Trachet, Madireddy_2009

WETENSCHAPSPARK 5
B 3590 DIEPENBEEK
T ► 011 26 91 12
F ► 011 26 91 99
E ► info@steunpuntmowverkeersveiligheid.be
I ► www.steunpuntmowverkeersveiligheid.be
PROMOTOR ► Prof. dr. ir Dick Botteldooren, ir. Ina De Vlieger
ONDERZOEKSLIJN ► Duurzame mobiliteit
ONDERZOEKSGROEP ► VITO, UGent, UHasselt, VUB, PHL
RAPPORTNUMMER ► RA-MOW-2010-001
Steunpunt Mobiliteit & Openbare Werken
S p o o r V e r k e e r s v e i l i g h e i d
The influence of traffic management on
emissions
Literature study of existing emission models and initial
tests with micro traffic simulation
B. Trachet, M. Madireddy

DIEPENBEEK, 2010.
STEUNPUNT MOBILITEIT & OPENBARE WERKEN
SPOOR VERKEERSVEILIGHEID
The influence of traffic management on emissions
Literature study of existing emission models and initial tests with
micro traffic simulation
RA-MOW-2010-001
B. Trachet, M. Madireddy
Onderzoekslijn: Duurzame mobiliteit

Documentbeschrijving
Rapportnummer: RA-MOW-2010-001
Titel: The influence of traffic management on emissions
Ondertitel: Literature study of existing emission models and initial
tests with micro traffic simulation
Auteur(s): B. Trachet, M. Madireddy
Promotor: Prof. dr. ir. Dick Botteldooren, ir. Ina De Vlieger
Onderzoekslijn: Duurzame Mobiliteit
Partner: VITO en Universiteit Gent
Aantal pagina’s: 64
Projectnummer Steunpunt: 8.3
Projectinhoud: Verkeersmanagement en milieu
Uitgave: Steunpunt Mobiliteit & Openbare Werken – Spoor Verkeersveiligheid, maart
2010.
Steunpunt Mobiliteit & Openbare Werken
Spoor Verkeersveiligheid
Wetenschapspark 5
B 3590 Diepenbeek
T 011 26 91 12
F 011 26 91 99
E info@steunpuntmowverkeersveiligheid.be
I www.steunpuntmowverkeersveiligheid.be

Steunpunt Mobiliteit & Openbare Werken 3 RA-MOW-2010-001
Samenvatting
Het doel van werkpakket 8.3 is onderzoek te verrichten naar de mogelijke verbetering in
de uitstoot van uitlaatgassen en geluidsemissies door een gepast verkeersmanagement.
Dit zullen we verwezenlijken door een emissie- en geluidsmodel, als twee externe plug-
in’s in de verkeerssimulatie software Paramics te implementeren.
Dit rapport bevat een literatuurstudie op basis waarvan we het best beschikbare
emissiemodel voor dit doel zullen kiezen. De geschiktheid van de modellen hebben we
gescreend aan de hand van enkele voorwaarden waaraan het moet voldoen. Het model
moet kunnen rekening houden met de Vlaamse verkeerssituatie. Het moet voorspellingen
maken voor verschillende polluenten, waaronder ook fijn stof (PM2.5). Bovendien moet
het extern gevalideerd zijn. Enkele bijkomende karakteristieken zijn goed meegenomen:
gebruiksvriendelijkheid, opnemen van koude start emissies, uitgebreide keuze aan
voertuigencategorie, brandstoftype en euroklasse. Het beste model moet hier
geïnterpreteerd worden als het model dat de werkelijke emissies het best benadert.
Eerst hebben we verscheidende emissiemodellen onderzocht:
Amerikaanse emissiemodellen.
De meeste Amerikaanse modellen gebruiken een statistische aanpak door
regressiecurven te fitten op emissiemetingen met als variabelen de ogenblikkelijke
snelheid en versnelling. De betrouwbaarheid van deze modellen hangt voor een groot
stuk af van de gebruikte databank. Amerikaanse voertuigen verschillen echter in veel
opzichten van de voertuigen in de Europese vloot en bovendien is de gebruikte databank
in de Amerikaanse modellen niet aangevuld met de laatste technologieën.
Europese emissiemodellen.
4 Europese modellen bleken uit de literatuurstudie de beste kans te maken: VETESS,
EMPA, PHEM en Versit+. De eerste 3 modellen slaan emissiewaarden op in een matrix
gebaseerd op motorparameters zoals toerental, koppel en vermogen. Zo zijn de modellen
beter in staat om weer te geven wat er in de motor gebeurt. Deze modellen passen ook
een correctie toe voor transiënte motortoestanden, die een grote invloed hebben op de
emissie van uitlaatgassen van wagens uitgerust met uitlaatgasbehandelingssystemen
zoals een 3-weg katalysator (niet voor CO2 en geluid). Van deze drie vermelde modellen
heeft EMPA het nadeel dat het geen fijn stof emissies voorspelt. Fijn stof is van groot
belang in de politieke besluitvorming naar verkeersmanagement toe en is dus
onontbeerlijk voor onze doeleinden. VETESS is slechts gebaseerd op emissiemetingen op
3 voertuigen. Het is moeilijk om op basis van drie voertuigen de emissies van de
volledige vloot te voorspellen. Dit komt ook tot uiting in minder goede validatieresultaten
wanneer de voorspellingen van VETESS vergeleken worden met emissiemetingen op
voertuigen die niet gebruikt werden om het model op te stellen. PHEM tenslotte is
gebaseerd op metingen op 32 voertuigen, waarin alle euroklassen voor zowel diesel als
benzine wagens vertegenwoordigd zijn. Ook is een PHEM vrachtwagenmodel beschikbaar
en zijn PM-metingen opgenomen in het model. PHEM is echter weinig gebruiksvriendelijk
en niet beschikbaar binnen dit project (in tegenstelling tot VETESS) en zou dus extern
aangekocht moeten worden.
Het vierde veelbelovende Europese model (Nederland) is Versit+. Het is een statistisch
model dat steunt op een databank met 3 200 voertuigen met testen uitgevoerd op 80
voertuigtypes volgens euroklasse, brandstoftype, injectietype, wissel van versnelling,
gewicht en deeltjesfilter. De input voor Versit+ is de ogenblikkelijke plaats van elk
voertuig in het netwerk. De producent levert hierbij zelf een micro verkeersmodel dat de
input voor Versit+ kan aanleveren. Versit+ is gebruiksvriendelijk en steunt op een zeer
nauwkeurige databank. We hebben Versit+ in 2009 kunnen testen. De vergelijking met
VITO-metingen was heel bevredigend. Verder is het model commercieel beschikbaar.
Hierdoor is Versit+ een ideaal model als externe plug-in voor het verkeersmodel.

