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Doctoral Thesis

Modal pollutant emissions model of diesel and gasoline engines Author(s): Ajtay, Delia Elisabeta Publication Date: 2005 Permanent Link: https://doi.org/10.3929/ethz-a-005163854

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ETH Library

Diss. ETH No. 16302

Modal Pollutant Emissions Model of Diesel and Gasoline Engines

A dissertation submitted to the

SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH

for the

degree

of

Doctor of Technical Sciences

presented by Delia Elisabeta

Ajtay

M.Sc. Mathematics Born

11th August

Nationality

accepted

on

1975

Romanian

the recommendation of

Prof. Dr. Lino Guzella, examiner Prof. Dr. Stefan

Hausberger,

Dr. Martin Weilenmann

2005

,

co-examiner

co-examiner

Abstract

Road traffic accounts for

important part of air pollution. For roughly 30 years, emission limits have been enforced by legislation in Europe and else¬ where. The success of the stringent regulations has been monitored by the environmental agencies. Although in the early days the interest was in the ful¬ fillment of the legislation limits, real-world emissions represent today's main an

focus. Emissions models

are

used to derive

international, national and regional emis¬

sion inventories to

predict

the

using measurements performed in emissions laboratories and impact of different traffic related measures. These emission mod¬

els connect the

driving behaviour and fleet data to the test bench investigations.

The

availability

of vehicle emission models has

recent years. There are

models:

one

emission measurements from

based

on

basically

bag

two

improved significantly in the types of emissions and fuel consumption

measurements and the other based

measurements. Emission models based

fic situations similar to the

on

bag

values

give

on

instantaneous

results for the traf¬

bag. If the driving behaviour changes, new measurements with comparable driving patterns have to be per¬ formed. To account for the additional effects as load, slope or gearshift strate¬ gies, bag based models include correction functions. However, these correc¬ tion functions

based

are

which may not be

one

on a

used to fill the

small number of measurements with few vehicles

representative

for the emissions behaviour.

combination of these correction factors (i.e. when a

full

load)

can

be

Moreover, the

uphill

with

given time to speed, engine speed, torque, etc.

their

a

vehicle drives

extremely misleading.

Instantaneous emissions

modelling

maps the emissions at

a

generating "engine state", like vehicle makes it possible to integrate new, unmeasured driving patterns

over

and calculate their emission factors without further measurements.

sion factors for

a

large

number of

driving

situations

can

This

the model

Thus, emis¬

be determined from

a

small number of measurements.

l

Abstract

goal of this thesis is sumption model. Such a

The

to

develop

an

instantaneous emissions and fuel

model should be

capable

to

predict

con¬

emission factors

for any unmeasured

limited number of measurements.

Beside

road

speed diagram using a that, contributory aspects like load,

gradient, gearshift strategies be, thus, significantly more flexi¬ ble than the existing approaches and would be especially useful for the assess¬ ment of local studies (i.e. the impact of traffic management schemes, change of driving behaviour, etc). should be also included. Such

a

model shall

Due to the fact that the instantaneous emission model relates at each moment

of time the emission

signals

of the measurements is

original

emission

one

signals

to their

of the

generating engine variables,

key

measured in

a

issues for test

If these

peaks

are

flattened

by

successful result. But, the

delayed to their time of forma¬ the engine to the analysers, and

are

tion, since the exhaust gas is transported from the emission

a

the accuracy

convolution.

neglected, the emission events are correlated to the wrong second, resulting in incorrect engine status in emissions modelling. For instantaneous emissions modelling, emission values can be correlated to the correct engine state of the car only if they are at their right location on the time scale. Therefore, these delays and mixing dynamics must be compensated, i.e. the behaviour of the gas transport systems must be modelled and inverted. The modelling of the different gas transport systems is performed using linear time-varying approaches, such that emissions at their location of formation (engine-out or catalyst-out) are reconstructed from the signals recorded at the analyser. Using

dynamic aspects

of the exhaust transport

the reconstructed emissions

data,

an

are

instantaneous emissions model is

pollutants and for various categories of ve¬ hicles. The model performs reliably, emission factors from several real-world driving situations being accurately forecasted. developed

for different classes of

For the modern

gasoline cars equipped with a three-way catalyst, the approach has to be extended by modelling separately the engine-out emissions and, af¬ terwards, the catalyst-out emissions. In order to take into account the transient generation of exhaust gases, the engine-out emissions are modelled using a 4D emissions model. With the engine-out emissions as input data, a simple catalyst submodel is developed, based on the oxygen storage mechanism. The validity of the model and the parameters estimation procedure is checked by applying them to real world case studies. It is demonstrated that the model is capable of predicting the operating behaviour of the catalyst under realistic conditions and is, thus, suited for use within emissions modelling.

11

Zusammenfassung

Der Strassenverkehr ist eine der

sionsgrenzen

setzgebung

Hauptquellen der Luftverschmutzung.

Die Emis¬

Motorfahrzeugen wurden seit etwa 30 Jahren durch die Ge¬ Europa stufenweise verschärft. Der Erfolg dieser Regulierungs¬

von

in

chritte wurde seither durch Umweltbehörden kontrolliert. Während das Inter¬ esse

die

früher mehr der

Ermittlung

gesetzlichen Vorschriften galt, Emissionen im Mittelpunkt.

Erfüllung

der realen

der

stehen heute

Emissionsmodelle, welche auf Messungen an Rollenprüfständen basieren, wer¬ den benötigt um internationale, nationale und regionale Emissionsbestandsauf¬ nahmen abzuleiten. Ausserdem werden diese Modelle genutzt,

um

den Ein-

fluss unterschiedlicher

Verkehrsparameter (z.B. Geschwindigkeitslimiten) auf die Emissionen vorhersagen zu können. In diesen Emissionsmodellen werden Daten zu Fahrverhalten und Fahrzeugbeständen mit den Emissionsmessungen von Rollenprüfstandsuntersuchungen gefaltet. Seit den letzten Jahren stehen mehr und mehr

Verfügung.

