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Next Generation(Smaller, Lower Cost, Lower Energy Consumption)
Portable Emissions Measurement Systems
Karl Ropkins1, Hu Li1
and Andrew Burnette2
6th International PEMS Conference and Workshop
UCR CE-CERT
17th & 18th March 2016
1Transport Studies, EngineeringUniversity of Leeds, Leeds, LS2 9JT, UK
k.ropkins@its.leeds.ac.uk
2 infoWedge,El Dorado Hills, CA 95762
andrew.burnette@infowedge.com
Overview
1 Research Rationale
2 Building a Smaller (Integrated) PEMS
3 Gas-phase Species Measurement
4 Particulate Measurement
Research Rationale
1 Research Rationale
2 Building a Smaller (Integrated) PEMS
3 Gas-phase Species Measurement
4 Particulate Measurement
Research Rationale
Do We Need a Smaller PEMS?
The size, weight and power consumption ofthe current generation of commercial PEMS isa well-documented concern
Even an entry-level PEMS could significantlyextend our monitoring, inventorying andresearch capabilities
Low-cost real-world optimization and/orvalidation for new technologiesAn option for the more challenging (andmore mundane?) applications
And, such an entry-level PEMS is closer thanyou might think - we are already delivering‘prototype’ and ‘early-to-market’ systems
But we should be aiming for more...Examples:
Global MVR Firefly3DATX parSYNC
WVU/Carder
Research Rationale
The Bigger Picture
Vehicle Emissions Sampling Strategies
Analysers nail
Sampling Point
Infrastructure nail
Probe Vehicle Trailer
Chaser Vehicle
Across-Road
Roadside, Tunnel, etc.
(From Ropkins, K. Real-time Mobile Monitoring of Vehicle Emissions. Royal Society of Chemistry AAMG AirAnalysis Out of the Laboratory and into the Field, National Physical Laboratory, UK, 2008.)
Building a Smaller (Integrated) PEMS
1 Research Rationale
2 Building a Smaller (Integrated) PEMS
3 Gas-phase Species Measurement
4 Particulate Measurement
Building a Smaller (Integrated) PEMS
Modular Design
ExhaustSampling
Inlet
Local CPUand Logger
PowerSupply
ActivitySensor(s)
ParticulateSensorModule
Pump
GasesSensorModule
Pump
FlowSensor
Physical Connection
Power Connection
Communication Link
Building a Smaller (Integrated) PEMS
The Exhaust Inlet
ExhaustSampling
Inlet
Local CPUand Logger
PowerSupply
ParticulateSensorModule
Pump
GasesSensorModule
Pump
FlowSensor
We are not talking about the exhaust inlet today, but it isimportant to emphasize that good sample delivery is fundamental -It simply does not matter how good your analyzer/sensor/etc is, if
you are not delivering a representative sample to it...
Gas-phase Species Measurement
1 Research Rationale
2 Building a Smaller (Integrated) PEMS
3 Gas-phase Species Measurement
4 Particulate Measurement
Gas-phase Species Measurement
Dynamometer and On-Road Data
Cycle Time
NO
Em
issi
ons
Mustang Dynamometer Test Cycle NO Emissions and Vehicle Speed
Veh
icle
Spe
ed
0.000
0.005
0.010
0.015
0.020
0 100 200 300 400 500
0
25
50
75
100
From recent work withMustang Dynamometer
Validation on-going
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−81.46 −81.44 −81.42 −81.40 −81.38 −81.36
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Emissions NO
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0.0015
0.0020
Gas-phase Species Measurement
Gas Sensor Evaluation
GasMOD v0.2b CO2
ME
XA
Ref
eren
ce C
O2
10
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EX
A R
efer
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CO
2 an
d G
asM
OD
v0.
2b C
O2
10
12
14
0 100 200 300 400
MEXA Reference CO2GasMOD v0.2b CO2
Agreement MEXA NDIR / parSYNC PLUS® Module R ≈ 0.98(Clean IR diode sensor; prior calibration)
So, the ‘take home’ message here is that you can get good dataout of sensors, but there are caveats...