Tenslotte zijn ook modellen voor geluidsemissies onderzocht. Het Harmonoise/ Imagine
model dat als Europese standaard voorgesteld wordt, is een evidente keuze.
Bruikbaarheid van emissiemodellen.
In een tweede deel van het rapport wordt de voorgestelde aanpak - de implementatie
van een emissiemodel als plug-in in verkeerssimulatie software - getest in enkele
gevalstudies (gebeurd in 2007). Voor deze gevalstudies werden twee trajecten in Gent-
Brugge afgelegd met een testvoertuig waarbij de snelheid en GPS locatie per seconde
werd opgeslagen. Deze data dienen als input voor de verschillende gevalstudies. In een
eerste gevalstudie wordt getest wat het voordeel van nieuwere, meer ontwikkelde
emissiemodellen is. Uitlaatgassen op het traject in Gent-Brugge voorspeld door VETESS
worden vergeleken met de voorspellingen van Mobilee. Mobilee is een statistisch model
dat voertuigsnelheid en -versnelling als variabelen gebruikt. Mobilee werd gekozen voor
deze gevalstudie omdat het makkelijk te implementeren is in Paramics. Het kan dus
dienen om de toepasbaarheid van de voorgestelde aanpak te testen in case studies.
Beide modellen geven gelijkaardige resultaten voor CO2, met een correlatie > 0.9 voor
zowel diesel als benzine voertuigen en een gemiddeld verschil van maximum 8 %. Voor
andere emissies werden echter grote verschillen tussen VETESS en Mobilee genoteerd.
Om deze reden werden enkel CO2 voorspellingen van Mobilee en geluidsvoorspellingen
op basis van Harmonoise gebruikt in de volgende gevalstudies.
Nauwkeurigheid van het micro verkeersmodel.
De tweede studie heeft als doel de accuraatheid van micro verkeerssimulatie (Paramics)
te testen. Daartoe worden de emissie voorspellingen van Mobilee (CO2) en Harmonoise
(geluid) gebaseerd op de snelheidsmetingen op de cycli in Gent-Brugge vergeleken met
de emissievoorspellingen van beide modellen gebaseerd op de verkeersdata
geproduceerd door een Paramics model van de verkeerssituatie in Gent-Brugge. Op elke
tijdstap zijn er in Paramics een ander aantal voertuigen gesimuleerd. Als Paramics
accuraat is, moeten de voorspellingen van de emissies op basis van de metingen in elke
situatie tussen de minimale en maximale emissie op die plaats in Paramics liggen. Dit is
altijd het geval, behalve na een kruispunt of bocht in het circuit. Dit laatste betekent dat
voertuigen in Paramics te snel accelereren in vergelijking met de werkelijkheid, en dus
aanleiding geven tot een emissiepiek die niet lang genoeg duurt. Dit kan echter
aangepast worden als een instelling in Paramics zelf, en wijst dus niet op een structurele
fout in het model.
Gevoeligheidsanalyse.
In een derde gevalstudie wordt de gevoeligheid van Harmonoise en VETESS getest.
Hiertoe worden emissievoorspellingen van CO2 en geluid vergeleken voor agressief en
normaal rijgedrag op de cyclus in Gent-Brugge. Agressief rijgedrag geeft een stijging in
gemiddelde geluidsemissie per seconde van 91.7 dBA (kalm rijgedrag) naar 94.3 dBA op
de normale verkeerscyclus en van 91.9 dBA (kalm rijgedrag) tot 93.6 dBA op de
verkeerscyclus met sluipverkeer. De gemiddelde CO2 emissie per seconde stijgt van 1.37
gram tot 2.81 gram voor lokaal verkeer en van 1.66 gram tot 2.60 gram voor het traject
sluipverkeer. De toename in geluidsemissies door agressief rijgedrag is vooral merkbaar
op plaatsen met een recht stuk weg door de hogere snelheden die gehaald worden. De
toename in CO2 emissies uit Mobilee door agressief rijgedrag is vooral merkbaar aan
kruispunten en bochten, door de hogere versnelling die gehaald wordt.
In het algemeen laat deze literatuurstudie toe te concluderen dat er voldoende accurate
emissiemodellen (vooral Versit+) te vinden zijn om het effect van verkeersmanagement
op de globale emissie te begroten. Voor CO2 en geluid hebben we bovendien aangetoond
dat micro verkeerssimulatie gebruikt kan worden om het effect van verkeersmanagement
te kwantificeren vooraleer de maatregelen te implementeren.

English summary
Title: The influence of traffic management on emissions
Subtitle: Literature study of existing emission models and initial tests with micro traffic
simulations
Abstract
Objectives and Goals of the Project.
The mission of work package 8.3 is to investigate the influence of traffic management on
the reduction of exhaust emission and noise from road vehicle streams. This will be done
by implementing a emission model and noise model as two external plug-ins into the
traffic simulation software Paramics.
This report contains a literature study that will help to determine the best available
emission model for this purpose. Since there are several models available, some required
traits are used to screen the models. The model should successfully represent Flemish
traffic; it should be able to predict all the pollutants including PM and the accuracy of the
model should be externally validated. While these are necessary, some of the additional
characteristics were desirable. These include user-friendliness, accounting for cold-start
emissions, transient correction, presence of categories for different vehicle classes, fuel
types and Euro class types. The best model should be interpreted as the model that
predicts emissions closest to the real values.
For this purpose firstly, several pollutant emission models were investigated.
American Emission Models.
Most American models use a statistical approach, fitting regression curves on emission
measurements with instantaneous speed and acceleration. The reliability of the resulting
models depends to a great extent on the used database. However American vehicles are
different from those in the European fleet, and the database used in most models was
also not up-to-date with the latest technologies.
European Emission Models.
4 European models come forth from the literature study as possible option: VETESS,
EMPA, PHEM and Versit+. The first three of these models map emission values in a
matrix based on engine parameters such as engine speed, torque and power. This way
the models can better reproduce what happens inside the engine. These models also
apply a correction for transient behavior, which has a great influence on the emission
production in newer vehicles with after treatment systems. Of these three models, EMPA
has a disadvantage because it doesn’t include PM emissions and it is not very user-
friendly. VETESS is only based on 3 vehicles and is thus not very representative for the
entire fleet. This is also a problem when the model is validated against measurements of
vehicles not included in the model. PHEM represents all Euro classes and also includes a
truck model and it can model PM as well. However this model is not available in house
(opposite to VETESS) and thus it should be purchased.
The fourth model, Versit+, is a statistical model based on a large database of about 3200
vehicles with tests conducted on 80 vehicle categories based on Euro class, fuel type,
injection type, gear shift method, weight and DPF. The input to Versit+ is the
instantaneous place of each vehicle on the network. The producer provides a micro traffic
model that can supply input to Versit+. Versit+ is very easy to use and is based on a
very reliable database. VITO had the opportunity to test Versit+ in 2009. The results
matched with VITO’s measuring results VITO. Furthermore, the model is commercial
available. Hence, Versit+ becomes an ideal model for an external plug-in into the traffic
model.
Noise emission models have been investigated as well. Here the Harmonoise/ Imagine
model is an obvious choice since it is the proposed European standard model.