Es

zwei Arten

gibt grundsätzlich

verbrauchsmodellen: der eine

Typ

basiert auf

Fahrzeugemissionsmodelle von

zeugen

nur

Emissions- und Kraftstoff¬

Sackmessungen

auf "online"

zur

Messungen. Emissionsmodelle, die auf Ergebnisse für jene Verkehrssituationen,

und der andere

Sackwerten

die

zum

basieren,

er¬

Füllen der Ab¬

gassäcke genutzt wurden. Wenn sich das Fahrverhalten ändert, müssten neue Messungen mit entsprechenden Fahrmustern durchgeführt werden. Modelle, welche auf Sackmessungen beruhen, beinhalten teilweise Korrekturfunktionen, mit denen

Zuladung, Steigung und Schaltstrate¬ gie zu berücksichtigen versucht. Allerdings basieren diese Korrekturen meist auf einer geringen Anzahl Messungen mit wenigen Fahrzeugen, welche nicht repräsentativ für das Emissionsverhalten der Flotte sein könnten. Ausserdem man

zusätzliche Effekte wie

kann die Kombination solcher einzeln ermittelter Korrekturen extrem irrefüh¬ rend sein. Wie soll z.B. eine

Steigungsfahrten Die "online"

Bergfahrt

und Fahrten mit

mit

Zuladung

Zuladung separat

Emissionsmodellierung bezieht die

berechnet

weden,

wenn

gemessen wurden?

Emissionen

zu

einem bestimm-

111

Zusammenfassung Zeitpunkt auf den aktuellen Motorzustand: Fahrzeuggeschwindigkeit, Mo¬ tordrehzahl, Drehmoment, etc. Dies ermöglicht es, neue, nicht gemessene Fahr¬ ten

muster in das Modell

Messungen

zu

zu

integrieren

und deren Emissionsfaktoren ohne weitere

berechnen. Dadurch können für eine grosse Anzahl

situationen Emissionsfaktoren

aus

einer kleinen Anzahl

von

von

Fahr¬

Messungen

be¬

stimmt werden. Das Ziel dieser Doktorarbeit ist es, ein "online" Emissions- und Kraftstoff¬

verbrauchsmodell

entwickeln. Ein solches Modell sollte

geeignet sein für für jeden beliebigen,

zu

Einzelfahrzeuge und Fahrzeugklassen Emissionsfaktoren ungemessenen Geschwindigkeitsverlauf vorherzusagen. Zudem sollten Aspek¬ te wie Ladung, Strassengefälle und Schaltsstrategien korrekt mit einbezogen werden. Ein solches Modell wird dadurch flexibler als vorhandene Ansätze und ist besonders nützlich für die

Bestimmung

von

Verkehrslenkungsmassnahmen, änderungen

von

(d.h. Einfluss Fahrverhalten, etc.).

lokalen Studien in

Aufgrund der Tatsache, dass die Emissionssignale in den "online" Modellen für jeden Zeitpunkt dem Zustand des Motors zugeordnet werden müssen, ist die zeitliche Exaktheit der Messung einer der wichtigsten Aspekte für erfolg¬ reiche Ergebnisse. Die direkten gemessenen Emissionssignale treffen jedoch auf Grund des Transports durch das Auspuffsystem und die Messleitung ver¬ spätet im Analysator ein. Zudem werden die Emissionsereignisse durch den turbulenten

Transport "verschmiert".

Wenn diese

dynamischen Aspekte des Abgastransportes vernachlässigt wer¬ den, werden die Emissionsereignisse mit zeitlich verschobenen Motorzustän¬ den korreliert. Für "online" Emissionsmodelle können Emissionswerte

nur zum

richtigen Motorenzustand korreliert werden, wenn sie sich auf den richtigen Zeitpunkt beziehen. Daher müssen diese Verschiebungen kompensiert werden, d. h. das Verhalten des den. Die nem

Modellierung

Gastransportsystems

muss

der unterschiedlichen

linearen zeit-variabeln Ansatz

modelliert und invertiert

Gastransportsysteme

durchgeführt,

so

wer¬

wird mit ei¬

dass die Emissionen

am

Ort der aus

Entstehung (Austritt aus dem Motor oder Austritt aus dem Katalysator) dem vom Analysator aufgezeichneten Signal rekonstruiert werden können.

In der

Folge

wird

aus

den zeitlich

modell für verschiedene

korrigierten Emissionsignalen ein Emissions¬ Schadstoffklassen und Fahrzeugkategorien entwickelt.

Es basiert auf der Korrelation der Emissionswerte mit Drehzahl und Drehmo¬ ment des Motors. Das Modell funktioniert für

gewisse Fahrzeugklassen

zuver¬

lässig:

die Emissionsfaktoren werden für ann"hrend alle realen Fahrsituationen

genau

vorhergesagt.

IV

Zusammenfass ung

Dieser Ansatz musste

jedoch für moderne Benzinfahrzeuge, ausgestattet mit einem Drei-Wege-Katalysator, ausgeweitet werden. Die Emissionen aus dem Motor und das Verhältnis des Katalysators werden separat modelliert. Die Emis¬ sionen liert zu

aus

dem Motor mussten mit einem vier-dimensionalen Kennfeld model¬

werden,

um

die transiente

Abgasproduktion zu berücksichtigen.

Zusätzlich

Drehzahl und Drehmoment werden die Emissionen auf die zeitliche Ab¬

leitung

des

Saugrohrdrucks bezogen.

de ein Teilmodell

Für das Verhalten des

Katalysators

wur¬

entwickelt, für welches die "engine-out" Emissionsdaten als

"Inputdaten" genutzt wurden. Es basiert auf dem Sauerstoffablagerungsmecha¬ nismus. Die Genauigkeit des Modells und die Parametrierung wurden über¬

verglichen wurden. Es wird gezeigt, dass das Model geeignet ist, die Wirkung des Katalysators unter realistischen Bedingungen vorherzusagen und das es folglich für Emissionsmodellierungen

prüft,

indem diese mit realen Fallstudien

verwendbar ist.

v

Zusammenfassung

Seite Leer / Blank leaf

Contents

1.

Introduction

1

The

1

1.3.

pollutants and the environment Legislation Real-world driving cycles

1.4.

