMVersion 11.7.2, released 16/12/2014
14 Technical University of Graz (AT)
HBEFA update version3.2 (Dec ‘13) CADC drive cycle
ERMES drive cycle
Modal data 5 EuroVIdiesel cars
Bag data 19 EuroVIdiesel cars (TUG,TNO) Premium vehicles
Large, higher poweredvehicles
Real Driving Activity Monitoring (PAMS)Sheffield 2013 : VBox GPS + CAN (1Hz) + Road gradient (%)
15
Vehicle tracking routes across Sheffield (160km){©Copyright GoogleTM 2013}
Wyatt, D., Li, H., Tate, J. 2014. The impact of road grade on carbon dioxide (CO2) emission of a passenger vehicle in real-world driving.Transportation Research Part D: Transport and Environment, 32, pp 160-170, October 2014, DOI: 10.1016/j.trd.2014.07.015
PHEM NOX Emission FactorsSheffield 2013 activity data (with grade)
16
0
0.2
0.4
0.6
0.8
Euro 0 Euro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Euro 6c
NO
X(g
ram
s.km
-1)
Euro standard
Diesel
Petrol
OutlookDiesel Euro VI passenger cars
Initial assessment – good improvement over EuroV RDE far in excess of emission standard limits
Worst performance expected in congested, urban drivingconditions Demands continuous review Not simply an evaluation of the first generation of EuroVI vehicles
from premium manufacturers
Deterioration of technology unknown SCR dosage a concern
Significance of engine re-mapping and emission controlremoval (e.g. DPF removal) unknown Considered minor new cars (warranty and leasing schemes)
17
PAMS & Remote Sensing comparisonSpeed & Acceleration
19
Frequency diagrams of the observed Speed and Accelerationsdistributions from theVehicle tracking surveys and those recorded by
the Remote Sensing Device (Sheffield, 2013)