Usability of Emission Models.
In a second part of the report the proposed approach of implementing an emission model
in the traffic simulation software is tested with some case studies (executed in 2007). For
these case studies two trajectories in Gent-Brugge have been driven with a test vehicle
that registered the speed and GPS location every second. These data are then used as
input for the different case studies. In a first case study the benefit of the newer
emission models is tested. Exhaust emissions on the Gent-Brugge trajectory predicted by
VETESS are compared with those from Mobilee, a dated statistical model that is only
based on speed and acceleration. Mobilee is used because it is easily implemented in
Paramics and can thus serve to investigate the approach in the presented case studies.
Both models give similar results for CO2, with a correlation > 0.9 for both diesel and
petrol vehicles, and an average difference of maximum 8%. For all emissions except CO2
however, large differences can be noted. Therefore only CO2 and noise emissions will be
compared in the next case studies.
Accuracy of the Micro Traffic Model.
The second study is testing the accuracy of using a micro traffic simulation (Paramics).
For this purpose Mobilee (CO2) and Harmonoise (noise) emission predictions based on
the speed measurements in Gent-Brugge are compared with the emission predictions
based on the traffic data from a Paramics model of the Gent-Brugge area. In every time
step the simulation includes a different amount of vehicles. If Paramics is accurate, the
predictions based on the measurements should in every case lie between the maximum
and the minimum emitting vehicle in Paramics. This is always the case, except after
some crossings or corners. This implies that vehicles in Paramics might accelerate too
fast compared to the measured vehicle, giving rise to a shorter duration of the emission
peak. This can however be changed easily as a setting in Paramics.
Sensitivity Studies.
In a third case study the sensitivities of Harmonoise and VETESS are investigated.
Aggressive driving results in an increase in average noise emission per second from 91.7
dBA (calm driving) to 94.3 dBA on the normal traffic cycle, and from 91.9 dBA(calm
driving) to 93.6 dBA on the sneak traffic cycle. The average CO2 emission per second
increases from 1.37 gram to 2.81 gram for the normal traffic cycle and from 1.66 gram
to 2.60 gram for the sneak traffic cycle. Noise emissions due to aggressive driving rise at
places with a straight road due to higher speeds. CO2 emissions from Mobilee due to
aggressive driving mostly increase after traffic lights or road turns, due to higher
acceleration. This indicates that the emission models are sensitive enough to study the
influence of traffic management.
In general, this literature study proves that suitable emission models (especially,
Versit+) are available for studying the effect of traffic management. For CO2 and noise,
it was shown that micro traffic simulation can be used to study the effect of this traffic
management prior to implementing.

Table of contents
1. LIST OF ABBREVIATIONS ............................................................9
2. INTRODUCTION ......................................................................11
3. SOURCE MECHANISMS EMISSIONS ...............................................14
3.1 Origin of emissions 14
3.1.1 General .................................................................................... 14
3.1.2 CO2 .......................................................................................... 14
3.1.3 CO ........................................................................................... 14
3.1.4 HC ........................................................................................... 14
3.1.5 NOx .......................................................................................... 15
3.1.6 PM ........................................................................................... 15
3.1.7 Fuel related pollutants SO2 and Pb................................................ 15
3.1.8 Noise........................................................................................ 15
3.2 After treatment of exhaust emissions. 16
3.2.1 The 3 way-catalyst ..................................................................... 16
3.2.2 Diesel Particulate Filter (DPF)....................................................... 17
3.2.3 Exhaust muffler ......................................................................... 17
3.3 Parameters that influence emissions. 18
3.4 Composition of the fleet – high and low emitters 19
3.4.1 General .................................................................................... 19
3.4.2 Heavy Duty traffic ...................................................................... 19
3.4.3 Automatic transmission............................................................... 19
3.4.4 Hybrid vehicles .......................................................................... 20
3.5 Political importance of different emissions 20
3.5.1 CO2 .......................................................................................... 20
3.5.2 PM ........................................................................................... 21
3.5.3 Noise........................................................................................ 21
3.5.4 NOx .......................................................................................... 21
3.5.5 Summary.................................................................................. 21
4. LITERATURE STUDY: EMISSION MODELS........................................23
4.1 Instantaneous exhaust emission models 23
4.1.1 Introduction .............................................................................. 23
4.1.2 VETESS .................................................................................... 23
4.1.3 Mobilee..................................................................................... 25
4.1.4 MODEM (Joumard et al, 1995) ..................................................... 25
4.1.5 CMEM (Barth et al, 2000) ............................................................ 26
4.1.6 EMIT (Cappiello et al, 2002) ........................................................ 28