Overview of the emission models

10

1.4.1.

Average speed

11

1.4.2.

Traffic situation models

14

1.4.3.

Instantaneous emission models

16

1.1. 1.2.

2.

1.5.

Scope

1.6.

Contribution

Modelling

8

models

of the thesis

19

21

of the exhaust gas

2.1.

Introduction

2.2.

Methodology

transport systems

23 23

of the model

27

2.2.1.

Basic model

27

2.2.2.

Evaluation of the exhaust volume flow

28

2.2.3.

Raw gas

2.2.4.

Exhaust system of the

2.2.5.

Dilution system model

38

2.2.6.

Overall validation

41

system model

30 34

car

of the inversion

43

2.3.1.

Basic inversion model

43

2.3.2.

Inversion of the

2.3.3.

Inversion of the exhaust system of the

2.3.4.

Inversion of the dilution

2.3.5.

Overall inversion

47

Static instantaneous emission model

49

2.3.

3.

5

Methodology

raw

gas

analyzer system car

analyser system

44 45 46

3.1.

Introduction

49

3.2.

Methodology

52

3.2.1.

Measurement

3.2.2.

Model

procedure

development

52 53

vii

Contents

4.

3.3.

Validation

60

3.4.

Conclusions

65

Dynamic

5.

67

4.1.

Introduction

4.2.

Dynamic engine

4.3.

Validation for different

4.4.

4.3.1.

Diesel

4.3.2.

Gasoline

68

model

loads, slopes and gear-shift strategies

.

72 75

case

77

case

78

Conclusions

81

Dynamic catalyst model 5.1.

Introduction

81

5.2.

Methodology

84

5.2.1.

Mathematical model

84

5.2.2.

Parameter estimation

87

5.2.3.

Static conversion

88

curves

5.3.

Validation

92

5.4.

Conclusions

95

6.

Conclusions and Outlook

A.

Appendix

vin

67

instantaneous emission model

97

101

A.l.

Kinematic characteristics of the real-world

A.2.

Set-up for the measurements tems modelling

driving patterns

necessary for the

.

.

101

transport sys¬ 103

1. Introduction

become ticles

decades, the environmental effect of burning fossil fuel has important issue. Smog, greenhouse effect, acid rain and toxic par¬

the past

During

an

are

increasing traffic, heating and industrial thermal stricter legislation, fuel consumption and vehicle emissions

consequences of the

processes. Under

of vehicle fleet and of average per distance have been reduced, but the increase distance travelled have counteracted these measures. Due to the

quantity

of information necessary to determine the different para¬

emissions, direct measurement becomes impractical and expensive. Therefore, models for predicting emissions, although difficult

meters

to

related

to

the traffic

develop, represent

impact

alternative to direct measurement.

pollutants

1.1. The

The

an

of traffic air

and the environment

pollution

of undesirable material in the

ence

cause

harmful

transport is

effects, both of the

one

environment and to the human health. Road

to the

major

high. Air pollution represents the pres¬ air, in quantities which are large enough to

is

sources

of air

pollution.

Since human

population

of emissions from road transport, vehicle emissions con¬ tribute to the personal exposure even more than expected from their share on is close to the

sources

total emissions

([40], [77]).

spark ignited gasoline engine. Gasoline mix¬ ture represents a blend of paraffins and aromatic hydrocarbons which combust with air at a very high efficiency. The simplified combustion reaction is: The

majority

of the vehicles

gasoline

Carbon dioxide

(CO2)

+

use a

02{in air)

—»

CO2

+

H2O

+ heat

(1.1)

(H2O) are the desired products of the fuel imperfect combustion process, the following

and water

combustion. However, due to the

1

1. Introduction

undesired

compounds

result



carbon monoxide



unburned



nitrogen



hydrogen (H2,



carbon dioxide



water



oxygen

as

(CO,

exhaust

at the range

hydrocarbons (HC,

oxides

(H20,

(02,

(NOx,

components1 ([24]):

at

of 0.1-6 vol. %);

the range of 500-5000

at the range of 100-4000

at the range of 0.17 vol.

(C02,

at the range of

ppm);

%);

10-13.5 vol. %);

at the range of 10-12 vol. at

ppm);

%);

the range of 0.2-2 vol. %).

gasoline vehicles, diesel cars are increasingly used due to the econ¬ In omy of operation and decrease of greenhouse gases, especially of C02. this case, the fuel is injected into a highly compressed charge of air where the temperature is high enough for the combustion to occur. Thus, diesel engines are based on a compression-ignited process. Due to the nature of this combus¬ tion process, some quantities of unburned fuel, lubricating oil emissions and Beside the

large C02

numbers of and

dry

soot

particles

result. Beside the desired components

H20, the exhaust emissions of diesel engines consist of:

particulate



solid exhaust: soot



gaseous exhaust: carbon monoxide

of



02,

matter

(PM);

(CO), hydrocarbons (HC) and oxides

nitrogen (NOx);

liquid exhaust: soluble organic cating oil) and liquid sulfates.

fraction

(SOF:

unburned fuel and lubri¬

Carbon dioxide

present in the fuel will be eventually transformed to carbon dioxide in the atmosphere. Even if, due to incomplete combustion, carbon All the carbon

exhaust, it is ultimately oxidized in the atmosphere to form C02. Carbon dioxide is a major source to the greenhouse effect, which monoxide may result leads

'N2

2

finally is

a

to

as

global warming. C02 production

remainder

is

an

invariable consequence

1.1.

of

as

pollutants

fossil fuel and, at least, the process of fuel

burning

efficient

The

and the environment

burning

should be

as

possible.