Gas-phase Species Measurement
Concentrations, Flows and Emissions
Concentrations R ≈ 0.98
GasMOD v0.2b CO2
ME
XA
Ref
eren
ce C
O2
10
12
14
10 11 12 13 14
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XA
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eren
ce C
O2
and
Gas
MO
D v
0.2b
CO
2
10
12
14
0 100 200 300 400
MEXA Reference CO2GasMOD v0.2b CO2
Exhaust Flow R ≈ 0.95
GasMOD v0.2b Exhaust Flow
CV
S R
efer
ence
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aust
Flo
w
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S R
efer
ence
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aust
Flo
w a
nd G
asM
OD
v0.
2b E
xhau
st F
low
0
1000
2000
3000
0 100 200 300 400
CVS Reference Exhaust FlowGasMOD v0.2b Exhaust Flow
Emissions R ≈ 0.94
GasMOD v0.2b CO2 [g/s]
RE
F C
O2
Em
issi
ons
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RE
F C
O2
Em
issi
ons
and
Gas
MO
D v
0.2b
CO
2 [g
/s]
0
2
4
6
8
10
0 100 200 300 400
REF CO2 EmissionsGasMOD v0.2b CO2 [g/s]
Gas-phase Species Measurement
Some early ‘lessons learnt’
Observations coming out of work on the parSYNC GasMODTM
module
Optics are bad news - high power, weightand heat overheads - but they are still thecurrent best option
There are size and power trade-offs buildingan optics sensor bundle and the ‘first gen’cartridge measures
CO2 - GHG, fuel consumptionNO, NO2 - air quality priority, not NOx (Aregulatory CO2/CO/THC/NOx cartridgewould be demand driven)
The issue is not getting good sensors - thatjust costs money ; it is keeping those sensorsalive as long as possible, drift correcting andspotting when they die...
(maybe not always going to be so; patents possible)
Gas-phase Species Measurement
A Comment about Defeat Devices
Cycle Time
NO
Em
issi
ons
On−Road Journey (1) NO Emissions and Vehicle Speed
Veh
icle
Spe
ed
0.0000
0.0005
0.0010
0.0015
0.0020
600 800 1000 1200
0
20
40
60
80
Cycle Time
NO
Em
issi
ons
On−Road Journey (4) NO Emissions and Vehicle Speed
Veh
icle
Spe
ed
0.000
0.005
0.010
0.015
0.020
600 800 1000 1200
0
20
40
60
80
It does not need a very accurate PEMS to see a defeat device - butit does need a PEMS in the car with a defeat device...
Particulate Measurement
1 Research Rationale
2 Building a Smaller (Integrated) PEMS
3 Gas-phase Species Measurement
4 Particulate Measurement
Particulate Measurement
parSYNC® Sensor Module
ExhaustSampling
Inlet
Local CPUand Logger
PowerSupply
ParticulateSensorBlock
Pump
OpacitySensor
IonizationSensor
ScatteringSensor
Time [Seconds]
Res
cale
d/O
ffset
ioni
zatio
n, s
catte
ring
and
opac
ity
0.0
0.5
1.0
1.5
0 50 100 150
opacityscatteringionization
Particulate Measurement
Multiplex PM/PN Sensor Model
Three Sensor Fit
parSYNC* = [MPM4] = f(parSYNC1t=−1,0,1) + f(parSYNC2t=−1,0,1) + f(parSYNC3t=−1,0,1)
parSYNC* [ mg m−3]
MP
M4
[ mg
m−3
]
0
5
10
15
20
25
30
0 5 10 15 20
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time [sec]
MP
M4,
par
SY
NC
* [ m
g m
−3]
0
5
10
15
20
25
30
0 100 200 300
parSYNC*MPM4
Agreement MPM4 / parSYNC® multiplex sensor R ≈ 0.92(two sensor, scattering and opacity, parSYNC* model)
[Ropkins et al, 2015]
Particulate Measurement
PM Sensor Module Validation
UCR CE-CERTDynamometer
FTP Cycle
Replicate runs
DUSTRAKreference
parSYNC®threesensor multiplex
(R ≈ 0.98)
Bag 3 parSYNC*
UC
R2
Dus
trak
0
10
20
30
40
50
60
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UC