4.1.7 VT-Micro (Rakha et al, 2004a) ..................................................... 28
4.1.8 Poly (Qi et al, 2004) ................................................................... 30
4.1.9 EMPA (Ajtay and Weilenmann, 2004b) .......................................... 31
4.1.10 PHEM (Zallinger et al, 2005) ........................................................ 33
4.1.11 Versit+ (Smit et al, 2006) ........................................................... 35
3.1.12. Overview of the different models ................................................. 37
4.2 Larger scale exhaust emission models 41
4.2.1 Introduction .............................................................................. 41
4.2.2 MOVES (Koupal et al, 2005) ........................................................ 41
4.2.3 COPERT (EEA, 2009) .................................................................. 42
4.3 Instantaneous noise emission models 43
4.3.1 Introduction .............................................................................. 43
4.3.2 Nord 2000 (Kragh et al, 2001) ..................................................... 43
4.3.3 Harmonoise and Imagine (Peeters and van Blokland, 2007) ............. 44
5. CASE STUDIES........................................................................45
5.1 Introduction 45
5.1.1 Background............................................................................... 45
5.1.2 Methodology.............................................................................. 46
5.2 Case study 1a: Performance of exhaust emission models 46
5.2.1 Methodology.............................................................................. 46
5.2.2 Results ..................................................................................... 47
5.2.3 Conclusions............................................................................... 49
4.3 Case study 1b: Performance of VERSIT+ on standard drive cycle using VOEM 49
5.4 Case study 2: Accuracy of Paramics 53
5.4.1 Methodology.............................................................................. 53
5.4.2 Results ..................................................................................... 54
5.4.3 Conclusions............................................................................... 56
5.5 Case study 3: Sensitivity of the models 57
5.5.1 Methodology.............................................................................. 57
5.5.2 Results ..................................................................................... 57
5.5.3 Conclusions: Model Sensitivity ..................................................... 59
6. CONCLUSIONS........................................................................60
7. FURTHER RESEARCH ................................................................61
8. LITERATURE LIST ....................................................................62

1. LI S T O F AB B R E V I A T I O N S
CO Carbon Monoxide
CO2 Carbon Dioxide
COHb CarboxyHeamoglobin
CMEM Comprehensive Modal Emissions Model
COPERT Computer Program to calculate Emissions from Road Traffic
DPF Diesel Particulate Filter
dBA Decibels
EMIT EMIssions from Traffic
EMPA The Swiss Federal Laboratories for Materials Testing and Research
ENVIVER ENvironmentVIssimVERsit
FTP Federal Test Procedure
GPS Global Positioning System
HC HydroCarbons
HD Heavy Duty
LD Light Duty
MODEM Modal Emissions Model
MOVES MOtor Vehicle Emission Simulator
N2 Nitrogen
NEDC New European Driving Cycle
N20 Nitrous Oxide (Laughing Gas)
NOx Nitrogen Oxides
PM Particulate Matter
PHEM Passenger Cars and Heavy-Duty Emissions Model
RMS Root Mean Square
SO2 Sulphur Dioxide

THC Total Hydrocarbons
UFP Ultra Fine Particles
VEDETT Vehicle Embedded Device for data-acquisition Enabling Tracking and
Tracing
VETESS Vehicle Transient Emissions Simulation Software
VITO Flemish Institute for Technological Research
VOEM Vito’s On-the-road Emission and Energy Measurement
VSP Vehicle Specific Power
VT-Micro Virginia Tech Micro

2. IN T R O D U C T I O N
The mission of work package 8.3 is to investigate possible reduction in emission and
noise production by road vehicle streams by suitable traffic management and planning.
Conclusions drawn in this work package will be based on field measurements using the
VEDETT1
measurement system and on microscopic traffic simulations, using models such
as Paramics. Both yield driving data (instantaneous speed and acceleration) on an
individual vehicle level, but their typical use will be different. While simulation allows
investigating a broad range of existing and future situations and to study in detail
parameter dependence, VEDETT measurements have the huge advantage that they are
very close to reality. Whatever model is used to obtain traffic parameters, a crucial point
in the whole process of studying environmental sustainability is detailed modeling of
dynamic emissions (CO2, CO, PM, NOx, HC, and noise) as is shown in Figure 1.
Figure 1: schedule for work package 8.3
1
VEDETT stands for Vehicle Embedded Device for data-acquisition Enabling Tracking and
Tracing. This technology allows to simultaneously record position, speed, acceleration,
fuel consumption etc. on vehicles in normal traffic.

The applicability of this approach and the validity of the results depend on the accuracy -
the extent to which these functions correctly represent the real-life situation – of the
dynamic emission models that will be used. Therefore it is important to make use of the
most up-to-date emission functions available. It makes sense to look beyond the models
currently in use by the partners in order to assure that the forthcoming efforts are not
undermined by shortcomings in these models.
This report contains a literature study that analyses and compares the available dynamic
emission functions with the objective of choosing the best possible emission model. Some
of the data used for validation of models is obtained from Vito’s On-the-road Emission
and Energy Measurement (VOEM) System. These measurements were obtained by
mounting the measurement system on the vehicle and driving a predetermined route
that is representative of standardized drive cycle or a real time drive. VOEM measures
accurate, dynamic and mass based emission levels.
To compare the available models, a list of required and desired traits was compiled. The
necessary traits for the model are:
• The model should be able to produce emission data on a second by second basis
depending on the instantaneously and recent driving parameters of the vehicle.
• The minimal set of emission parameters has to include CO2, NOx, and PM. The
model for noise should produce output per octave band.
• The accuracy of the model should be externally validated.
• The model should be representative for the fleet composition on Flemish roads, in
particular with regard to the dependence of emissions (gaseous and noise) on
speed and acceleration.
While these are necessary, some of the traits are desirable. These can be:
• Accounting for cold-start emissions.
• Transient correction on gaseous emissions.
• Presence of categories for different vehicle classes, fuel types and Euro class
types.
• Adaptability of the model to incorporate new technologies such as after-treatment
systems and fuel-blends.
• Possibility to correct for road surface and tires in the noise model.
• User-friendliness.
During the coming years this work package will use the chosen emission curves
embedded in a micro-simulation model for the traffic. A reliable microscopic traffic model
can then be used to test traffic management-related decisions on their environmental
impact. The success of this proposed approach depends on two things. Firstly, the
microscopic traffic simulation should be accurate enough to model the dynamics of the
traffic flow. Secondly, the approach should be sensitive enough to reveal the differences
in emission that we are looking for. In this report a number of case studies are included

to test these two aspects. Accuracy is assessed by comparing CO2 emission2
and noise
emission from Paramics traffic simulations with those obtained from real car drive cycles
in the study area. Sensitivity is studied by comparing calm and aggressive driving in
urban context and by comparing driving over a bridge.
2
CO2 emission is chosen since available emission models are far more accurate for CO2
than for PM, HC or NOx.