Carbon monoxide The main

atmosphere engines, especially of gasoline

source

combustion

for CO in the

from the exhaust of internal

vehicles

[76]. Carbon monoxide

is

poorly soluble in water. In the human body, carbon monoxide binds with haemoglobin to form carboxyhaemoglobin (COHb), causing a reduction in the oxygen carrying capacity of the blood. This determines headache, dizziness or nausea and, at high level of a

colourless, inodorous and tasteless

comes

gas that is very

COHb, becomes lethal [39]. Carbon monoxide is

produced mainly during rich combustion situations, when there is insufficient oxygen to burn completely all the hydrocarbons from the fuel into C02. Some of these rich situations appear during transient engine operations like acceleration or high torque demand. Also, when the engine is cold, it is necessary to enrich the air/fuel mixture, causing high levels of CO until the engine is warmed-up. In microenvironments in which combustion engines are used under conditions of insufficient ventilation, like underground car parks or road tunnels, the mean levels of carbon monoxide can rise to values much higher than those from the ambient outdoor air, becoming thus extremely dangerous for the human health and hence, relevant for the ventilation design. Hydrocarbons In the vehicle exhaust there is

pounds.

The most

large variety of unburned hydrocarbon com¬ important are paraffins, olefins, acetylenes and aromatics. a

As for

CO, hydrocarbons are caused by the lack of oxygen when the air/fuel mixture is rich. Beside that, other reasons for hydrocarbon emissions are: flame

quenching at the walls, filling of crevices with unburned mixture, absorption by oil layers, incomplete combustion (partial burning or misfire), bulk quenching and evaporative emissions [38]. Due to their

and

variety,

the

hydrocarbons

have different

impacts

on

human health

example, can lead to leukaemia and it is carcinogenic to humans [68]. In the troposphere, hydrocarbons react with ni¬ trogen dioxide (N02), forming ozone and photochemical smog. Ozone causes cough, throat irritations, pain on deep breath, chest tightness and, sometimes, headache and nausea [36]. Additionally, ozone determines the damaging of vegetation. on

the environment. Benzene, for

3

1. Introduction

Oxides of nitrogen

nitrogen are either direct products of the combustion in engines, like nitric oxide (NO) and nitrogen dioxide (N02), either a product of the catalytic converter like nitrous oxide (N20). The first two species are collectively de¬ noted as NOx. They are produced during combustion when oxygen reacts with nitrogen due to a high combustion temperature (> 1500°C). Nitric oxide (NO) is colorless, inodorous, tasteless and relatively non-toxic for humans. Similar to CO, nitric oxide is eventually oxidized in the atmosphere Oxides of

to

form

N02.

Nitrogen

dioxide

(N02)

is reddish-brown in

colour, extremely toxic and has a human pulmonary functions, caus¬

harsh odor.

Nitrogen dioxide has effects on ing damages of lung tissue, couching, bronchitis, etc. Beside the health effects, N02 is extremely important to monitor because: (a) it is also an absorber of visible radiation which could have a direct role on the global climate change if its concentration were to become too high; (b) it is a key factor in the formation of ozone in the troposphere and (c) it is, along with atmospheric sulfur oxides, responsible for acid rains [60]. Particulate matter Airborne

particulate

matter

(PM) represent

a

combination of

organic and inor¬ catalyst, diesel cars and

ganic substances. Gasoline vehicles with or without heavy-duty trucks, all emit particles mainly in the range of 0.1-0.2 fim in di¬ ameter. Gasoline cars equipped with three way catalytic converters emit much lower particle masses than those without, while diesel cars emit about 100 to 1000 times the particle mass of a gasoline car equipped with a catalytic con¬ verter.

Diesel

particulate matter is almost pure carbon and exists as a sub-aggregate of ultra-fine carbon spheroids with aerodynamic diameters of around 0.1 jum [69]. Apart from the presence of this unburned carbon in the exhaust, which is a con¬ sequence of incomplete combustion and implies therefore lower efficiency, the particulate matter may cause lung diseases. Significant relationships between particulate air pollution and human health have been found by epidemiological studies [64]. The particulates are usually denoted by PM2.5, which represents "particulate matter of size less than 2.5 /zm". However, the subject of particulate matter measurements and modelling will not be discussed in this paper, but excellent research on this topic can be found in

4

[53], [52], [54].

1.2.

Legislation

Sulfur oxides This sulfur

Both, gasoline and diesel fuels, contain sulfur in different

amounts.

is oxidized

(S02). Oxidation sulfuric acids, which

during

combustion and

produces

sulfur dioxide

of sulfur dioxide leads to the formation of sulfurous and

deposited to the earth by rain. This is called "acid rain" and has caused deforestation in Europe and North America and serious damages to buildings. Current regulation on sulphur oxide emissions are very strict and are presently can

be

fulfilled.

1.2.

Legislation

Motor vehicle traffic is

one

of the most

important sources for investigation in [51] indicates that

air

pollution

motor traffic throughout is the major source for air pollution in megacities, in half of them being the single most important source. Since 1950, the global vehicle fleet has grown ten times and it is estimated to double again within the next 20-30 years [56]. As cities expand, more people will drive more vehicles over greater distances and for longer time. Emissions caused by motor traffic are thus important to be

the world.

The

monitored and controlled. In

tightening of emission levels have been is¬ sued. The first passenger car emissions regulation was the directive 70/220/EEC and for heavy duty vehicle emissions the first directive was issued in 1998. The first mandatory European vehicle emission levels was set by the Euro-1 stan¬ dards introduced in the 91/441/EEC directive. Consequently, the Euro-2 stan¬ dard was set within the 94/12/EEC directive and the Euro-3 standard by the 98/69/EG directive. Presently, the Euro-4 standard is going to come in power Europe,

several directives for the

in 2006. In

Switzerland,

the ECE/UNO to

the ECE

the first emission limits

regulations.

regulations,

were

introduced in 1971,

In order to achieve the air

stricter emission limits

quality targets

conform

introduced in 1982. These

developed within the framework of the EFTA's "Stockholm group". In 1987 the next regulations, called FAV1, were enforced by setting first emission limits for diesel vehicles and by requiring three-way catalytic converter for gasoline vehicles. Stringent requirements for particles of diesel vehicles were set in 1998 within FAV2. Since 1996, the European legislative levels have been adopted in Switzerland. The evolution of Swiss regulations

for the limits

were

by adopting

were

5

1. Introduction

Class

Year

CO

HC

NOx

HC+NOx

Particles

[g/km]

[g/km]

[g/km]

[g/km]

[g/km]