R2
Dus
trak
and
Bag
3 p
arS
YN
C*
0
10
20
30
40
50
60
0 200 400 600 800 1000 1200
DustrakparSYNC*
Bag 5 parSYNC*
UC
R3
Dus
trak
0
10
20
30
40
50
0 10 20 30 40
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Cycle Time
UC
R3
Dus
trak
and
Bag
5 p
arS
YN
C*
0
10
20
30
40
50
0 200 400 600 800 1000 1200
DustrakparSYNC*
Particulate Measurement
Example Application
Test vehicle with modified DPF; modes 1 (working) to 3 (failed)
time [seconds]
tota
l sen
sor
volta
ge [v
]
0.0
0.5
1.0
0 100 200 300 400 500
1 2 3
sensors
opacity_x20
scattering_x20
ionization
MODE
seco
nd−
by−
seco
nd G
AM
pre
dict
ion
−1
0
1
2
3
−1 1 2 3
0
0.2
0.4
0.6
0.8
1
opac
ity
0
0.2
0.4
0.6
0.8
1
scattering
00.20.40.60.81
ionization
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ity
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acity
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2
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opac
ity
0
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scattering
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ionization
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00.20.4
0.6
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1
Thank You
Thank You
Karl Ropkins - k.ropkins@its.leeds.ac.ukHu Li - fuehli@leeds.ac.uk
Andrew Burnette - andrew.burnette@infowedge.com
Selected References
PEMS Instrument and Software Development/Evaluation
Ropkins, Sandhu, Burnette, A Novel Multiplexed Sensor-Based Approach to Mobile Particle Massand Number Measurement. 25th CRC Real World Emissions Workshop, 2015.Ropkins, Tate, Li, Andrews, Hawley, Bell, Chassis Dynamometer Evaluation of On-board ExhaustEmission Measurement System Performance in SI Car under Transient Operating Conditions. SAETechnical Paper 2008-01-1826, 2008.
Flow Measurement/Correction
Ropkins, Li, Hawley, Chen, Tate, Andrews, Bell, Robust surrogate measurement correction usinggeneralised additive model. Chemometrics and Intelligent Laboratory Systems, 95(2), 2009.
Data Time Alignment
Ropkins, Carslaw, Goodman, Tate, Application of non-linear time-alignment and integrationmethods to environmental time series. TrAC Trends in Analytical Chemistry, 28, 2009.Carslaw, Ropkins, Laxen, Marner, Williams, Near-Field Commercial Aircraft Contribution toNitrogen Oxides by Engine, Aircraft Type and Airline by Multiple Plume Sampling. EnvironmentalScience and Technology, 42(6), 2008.
Mobile Data Software/Analytics
Ropkins, Abdalla, Hanley. New methods for PEMS micro-trip analysis. 24th CRC Real-WorldEmissions Workshop, 2014.Ropkins, Abdalla, Hanley, Automating PEMS micro-trip analysis. 2014 PEMS Conference andWorkshop No.4, 2014.
Additional Information
PEMS Applications
PEMS are already used in multiple applications
...and we would use PEMS even more widely if we could
Additional Information
parSYNC®and parSYNC PLUS®
The 3DATX parSYNC® systems
parSYNC® PM/PN analyzer - at least ‘entrylevel’ technology currently beingcommercialized
parSYNC PLUS® gaseous and PM/PNanalyzer - currently undergoing ruggedizationand validation
Both small footprint
ca. 3 kg including batteriesNominally 280 × 215 × 140 mm
Both have long battery life
Both are easy to deploy, install and run
(Patents Pending)
Additional Information
Why 3kg, 280 x 215 x 140mm?
Obviously the long-term aim is even smaller/lighter but why this asthe first target?