3. SO U R C E M E C H A N I S M S E M I S S I O N S
3.1 Origin of emissions
3.1.1 General
The transportation sector is to a large extent responsible for the air pollution to which
humans are exposed. The ideal combustion process in a gasoline car can be found in
Equation 1:
heatOHCOOgasoline ++→+ 222 +noise
Equation 1: Complete combustion process
Due to imperfections in the combustion process undesired components can be set free in
the course of events that then end up in the atmosphere as exhaust gases or emissions.
The causes and consequences of the most important emissions are discussed in this
section. An elaborate description was provided by Pundir (2007).
3.1.2 CO2
CO2 is a product of the combustion process and is thus inevitable. All the carbon in the
fuel is eventually turned into either CO or CO2. Particulate matter (PM) accounts for some
of the carbon molecules, but if the car is equipped with a diesel particulate filter (see
below) they eventually get burned to CO2 as well. The total amount of CO2 – exhaust is
thus proportionate (99% of carbon is emitted as CO2) to the fuel consumption. CO2
mostly effects on global warming and thus causes sustainability issues that arise mainly
on macro-scale.
3.1.3 CO
CO is a colorless, odorless, and tasteless gas that is a by-product of incomplete
combustion, especially in gasoline vehicles. It arises mostly in ‘rich’ combustion
processes, when there is no sufficient oxygen to turn all carbon into CO2. This can occur
during a cold start or when driving at great heights for example. Also transient situations
such as accelerations or high speeds can give rise to extra CO. The CO from gasoline
vehicles is reduced by the use of a 3 way-catalyst (see 2.2). The formation of emissions
in gasoline engine was described in detail by Bosch (2006).
CO is harmful for the human health because it binds with hemoglobin to form Carboxy
Heamoglobin (COHb) and enters the lungs after inhalation. This reduces the oxygen
transfer in the blood and can lead to headache, dizziness and even death. The most
dangerous locations are situated in badly ventilated spaces such as tunnels and parking
lots.
3.1.4 HC
HC arises, similar to CO, due to a lack of oxygen in a rich mixture. Mazzoleni et al (2004)
could however not find a significant correlation between HC and CO emission factors. The
causes for HC-formation are a.o. evaporation, hole-filling with unburned fuel and
quenching3
. Absolute HC-values are very low, and thus hard to measure. The HC-exhaust
of gasoline vehicles is reduced in a 3 way-catalyst (see 2.2).
3
Quenching: extinguishing of the flame near the cold cylinder wall.

There is large variability in HC-compounds such as parafines, olecines, acetylenes and
aromatic compounds. Each of these HC-compounds has another effect on health.
Benzene for example can induce cancer and lead to leukemia. On macro-scale there is a
danger for a bond between HC and NO2 in the troposphere with the formation of ozone as
result. Too high ozone values in the air can in turn lead to coughing, chest pain and
headaches. Ozone is also harmful for the vegetation-life.
3.1.5 NOx
NOx is the common name for NO and NO2 . Nitogen oxides arise when oxygen reacts with
N2 in the air due to the high combustion temperature (T>1500°C). This happens
especially when there is a surplus of air in the mixture (poor mixture).
NO is colorless, odorless, tasteless and relatively harmless for humans. It is however
oxidized to NO2 in the atmosphere. NO2 is red-brownish of color, extremely poisonous and
has a distinct odor. It causes damage in the lung tissue with coughing and bronchitis as a
result. It is also harmful on macro-scale due to the formation of ozone when binding with
HC. NO2 also takes in radiation and can at high concentrations have an effect on global
warming. NO2 also causes acid rain.
3.1.6 PM
PM (Particulate matter) is the common name for all organic and inorganic parts that are
set free during combustion. PM particles can be divided into PM10 and PM2.5, where the
latter have a diameter of 2.5 micrometer and are believed to be more harmful. PM-
exhaust is significant for diesel engines, with values that rise several times higher than
those for gasoline vehicles with 3 way-catalysts. In most emission models only PM-
exhaust for diesel vehicles is modeled. Soot parts arise in the middle of an injection cloud
at high temperature and pressure due to pyrolysis of the fuel. PM-exhaust for diesel
vehicles is thus mostly organic and consists of small dust parts that form chains in the
exhaust pipe. When sufficient oxygen is available the soot can burn again and be turned
into CO2. There is a trade-off with the formation of NOx : an oxygen surplus leads to the
formation of more NOx, but in the same time more PM will disappear due to formation of
CO2.
Most harmful for the human health are PM2.5 particles: parts with a diameter smaller than
2.5 micrometer. These parts enter deep into the long tissue and can cause a lot of
damage there. PM2.5 is the most important exhaust emission regarding health issues on a
local scale.
3.1.7 Fuel related pollutants SO2 and Pb
Fuel related pollutants have been reduced considerably in Europe over the past decades.
Hence SO2 and Pb emissions no longer contribute significantly to environmental pollution
and will not be considered further in this work.
3.1.8 Noise
Noise emissions are subdivided into 2 large categories: rolling noise and engine noise.
Rolling noise comes from the tires and mostly depends on velocity and acceleration.
Rolling noise levels further depend on the type of tire and on the road surface. Usually
80% of the rolling noise sound power is modeled as a point source at a height of 0.01m
above the road surface, and 20% is modeled as a point source at 0.30m (passenger
cars) or 0.75m (heavy vehicles) above the road surface; the opposite is true for the
engine noise. Engine noise comes from the exhaust and the engine cap and depends on
speed and acceleration. Modern diesel cars on average produce less than one dBA more
noise than equivalent gasoline cars. Engine noise is modeled as a single source at a
height of 30 cm.
Noise pollution due to traffic has negative effects on the health. Berglund et al (2000)
mentioned several adverse health effects for noise. Noise can damage the hearing