0.25

0.62

Gasoline FAVl

1987

2.10

Euro 1

1991

3.16

1.13

Euro 2

1994

2.20

0.50

Euro 3

1998

2.30

0.20

0.15

Euro 4

2006

1.00

0.10

0.08

FAVl

1987

2.10

0.25

0.62

0.370

FAV2

1998

2.10

0.25

0.62

0.124

Euro 2

1994

1.00

Euro 3

1998

0.64

Euro 4

2006

0.50

Euro 5

2009

-

Diesel

0.10

Table 1.1.: Swiss and

and

European

European

are

0.080

0.50

0.56

0.050

0.25

0.30

0.025

placed

on a

standards for emissions of passenger

given

cars

in Table 1.1.

requirements, the vehicles dynamometer and driven through a specific

legislative

chassis

0.025

0.08

standards for emission levels is

To check the fulfillment of the

under test

0.70

emission

driving cycle. The

European legislative cycle (known

as

NEDC

-

New

European Driving Cy¬

cle) consists of an artificially created driving speed time series with low dynam¬ ics (see Figure 1.1). It contains a synthetic urban driving pattern (called ECE or

UDC

EUDC

-

-

Driving Cycle)

Extra Urban

Until the was

Urban

adoption

and

an

extra-urban

as

Driving Cycle).

of the Euro-3 standard

started with cold

driving pattern (known

engine

and

a

,

the

40 seconds

procedure was that the vehicle idle phase was run to warm-up

the

engine before the start of the measurements. From the introduction of Euro3, this pre-conditioning warm-up period was eliminated. In this way, emissions are measured from the beginning of a cold-start, making the fulfillment of Euro3 level much harder to acquire than the Euro-2 standard.

This

cycle, which skips the 40 seconds idle phase, is called NEDC 2000 (New European Driving Cycle 2000) or MVEG (European Motor Vehicle Emis¬ sions Group).

6

new

1.2.

200

400

600

time

Figure

1.1.:

In the United

800

1000

1200

time

[s]

Speed profiles

Legislation

of legislative NEDC

States, different emission standards

[s]

(left) and US FTP-75 (right)

are

enforced, with

even more

stringent requirements for California. The FTP-75 (Federal Test Procedure) cycle is being used in the US as a legislative cycle (Figure 1.1). The first 505 seconds of this test start when the engine is cold and represent the first part of this cycle. The test continues for another 867 seconds, at which point the vehicle is shut off. After a ten minutes interval, the first part is repeated with a warm engine. Effective model year 2000 vehicles have to be additionally tested on two Sup¬ plemental Federal Test Procedures (SFTP) designed to address short comings within the FTP-75 in the

(US06 cycle) and (2) the In

representation of: (1) aggressive, high speed driving use of air conditioning (SC03 cycle).

dynamometer driving schedule for light-duty vehi¬ California Air Resources Board. It is a more aggressive

California, the LA92 is

cles

developed by driving cycle then

the

a

the federal FTP-75. It has

higher speed, higher acceleration,

fewer stops per kilometer and less idle time. Most of the real-world emissions

strong acceleration, deceleration

are or

American

generated during

gear-shift phases.

phases like Both, European and speed and accelera¬

transient

legislative cycles have rather low maximum tion levels, which causes large discrepancies between emissions on certification tests and emissions

from real-world situations [21]. Therefore these standard

driving cycles are not representative their corresponding emission levels.

for real-world behaviour and, hence, for

7

1. Introduction

1.3. Real-world

driving cycles

agencies such as the Swiss Agency for Forest, Landscape (SAEFL), the Environmental Protection Agency

For about 20 years environmental Environment and

(EPA) in the United States, etc.,

have monitored the

success

of the emissions

regulations. The evolution in vehicle technologies (mainly in the electronic engine control systems) has caused, however, an increase in the difference be¬ tween emissions in legislation tests and those from real world driving. Thus, legislative cycles are no longer representative for real-world driving behaviour and, consequently, for the assessment of pollutants. The use of real-world driving cycles is therefore one of the key issues in emissions inventories. campaign has been conducted on Swiss roads in order to determine real-world driving behaviour [22]. Cars equipped with velocity and time logging devices were driven by special drivers, who were told to follow the flow of the traffic. During this measurement cam¬ paign 759'299 seconds of driving manner have been recorded and analysed by Within SAEFL,

statistical

an

extensive measurement

means.

Recorded data

were

characteristics. For

divided into

driving patterns

that, 14 parameters

were

based

on

the different road

defined to describe the road type:

change of the average speed during driving pat¬ tern, standard deviation of velocity, road gradient, percentage of time with con¬ stant velocity, percentage of time with velocity zero, length of the driving pat¬ mean

travel

speed, sign

of the

tern, etc.

By

means

of cluster

analysis,

tive for Swiss

12

driving patterns

which

behaviour have been selected to

driving real-world driving cycles. Each of these cycles patterns. Their corresponding speed time series

are

most

develop

representa¬

a new

contains three of these are

depicted

in

Figure

set of 4

driving 1.2.

driving situations, AE1R, AE2R and AE3R. Cycle R2 contains a motorway part A4R and two rural driving compo¬ nents, LE1R and LE2sR. Cycle R3 is formed out of rural driving LE2uR and urban driving LE3R and LE5R. Finally, R4 consists of urban driving LE6R, highway and urban stop-and-go driving, StGoHW and StGoUrb. Cycle

Rl is

composed

of three motorway

Within the frame of the

European

research program ARTEMIS

(Assessment

Reliability of Transport Emission Models and Inventory Systems), another real-world driving cycle called Common ARTEMIS Driving Cycle (CADC) has been developed [9]. The goal was to have a common cycle for all the and

8

1.3. Real-world

400

200

0

ft >m

AVl

WANVA

1.2.:

Speed profiles

,

Aa À. A

I 1500

1000

time

Figure

StGoUrb

StGoHW

500

0

1400

1200

1000

800

600R4

driving cycles

[s]

of the Swiss real-world

driving cycles Rl, R2, R3,

R4.