It is a user-friend size
Unit is small/light enough to be truly portableSingle person installation
It is a good size for a third-party system
Small enough to courierBut not so small that it is easily lost, run-over, etc
And there is still room inside
Scope to add new modules or reconfigure existing ones(So early adopters can benefit from ‘lessons learnt’)
Additional Information
Early Dynamometer Data
Cycle Time
NO
Em
issi
ons
Mustang Dynamometer Test Cycle NO Emissions and Vehicle Speed
Veh
icle
Spe
ed
0.000
0.005
0.010
0.015
0.020
0 100 200 300 400 500
0
25
50
75
100
From recent work withMustang Dynamometer
Validation on-going
Reference NO [ppm]
Exh
aust
NO
[ppm
]
0
200
400
600
800
1000
0 200 400 600 800 1000
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Additional Information
Early On-Road Data
Cycle Time
NO
Em
issi
ons
On−Road Journey (1) NO Emissions and Vehicle Speed
Veh
icle
Spe
ed
0.0000
0.0005
0.0010
0.0015
0.0020
0 500 1000
0
20
40
60
80
velocity [mph]
acce
l [m
/s/s
]
−2
−1
0
1
2
3
0 20 40 60
20
40
60
80
100
120
140
41.26
41.27
41.28
41.29
41.30
41.31
−81.46 −81.44 −81.42 −81.40 −81.38 −81.36
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Emissions NO
●
●
●●
0.00000.00050.0010
0.0015
0.0020
Additional Information
Demonstrator Study
Third-party deployment
Working with University ofYORK/SEI
African/Kenyan PEMS fieldstudy
Early emissions data onpoorly understood localvehicle fleet
Proof of concept
Third-party study modelCourier ‘PEMS by Post’
Longitude
Latit
ude
−1.30
−1.25
−1.20
−1.15
36.75 36.80 36.85 36.90
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PM Emissions(Normalized)
●
●
●●
00.20.4
0.6
0.8
1
Journey Time [seconds]
PM
[nor
mal
ized
]
Vehicle 1; Urban/Interurban Route Tailpipe PM and Altitude
Alti
tude
0.0
0.2
0.4
0.6
0.8
1.0
0 1000 2000 3000 4000 5000
1500
1550
1600
1650
1700
1750
Additional Information
Next Demonstrator
From: Work by Karim/CARB
Motorcycle deployment
To complement African deployment
(a challenging environment)
A challenging installation
Small instrument footprintHigh in-use stress/strain
Selection rationale
Currently no real-world data onmotorcycle fleetsParticular issue in e.g. Asia wherelocal fleets are large‘If motorbikes, where not...?’
Move towards ‘wrap around’ solution
Additional Information
A Comment on PEMS Data Analytics
PEMS log multiple measurements at 1Hz (or higher) resolutionSo even small studies generate large datasets
CO2 kg/km
CO g/km
HC g/km
NOx g/km
0.0 0.5 1.0 1.5 2.0 2.5
EMISSIONS
DEC
IDLE
CRUISE
ACC
0 10 20 30 40 50
TIME in MODE (%)
AVG
AVG RUNNING
0 10 20 30 40
SPEED (km/hr)
Vehicle Specific Power kW.metric ton−1
CO
2 g.
sec−1
0
2
4
6
8
10
−40 −20 0 20 40
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CO2 g.sec−1
●
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123
4
5
6
And a need for robust analytics because of the inherent risk intreating PEMS data like dynamometer data
Additional Information
Example PEMS Data Analytics
HDV vehicle retrofit project
Euro V 44 tonne articulated truck
(DICI turbo-charged 6-cylinder in-line Mercedes tractor unit, axor C2543, diesel engine; engine capacity 11.97 l, maximum output powerof 315 kW@1900 rpm; maximum torque is 2100 Nm@1100 rpm)
HDV fuel system modified to run on Diesel(PD)/biofuel blends
Full studies looked at a full suite of engine data, and gaseous andPM measurements but here focuses is on the analysis of FC data
Additional Information
Example PEMS Data Analytics
Strictly defined routes
Supply to Delivery Depo(A2W) and
Delivery to Supply (W2A)Depo
So, ideal scenario for ‘on-roaddrive cycle’
Total journey for A2W:
FC on Diesel Only Journeys21% higher than on BlendedFuel Journeys
(Arguably a ‘great but usual’result)
Additional Information
Example PEMS Data Analytics
Work by MSc Adrian Ortega Calle using the R package pems.utils
His work includes:
A ‘hole filing’ strategy for the handling of irregular time stampedrecords
(A lot of nice work that we’ll not be talking about...)
Microtrip analysis of Fuel consumption data
(That we will...)
Additional Information
Example PEMS Data Analytics
Example Microtrip Cut of A2W Route
100
150
200
250
0 10 20 30Distance
Hei
ght
cut
1
2
3
4
5
6
7
8
9
10
Additional Information
Example PEMS Data Analytics
Example Microtrip Analysis A2W Route Fuel Consumption
Additional Information
Example PEMS Data Analytics
Comments:
Microtrip analysis provides a useful means of exploring journey data
R provides a means to automating large volume ‘By microtrip cut’analysis of multiple journey datasets
And VSP binning appears to be a highly robust metric forexplaining observed differences
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