system. Noise interference with speech comprehension can result in problems with
concentration, misunderstandings, etc. Another aspect is sleep disturbance. The primary
sleep disturbance effects are: difficulty in falling asleep (increased sleep latency time);
awakenings; and alterations of sleep stages or depth, especially a reduction in the
proportion of REM-sleep (REM = rapid eye movement). Exposure to night-time noise also
induces secondary effects, or so-called after effects. These are effects that can be
measured during the day following the night-time exposure, while the individual is
awake. The secondary effects include reduced perceived sleep quality; increased fatigue;
depressed mood or well-being; and decreased performance. Finally acute noise
exposures activate the autonomic and hormonal systems, leading to temporary changes
such as increased blood pressure, increased heart rate and vasoconstriction. After
prolonged exposure, susceptible individuals in the general population may develop
permanent effects, such as hypertension and ischemic heart disease associated with
exposures to high sound pressure levels. The magnitude and duration of the effects are
determined in part by individual characteristics, lifestyle behaviors and environmental
conditions. Sounds also evoke reflex responses, particularly when they are unfamiliar and
have a sudden onset.
3.2 After treatment of exhaust emissions.
The after treatment methods that exhaust gases undergo play an important role in the
reduction of emissions. The most important ones are the 3 way-catalyst for gasoline
vehicles and the diesel particulate filter (DPF) for diesel vehicles.
3.2.1 The 3 way-catalyst
The catalytic conversion of emissions consists of 2 steps:
a. Reduction of NOx
This reaction occurs according to following equations:
222 ONNO +→
222 22 ONNO +→
Nitrate oxides are thus neutralized with the help of a platinum or rhodium catalyst
plate.
b. Oxidation of HC and CO
This reaction occurs according to following equations:
OyHxCOO
y
xHC
COOCO
yx 222
22
2)
2
2(2
22
+→++
→+
Here HC and CO are combusted using the oxygen that is produced in the first step.
The 3 way-catalyst gets his name from the fact that 3 pollutants are diminished.
There are however some specifications that need to be fulfilled for a good working
catalyst:
• A temperature of at least 350°C is necessary, so at cold starts the catalyst doesn’t
work as it should, causing additional emissions.

• Since the chemical nature demands that the easiest reaction should happen first,
too much oxygen always leads to a skipping of the reduction step, and thus an
uninterrupted NOx-exhaust. Therefore it is important that the engine is as close as
possible to its stoichiometric balance (air-fuel proportion of 14.7 or @=1). A
surplus of oxygen has to be avoided to maintain the reduction of NOx. In gasoline
engines the stoichiometric balance is realized by sensors with electronic feedback.
• In diesel engines compression is used instead of ignition, and there is always a
surplus of oxygen. The temperature in a diesel engine is often too low for
satisfactory catalyst performance. Moreover diesel engines generally work under
lean burn conditions (excess of O2). Therefore a 3 way-catalyst cannot be used in
diesel engines. An alternative is a 2way-catalyst, which only oxidizes HC and CO.
The reduction of NOx is in some cases achieved by adding urea.
3.2.2 Diesel Particulate Filter (DPF)
A DPF is placed at the exhaust of diesel engines to reduce the PM-exhaust. The filter is
made of a honeycomb-structure in ceramic material that catches the PM parts (see
Figure 2).
Figure 2: DPF honeycomb structure
The on-board computer sends extra fuel to the exhaust at distinct time intervals. This
extra fuel is then used to burn all the collected soot parts, and thus cleaning the filter. All
diesel cars answering to the Euro-5 norm (from 2009) will be obliged to have a DPF. The
diesel exhaust formation and the techniques to reduce NOx and PM were presented in
more detail by Bosch (2005).
3.2.3 Exhaust muffler
Exhaust mufflers block most of the noise traveling through the outlet system. Two kinds
of mufflers exist. The most common used is a reflection muffler. If plane sound waves
are propagating across a tube and the section of the tube changes at a point x, the
impedance of the tube will change. When the wave reaches a frontier between two
mediums which have different impedances, the speed, and the pressure amplitude
change (and so does the angle if the wave does not propagate perpendicularly to the
frontier). Using cavities with different diameter sound wave reflection is used to create a
maximum amount of destructive interferences. However, reflection mufflers often create

higher back pressure which can lower the performance of the engine at higher rpm's. The
higher the pressure behind the exhaust valves (i.e. back pressure), the higher is the
necessary effort to expel the gas out of the cylinder.
The alternative to reflection mufflers are absorption mufflers. Absorption muffler is
composed of a tube covered by a sound absorbing material such as fiberglass or steel
wool. The tube is perforated so that some part of the sound wave goes through the
perforation to the absorbing material. The advantages include a low back pressure and a
relatively simple design, at the cost of less efficient sound damping. They are often used
in sports vehicles due to their better engine performance.
European noise homologation mainly influences engine noise. Car manufacturers tend to
design the exhaust mufflers to meet homologation requirements as closely as possible.
Recently, the European tyre noise directive was made more restrictive. It can be
expected that this will affect rolling noise in near future.
3.3 Parameters that influence emissions.
The eventual goal of the investigation on dynamic emission curves is to test traffic
management decisions on their effect on emissions. To provide a better understanding of
the different emission models presented in the literature study a sound understanding of
the influence of the engine parameters on the produced emissions is indispensable. A
conclusion that comes forth from all investigations is that a simple relation between
speed or acceleration and emission is no longer sufficient to explain emission values.
Modern cars are equipped with very efficient techniques to reduce specific exhaust
emissions, such as the 3 way catalysts and DPF. Therefore these exhaust values are in
most situations very low, except for some peaks in transient engine situations that to a
great extent determine the eventual exhaust value. CO2 and noise are the only emissions
not affected by these transient situations.
Table 1 contains an overview of the parameters that influence the different emission
values. Knowledge of these parameters is indispensable for a good understanding of the
approach that is used in the different emission models.
When interpreting Table1 it has to be clear that the most important parameter for all
emissions except CO2 and noise is the last one: transient engine operations. The
prediction of the pollutants depends to a large extent on the rate of change of load.
Some of the emissions are generated by the change itself rather than as a function of a
series of steady states. These transient effects influence the emissions strongly,
especially for engines with 3 way catalyst. Any transient movement away from
stoichiometric fueling results in a magnified effect at the catalyst exit, and an emission
peak. Other controls like EGR (exhaust gas recirculation) valves or turbochargers and
their transient behavior can also have an important impact on emissions. Transient
effects can also occur for example when the load conditions of the engine change, during
fuel cutoff during overrun, engine misfire or mixture enrichment during acceleration
(Ajtay, 2005). The influence of these transient peaks on the total emission value is
significant. For example Pelkmans et al (2004) mention CO measurements in city traffic,
where 7 emission peaks in a cycle of 1600 seconds represent 70% of all CO emissions
over the cycle.