ARTEMIS partners, suitable for both

representative Using

for the actual

driving

bag

and instantaneous measurements and

conditions of

European

the Swiss data, the data from another multinational

cars.

project ([10])

and

European driving patterns have been identified by factorial analysis and clustering tools: congested urban, urban dense, urban with low speed, urban with free flow, urban unsteady, secondary roads unsteady, secondary rural roads, rural roads with steady speed, main-road unsteady, main-road with steady speed, motorway unsteady and motorway with additional data recorded in

Naples,

14

steady speed. aggregated road highway (Figure 1.3). Each of be attributed to a specific road

CADC is divided in three main parts which account for the

categories: urban,

rural

(i.e. extra-urban) and

sub-cycles that can "sub-category", allowing thus disaggregation of the emission levels at various driving conditions. The three main parts are independent from one another and each of them includes a pre- and a post-conditioning phase. these parts contain 4

Unlike R1-R4

cycles

or

5

which follow the NEDC's fixed,

predefined gearshift

9

1. Introduction

1

W

i

i

1

i

Rural

Urban

I WmilPlMT I

500

f

ull

i_J

II

1

2000

1500

1000

i

|J

2500

3000

I

I

3500

timersl

Figure

1.3.:

Speed

time series of the CADC

cycle

strategy, the CADC considers four strategies for the gearshift depending the technical characteristics of the vehicles

Kinematic characteristics and are

given

in

Appendix

description

(power,

mass, transmission

on

ratios).

of the Swiss and of the CADC

cycle

A.l.

cycles (MEC01) de¬ veloped by a research team from California [16], the cycle for the area of Hong Kong [74] or the Istanbul urban driving cycle [27]. Similar real-world

driving cycles

are

the modal emission

Although the real-world cycles represent a significant improvement towards representative emissions factors, they cannot take into account driving style dis¬ tribution, different loadings of the vehicle or gradients of the road. Therefore, models that are able to predict the emissions generated by these contributory aspects are of increasing interest.

1.4. Overview of the emission models

From 1995 until

2000, in Switzerland, the total vehicle-km of all road vehicles

has increased

about 7 percent

future is

by

and, by the

expected

to

year

[44]. This growth is likely

to

continue in the

2010, the increase in total vehicle-km relative

be about 19-20 percent

to 1995

[44].

Both, the increased vehicle fleet and larger distance covered yearly per vehicle have counteracted the

improvements generated by

the stricter

legislation.

Using test bench measurements, emission models are developed to obtain re¬ gional, national or international emission inventories and to predict the impact

10

1.4.

of different traffic related

Overview of the emission models

Emission models

measures.

can

be

split

into two

categories:



Fleet emissions models:

cycles and compute

a

they

use

weighted

emission factors of different measured

sum

that is

to fleet statistics to

multiplied

generate fleet emission models. •

Vehicle emission models:

they

allow to calculate emissions for any

pattern, for any combination of vehicle load, slope out of a limited set

of measured test

or

speed

gear-shift strategy

cycles.

depend on a large set of input parameters: traffic situation, loading, gradient of the road, driving behaviour, etc. Due to the quan¬

Vehicle emissions vehicle

tity

of information necessary to determine the different combinations related to

emissions, direct

the traffic

measurement becomes

impractical

Therefore, models for predicting emissions represent

an

and

expensive.

alternative to direct

measurement.

For

more

than

surements

a

decade attempts have been made to store

of test

on

chassis

dynamometers

way, such that emissions of other

driving

or

engine

conditions

or

map emission

mea¬

test

benches in

can

be calculated out of

a

neutral

them without additional measurements.

variety of vehicle emissions and fuel consumption models derived for different spatial and temporal scales. These models can be categorised into three main groups with increasing level of complexity: (a) average speed mod¬ els, (b) traffic situation models and (c) instantaneous (modal) models.

There is

a

1.4.1.

Average speed models

speed models relate emissions and fuel consumption to the aver¬ age speed of each driving cycle. The emission and fuel consumption rates are generated from chassis dynamometer measurements for a variety of simulated cycles at different average speed levels.

The average

example of this type of the model is the COPERT III computer program developed by the CORINAIR Working Group on behalf of the European Com¬ mission [59]. COPERT III uses linear regression to express emission factors as function of the average travelling speed (Figure 1.4).

An

11

1. Introduction

NO*-PC

Gasoline

2.0 1- EURO I

1.4

-ce

2,5

2,0

j--y«-8.5-4E=Qdx?---üJa8dx-+-QJS2il.

o-

R2 =Ü.0J772 1,5

SS

°

--

Raw Value

s

Average Valu«s

2

o

o

o

%

1,0

-p--

P.

0,5

^M:

--

8

L-l

0,0 20

40

1.4.: Calculation of

Figure

function of the average

80

60

Real

Mean

Cycles

NOx

100

140

Speed (km/h)

emissions for Euro-1

speed (source

120

COPERT

gasoline

vehicles

as

III)

prediction quality of the emission factors decreases dramatically from preECE vehicles (since emissions were high and presented good correlation with mean speed) to Euro-1 vehicles (where emissions become lower and more scat¬ tered between vehicles and operating conditions). The Pearson coefficient R2 The

for the

quality

of CO estimation

values between 0.133 and 0.159

drops

from 0.924

(for pre-ECE vehicles)

(for Euro-1 cars), depending

on

the

to

engine

capacity (Table 1.2). In

2003, about 15 European countries

were

official emission estimates, among them

using

the COPERT III model for

Belgium, Denmark, France, Greece,

Ireland, Italy and Spain. Emission factors for Euro-2, Euro-3 and Euro-4 vehi¬ cles

were

derived from the Euro-1 emission functions

An evaluation of the COPERT III model remote

sensing

measurements

in Sweden

was

[26]

favorable agreement for CO and HC emissions

using reducing

performed by

factors.

on-road

optical

with the results

showing not so and better quality for NOx emis¬

sions. In the

tion

US, the MOBILES model developed by the U.S. Environmental Protec¬

Agency [15] and the EMPAC model developed by the California Air Re¬ sources Board (CARB) [20] attempt to determine the overall emission levels,

12

Table 1.2.:

-

0.247

0.781 0.767 0.656 0.719

0.294V + 0.002478V2

0.00957V2 0.377V + 0.00283V2

0.00203V2

0.159

0.2955V + 0.0018V2

passenger

-

cars.

