next generation - cert.ucr.edu - pems2016.pdfnext generation (smaller, lower ......

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

[email protected]

2 infoWedge,El Dorado Hills, CA 95762

[email protected]

mailto:[email protected]


Overview

1 Research Rationale

2 Building a Smaller (Integrated) PEMS

3 Gas-phase Species Measurement

4 Particulate Measurement

Research Rationale





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






Modular Design

ExhaustSampling

Inlet

Local CPUand Logger

PowerSupply

ActivitySensor(s)

ParticulateSensorModule

Pump

GasesSensorModule

Pump

FlowSensor

Physical Connection

Power Connection

Communication Link


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






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

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


Gas Sensor Evaluation

GasMOD v0.2b CO2

ME

XA

Ref

eren

ce C

O2

10

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EX

A R

efer

ence

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...


Concentrations, Flows and Emissions

Concentrations R ≈ 0.98

GasMOD v0.2b CO2

ME

XA

Ref

eren

ce C

O2

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ME

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

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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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CV

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

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issi

ons

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RE

F C

O2

Em

issi

ons

and

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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]


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)


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


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






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


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]


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

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10

20

30

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50

60

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arS

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Bag 5 parSYNC*

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5 p

arS

YN

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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

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3

−1 1 2 3

0

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0.6

0.8

1

opac

ity

0

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1

scattering

00.20.40.60.81

ionization

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ionization

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2

0

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opac

ity

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scattering

00.20.40.60.81

ionization

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00.20.4

0.6

0.8

1

Thank You

Thank You

Karl Ropkins - [email protected] Li - [email protected]

Andrew Burnette - [email protected]




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


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)


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’)


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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Early On-Road Data

Cycle Time

NO

Em

issi

ons


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ed

0.0000

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0 500 1000

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20

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80

100

120

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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


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)

●

●

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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

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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


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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123

4

5

6

And a need for robust analytics because of the inherent risk intreating PEMS data like dynamometer data


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



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)



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...)



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



Example Microtrip Analysis A2W Route Fuel Consumption



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

next generation - cert.ucr.edu - pems2016.pdfnext generation (smaller, lower ......

Documents