CO2 CO HC NOx PM Noise
O2 surplus + -
O2 deficit + +
High engine temp. + +
Low engine temp. (e.g. cold start) +
(3 way)
+
(3 way)
+
(3 way)
High load (more fuel consumption) + + + + +
Transient engine situation +
(3 way)
+
(3 way)
+
(3 way)
Table 1: Parameters that influence emissions
3.4 Composition of the fleet – high and low emitters
3.4.1 General
Except for bulk of diesel and gasoline vehicles, the fleet may contain different types of
vehicles. These vehicle types generally only account for a minor part of the total traffic,
but they can still have a significant influence because their emissions are higher (or
lower) than average. Some of these vehicle types are discussed here.
3.4.2 Heavy Duty traffic
The current investigation aims to provide an aid in testing measures regarding traffic
management on their impact on emissions. For city traffic the proportion of heavy duty
vehicles is lower than on the highway. Kirchstetter et al (1999) mention however that HD
vehicles emit 5 times more NOx and up to 20 times more PM2.5 emissions. Heavy duty
traffic also produces between 5 and 10 dB(A) more noise than cars depending on the
driving speed, acceleration, and road condition.
HD traffic can thus still have a significant influence on the overall emission rates.
Especially the presence of buses in city traffic must be taken into account. It is
dangerous to base decisions regarding traffic management on models that only take LD
vehicles into account. Knowledge of the proportion of LD-HD vehicles on the investigated
traffic situation will be required, as well as emission models for both categories. If only
LD emissions are taken into account, the user must be aware of the fact that the model
will not be applicable on locations where a lot of HD traffic is passing, such as national
highways.
3.4.3 Automatic transmission
An aspect that is not often highlighted in the emission discussion is the fact that vehicles
with automatic transmission generally use more fuel than those with manual
transmission. The main reason is in the lower mechanical efficiency. The manual
transmission couples the engine to the transmission with a rigid clutch instead of a
torque converter that introduces significant power losses. The automatic transmission
also suffers parasitic losses by driving the high pressure hydraulic pumps required for its
operation. Moreover the manual transmissions are lighter and have better gear

efficiency. Fuel consumption for cars with automatic transmission can rise with 5-10 %
(Kluger and Long, 1999). Homologation test for noise emission show that cars equipped
with automatic transmission produce 1 dBA lower sound levels, a difference that was
found statistically significant (based on 2007 brands and models). Both the noise
produced by the transmission and the different engine speed during the test may
contribute to this difference. The effect of automatic transmission is a more important
factor in the US, where the majority of the vehicles has an automatic transmission, but
still it should be taken into account when assigning emissions to different vehicle
categories.
3.4.4 Hybrid vehicles
An part of the fleet is only powered by electricity or fuel-cells. These hybrid vehicles
have emissions when they are using their environmental-friendly engine .In serial
hybrids, transient conditions are eliminated and thus it can be expected that emissions of
CO, NOx, HC, and PM if relevant will be low. In parallel hybrids this is only the case to a
certain extend. The noise homologation value for hybrid cars is several dBA lower than
that for fuel powered vehicles (although the number of hybrid vehicles on the market
does not allow obtaining statistical difference). During cruise, the noise emission of
hybrid cars is predominantly rolling noise. This rolling noise in addition is rather low since
the manufacturers of hybrid cars opt to equip their product with quiet tyres to strengthen
their environmentally friendly image.
Hybrid vehicles are however not represented enough in the fleet yet to have a significant
influence on the total emissions. Therefore they have not been taken into account in the
development of the emission models presented in this paper. Yet this class of vehicles
will increase in the future. Future extensions of the developed instantaneous emission
models will have to contain hybrid vehicles emissions as well if they want to give an
accurate prediction of the total fleet emission.
3.5 Political importance of different emissions
3.5.1 CO2
In 1997 the Kyoto protocol was ratified. The aim of this protocol is to reduce the
greenhouse gases in the industrialized nations that subscribed the protocol. The protocol
states that the emissions of greenhouse gases (CO2 equivalents) in Belgium should be
7.5% lower in the period 2008-2012 than they were in 1990. This aim should be
achieved by a maximum allowed amount of emissions (so called emission rights).
Belgium gets the right to emit a yearly quantity of CO2 emissions that equals 92.5 % of
the emissions in 1990. In 1990 the CO2 emissions came to 146,24 million tons. In 2001
this number had already mounted up to 149,30 million tons. The Kyoto goals however
only permitted an exhaust of 135,27 million tons of CO2. Based upon the year 2001
already a CO2 deficit of 14 million tons is noted4
. A significant reduction of CO2 exhaust is
thus an important political goal. This can be achieved by governmental action due the
support of measures that help to reduce the CO2 exhaust of industry and traffic. 16 % of
all CO2 exhaust in Belgium in 2003 was caused by traffic. Therefore the reduction of CO2
exhaust will be an important goal of the traffic management for which this project will
serve. Accurate CO2 exhaust prediction is thus an important factor when evaluating
emission models.