V represents average drivin

0.145

0.001728V2 0.245V +

0.133

0.2867V + 0.0022V2

0.23012V + -

-

0.613

0.0011639V2

0.22V +

0.1511V +

-

-

-

12.826

9.617

0.68V +

0.825

0.790

ln(V)

0.00377V2

260.788 V~u-yi

-

-

-

9.846

9.446

17.882

8.273

14.577

14.653

37.92

161.36-45.62

0.102

0.747

0.0026V2

300Y-U.797

0.158

0.406V + 0.0032V2

26.62-0.44V +

gasoline

5-130

CC>2.01 of CO emission factors for

5-130

1.4

'

400

8

measured

(solid)

and fitted

500

[s]

(dashed) step

response of

validation of the transport model for the

Right graph:

signal at the tailpipe, measured analyzer signal,

gas system in the FTP-75 test. Note: dash-dotted:

u(t),

dashed: simulated

analyzer signal, y(t),

solid:

approach is to observe how well the model sim¬ ulates the system output based on a given input.This method of simulation is a commonly applied procedure that compares the actual measured output of a model

[49]. One

system

to the

such direct

simulated output from the model.

For the validation of the was

run,

lambda

using

sensor

a

developed

vehicle mounted

(ETAS LSU)

was

raw

on

system model,

a

transient FTP-75 test

dynamometer. A continuous the end of the tailpipe and was

the chassis

mounted at

having a fast response time of 20 ms. This In addition, the raw gas sensor was used to measure the input signal u(t). signal y(t) was recorded at the analyzer.

calibrated

Using

as an

oxygen sensor,

the measured

input u(t)

of the

raw

and the identified parameters of Equation

2.7,

signal), y(t),

to the

ure

is simulated and

compared

tailpipe) corresponding output (analyser measured output, y(t). In Fig¬

system (oxygen signal

at the

the

2.8 the excellent agreement between measured and simulated output

can

be

checked.

33

2.

Modelling

of the exhaust gas transport systems

system of the

2.2.4. Exhaust

The exhaust system of the in Section 2.2.1,

as a

car can

car

be modelled,

perfect delay

and

using

the basic model described

first order system. Additional

a

com¬

plexity arises here since the volume flow of the exhaust gas may vary by a factor of fifty, depending on the engine load. Therefore, the simple approach used in Section 2.2.3 must be extended.

catalyst outlet location

The transport of the exhaust gas from the

to the

tailpipe

goes with the volume flow of the exhaust gases. This volume flow varies be¬ tween 3 1/s and

liters,

the total

1501/s for

delay

a

typical

2000

cm3

car.

For

varies between 0.1 and 6 seconds

an

exhaust system of 20

(Figure 2.2).

Therefore, the parameters of Equation 2.1 become time-varying. In conse¬ quence, the sequence of the subsystems becomes important here. The results are different if the transport time delay is considered before or after the mixing

Following the geometry of the exhaust transport system, the lay should be split ideally into two parts (corresponding to the pipes), dynamic part in between (corresponding to the silencer). chamber.

Is

Input

u(t)

Signal at the tailpipe

order

differential

aTd(t)

with the

Transport

Mixing

Transport

time de¬

(1

equation

-

a)Td(t)

m

Tm(t)

Figure

2.9.: Block

diagram

of the exhaust system model

Again it was found by least squares optimization into two equal parts (a 0.5), thus:

that the time

delay

can

be

split

=

TmWi(t ?^) ,(4 ZM)=0(t_ZkÖ>) +

This result of the

was

(2.io)

+

+

tested for two different vehicles with

completely different shapes

tailpipe systems. Equation 2.10, Td(t) and Tm(t), are now functions shall be determined according to Equations 2.2 and 2.3.

The parameters of

of the

volume flow and

Intro¬

ducing

an

additional parameter p, the coefficients of the system

can

be obtained

as:

Td(t)=pTT(t),

34

Tm(t)

=

(l-p)TT{t),

0

/

Q.

O Ü

y

/

Q.

^.---

y

f

200

n

O O

er

i

/y

.,--"

,.--

-'"

' y

/r^^'

^

„y

/

£

^-"

10

/jy

/

oo

^-

CX

y /

/ /

cted

TJ

/

/ /

60°

^

/

/

â30

*E

°

X

F jx

Validation

^

n

10

20

30

CO measured

"0

40

200

[g/km]

400

CO, 4

predicted NO

[g/km]

y

/

Oy»

"e

â

600

measured

y

/

3

Oy y

/ /

y j^y

/ / /

to

t

/

jo yd.

/

„--*

.**

*''*'

''IT /

--

^'

Q/O

/

-*

*'.

Çr ~y

^--"

y

y*-'"' o

12

Figure

3.8.:

3

4

HC measured

[g/km]

Comparison

)

between

predicted

1

2

NO

measured

with

3

4

[g/km]

map and measured

bmep-n

emission factors for the average pre Euro-1 vehicle

(E

the normalised

mean

the fractional bias:

square

FB

0.5

the fraction of predictions within

—E \ ^- ^

E



E

(Em+Ep) R

a

\Em

2





EmJ



{Ep Epj —

;——^r^-

factor two from measurements: FAC2.

Em and Ep represent the measured and predicted emission factors, Em

Ep

values and o),

the

(coxid* CRed)

the total volume of the

-

c&J

rads

TWC,

e

is

obtained:

(5.6)

rox

-

a

are

constant

representing the

volume

fraction of the gas phase, V denotes the volumetric flow of the exhaust gas, whereas OSC stands for the oxygen storage capacity of the TWC in mol/m3.

Generally,

is has been found that the

faster than the balance be

86

ones

dynamics

of the gas

of the oxygen storage and release.

species

are

Therefore, the

much mass

equations (5.4) and (5.5) can be applied as static equations. This can done by setting the left-hand side of the two equations to zero. After some

5.2.

algebra,

the

following

terms

are

Methodology

obtained for both coxid and CRed concentra¬

tions: v

V +

°Oxid

0.b-e-Vc-OSC-ki-(l-i/>o) V

.rin

v

CRed

With these

equations,

_

/< -7\

LRed

/e

~

V +

e-Vc-OSC-k2-*Po

both concentrations

be calculated from the oxygen

can

occupancy, temperature and volumetric flow.

The concentrations

are

in turn

used for the estimation of the reaction rates and,

consequently, of i/jo

conversion efficiencies of HC, CO and

further

NOx

o\

P-o)



are

on



The

characterised

as

static functions of this relative oxygen level variable. This model may appear to be

simpler

when

compared

to

other

approaches

pre¬

sented in the literature. But, this model is used for the emission factors mod¬

elling. Usually, vehicles studied for this purpose are available for a short time and only a limited number of tests are possible. Moreover, the tests are per¬ formed on chassis dynamometers and not on engine test benches (which would allow more detailed measurements). Therefore, the goal is not to have a good accuracy on an instantaneous basis, but to have a simple and efficient way of predicting the cumulated emissions over a transient cycle. In fact, the model has only five parameters to be determined (kinetic parameters and OSC) and conversion curves to be estimated. It will be shown that the tuning of these parameters is very simple and can be easily performed. Moreover, the very transparent structure of the model makes extensions or simplifications easy to implement.

5.2.2. Parameter estimation

The present model introduces

reference to vides

an

a

set

of

of parameters that has to be estimated with

set

experimental

estimation for each

computation, using

a

one

data. For each

experiment, modelling

of the measured outputs

pro¬

(coXid-> CRed). The

the model, of each output

model parameters. The

tuning

of the model

depends on the values of the requires that the tunable parame¬

ters be fitted in order to minimize the error between available measurements

and the viewed

respective as an

simulations.

Hence, the problem of model tuning

can

be

optimization problem.

87

5.

Dynamic catalyst model

For the oxygen storage

fied

submodel, the following parameters have

to

be identi¬

by tuning:



the activation •

pre-exponential factors Ai incorporated in the reaction rates.

Kinetic parameters: this includes the two

energies Ei

that

are

Oxygen storage capacity: the storage capacity of the catalyst determined simultaneously with the kinetic parameters.

and

has to be

performed on the chassis dynamometer with the BMW vehicle (see Table 4.1). The already measured driving cycle R3 (see Section 1.3), which covers a large area of different operating points, was The model

was

tuned to measurements

used to fit the model

The

parameters.

goodness-of-fit of the model was chosen to be the sum of squares of the sampled error between measured and computed concentrations of oxidizing and reducing species (coxid and CRed) at the catalyst outlet. For the optimization procedure, the nonlinear least-squares error algorithm "lsqnonlin" from the Matlab Optimization Toolbox [55] has been employed. performance

The results of the

simulated

vs.

which

measure

tuning

assesses

the

for the studied vehicle

are

presented

in the form of

measured coxid and CRed concentrations at the outlet of the

con¬

verter. The accuracy of the model in

predicting catalyst-out aggregated species presented Figure cycle part from 300s to 400s. The prediction of the model is remarkably good. This successful prediction indicates that the oxygen storage dynamics implemented in the model are capable of modelling the phenomenon with good accuracy. is

in

5.2 for the

5.2.3. Static conversion

curves

The behaviour of the TWC is characterised

by

the conversion efficiencies of

HC, CO and NOx. Some of the sixteen transient driving patterns have been

employed

as

experimental

data necessary to

identify

the conversion

curves as

function of the estimated relative oxygen level. The reason for using more than one transient cycle was to achieve enough points covering all possible ranges of the ROL.

Consider, for example, the instantaneous emission profile during the CADC part 1, which corresponds to an urban driving pattern (Figure 5.3). Obviously,

88

5.2.

Methodology

390

400

400

500

310

320

330

~i

500

310

340

i—

r

320

330

350

340

350

time

Figure 5.2.: ing species.

Measured

vs.

360

370

i

360

380

i

370

380

390

400

r

390

400

[s]

computed concentrations

of the

oxidising

and reduc¬

89

5.

Dynamic catalyst model >

10 —i

r~

1

~l

1

1-

5000



E Q.

11 il 0

..i.

100

..lit.

200

I..

300

I

400

....

500

Li

m

600

it

700



A

800

900

1000 200

E a.

10000

&>

100

Juukiyiluii.-i..

t..

..

J.1

J lJ

o

5000

Figure 5.3.: Measured instantaneous NOx, HC inlet (dashed lines )and outlet (solid lines) over urban

the

and CO emissions at converter the 900 seconds of the CADC,

driving pattern.

significant overall conversion efficiency. The be, therefore, capable of matching the catalyst's breakthrough

specific catalyst

model should

attains

a

during accelerations, decelerations In order to be consisted with this

kinetic parameters and OSC

or

fuel cutoff situations.

catalyst's behaviour,

were

first the

already identified

used to generate the relative oxygen level

during the experimental data. Further on, conversion efficiencies of HC, CO and NOx were determined, but only when the values of the inlet concentrations were

above of

a

certain threshold.

NOx, for example, this threshold was set at 500 ppm. If the values of inlet NOx emissions are below this threshold it makes not a big difference for the For

cumulated emission value if the conversion range. At

points approximation.

were not

below this

a

constant

is set at 99%

conversion rate is

considered in the identification process of the conversion

efficiencies

were

or

an

at 50%

adequate

For this reason, the emissions below these threshold values

Once the events with

90

threshold,

efficiency

high

inlet concentrations

determined

on an

were

curves.

identified, the conversion

instantaneous basis. However,

we

have to

5.2.

-i

1

1

Methodology

i

r

~i~

measurements fitted function



.E

O

y 1

0.5-

o o

-ifrAiWtfc^-^1^» 01

0 2

0.3

0.4

atBa^èHËBBcate^iiWCTrw^iaiiiîifTOtiii

0.5

ROL

0.6

0.7

0.8

iliiiii

i,

-.

0.!

[-]

measurements

fitted function

ï

0.5

O

*!*»¥***£.