3.5.2 PM
Europe has since 2005 imposed a double limit for PM10 exhaust: a limit on the annual
average of 40 microgram per m3
and a daily limit of 50 microgram per m3
. The daily limit
can be exceeded only 35 times per year. Furthermore a new European guideline that has
not yet been approved will result in a new limit for PM2.5 of 25 microgram per m3
from
2010.
In 2006 the limit on the annual average emission was respected on all measurement
points. However the daily limit gives more problems: in 2005 it was exceeded on 17 of
the 31 measurement points, and in 2006 on 25 of the 31 points. In 2006 30% of the
Flemish population was exposed to too high PM concentrations. Europe demanded an
action plan for the areas that have too high values. Depending on the location, around 25
% of the PM emissions are believed to be caused by traffic. The chemical composition of
PM of different origin may differ and therefore the relative importance traffic in health
impact of PM may amount to more than 25%.
With regard to health impacts it should be mentioned that ultra-fine particles (UFP,
around 100nm) could be very significant. These small carbon-rich particles have a short
life span in atmosphere and thus pose a local rather than an international challenge. The
lack of European policy with regard to UFP should not diminish the effort we put in
reducing the emission of these particles.
For these reasons the reduction of PM10 and PM2.5 exhaust will be an important goal for
the traffic management for which this project will serve. Accurate PM exhaust prediction
from traffic is thus an important factor when evaluating emission models.
3.5.3 Noise
Traffic noise is via annoyance and sleep disturbance the major cause for dissatisfaction of
the population with the living environment in Flanders (Schriftelijk
Leefbaarheidsonderzoek, SLO 2001-2004-2007). As such road traffic noise emission
should be a political concern.
In addition, the environmental noise directive of the European Commission (2002/49/EG)
requires member states to design action plans for remediation of noise exposure caused
by traffic on major infrastructure and large cities by 2009 and for other important traffic
by 2013. Classical traffic noise reduction measures such as noise barriers and modified
road surface are expensive and not easily applicable in urban context. Thus traffic
management should be regarded as an alternative.
3.5.4 NOx
The objectives for NOx emission in Flanders are outlined in (Aminal, 2004). The target
needed to comply with the European directive for 2010 is set to a total traffic emissions
to 42 670 tons. In 2005 the overall NOx emission in Flanders amounted to 98 761 tons.
Thus NOx emissions should be reduced to roughly half over a period of 5 years.
Introduction of Euro-4 and Euro-5 vehicles should cause a significant reduction but the
2010 deadline will not be kept without speeding up this introduction. Increasing
percentage of diesel engines counteracts this positive evolution. Introducing HD vehicle
DeNOx-catalysers is one of the options. Traffic management may contribute a bit.
3.5.5 Summary
Based on political importance, the environmental goals of traffic management will mainly
be to:
• reduce noise, due the importance for local quality of life of this environmental
disturbance;
• reduce PM10, PM2.5 (and UFP) due to local effects on health;

• reduce CO2, because of the difficulty to reach international agreements and the
importance on a global scale.

4. LI T E R A T U R E S T U D Y : E M I S S I O N M O D E L S
In literature a lot of attention has gone to the development of reliable emission models
that would make the expensive emission measurements superfluous. Noise emissions
and exhaust emissions are usually modeled separately. In a first section of this chapter
the existing exhaust emission models are discussed and after that the noise emission
models.
4.1 Instantaneous exhaust emission models
4.1.1 Introduction
For exhaust emissions, an average speed-approach is used in models to support national
and regional emission inventories and prognoses. Examples are COPERT IV (EEA, 2009)
in Europe and Mobile5 and Mobile6 in the US (Heirigs et al, 2001). Using the average
speed as a single parameter means however that the influence of local speeds and
accelerations is smoothened. Two cycles with a comparable average speed can show a
totally different speed profile and thus also a totally different exhaust (Jost et al, 1995).
Besides that the predictive capacity of the average speed is very low for cars equipped
with a 3 way-catalyst. In these cars the exhaust depends on the transient situations and
not on the steady state (and thus the average speed). So, models based on average
speed are mostly used for applications at macro-scale. They are not accurate enough to
predict emissions on micro-scale.
In this application the goal is to use an exhaust emission model that can be used as an
aid to take decisions on traffic management at micro-scale. Therefore the average speed
models are not investigated further.
An approach that enables to account for instantaneous changes in driving behavior is the
instantaneous or modal emission model. In these models emissions are predicted at a
frequency of 1-10 Hz, which makes them more accurate. The models can also be
implemented into microscopic traffic models such as Paramics- or VISSIM. In this chapter
an overview of the available instantaneous emission models is given, with their
advantages and disadvantages. The overview starts with VETESS, a model that has been
developed in the European Decade project and that is available in this project through
the cooperation of VITO. After this the alternatives for VETESS, models that would have
to be purchased, are also discussed. Finally, all discussed instantaneous exhaust
emission models are compared and their pros and cons are evaluated.
4.1.2 VETESS
a. Operation
VETESS was developed in the framework of the European Decade project. It predicts
instantaneous emissions on a second-to-second basis for specific vehicles. It is based on
measurements of 3 vehicles: 1 Euro-4 gasoline car, 1 Euro-3 diesel car and 1 Euro-2
diesel car. The vehicles were tested extensively on test benches. In a first step, steady
state emission maps were created with these measurements. The engine parameters are
calculated from the drive cycle by simple mathematical equations. The total force on the
vehicle is calculated using the following equation:
Total force = acceleration resistance + climbing resistance + rolling resistance +
aerodynamic resistance
After subtraction of the losses during the energy conversion in the gearbox and the
differential, the torque at the wheels is known. The engine speed on the other hand is
calculated from the vehicle speed using the wheel diameter and a gear-change model.
The points at which gear is changed can be adjusted by the user. The two final variables,
engine torque and engine speed, are then stored on the rows and columns of a matrix.
For each combination of torque and speed the corresponding emission value is stored in

the matrix cell. When simulating a drive cycle afterwards one can look up the emissions
as a series of “quasi steady-state” conditions described by a combination of engine speed
and torque.
In reality however, exhaust emissions depend to a great extent on the transient
emissions, as described in section 2.3. To take this into account, the static model was
extended to a dynamic version. For this purpose an extra variable is introduced: the
torque change, measured at constant speed. Starting from steady state condition at
certain speed and torque, the torque is suddenly changed (in a step of about 0.2
seconds) and the emissions related to this step change are recorded. The change in
torque is induced by an external load. Based on these 3 variables (engine speed, torque
and change of torque) 4 parameters were identified to characterize changes in emissions.
Except for the steady state emission rate and the transient emission, the jump fraction
and the time constant of the transition between two emission rates are used.
An important remark for transient emissions is that the resulting value is integrated over
15 seconds. This means that compensation of delay and response time in the emission
measurements (exhaust pipe, transport of the sample gas to the analyzers and analyzer
response) are not essential for these calculations. This is contrary to instantaneous
dynamic models such as EMPA or PHEM (see 3.1.9, 3.1.10), where the gas transport time
has to be compensated for.
b. Validation
In a first validation the same 3 vehicles that were used to develop the model are now
simulated and compared to measurements. These 3 vehicles were tested on a NEDC
cycle and on a real-life cycle. The results for fuel consumption and CO2 are within a 5
percent margin, as long as the gear shift strategy matches with reality. A different gear
shift strategy can lead to errors of 20%. NOx and PM predictions were a lot better for
diesel than for gasoline, with errors in a range of 10-20 %. The results of diesel engines
for HC and CO depend to a large extent on the correctness of the transient correction
matrix, with transient corrections that range from 20 to 200 %. Due to the oxidation
catalysts the absolute HC and CO values are very low. The validation results for the
gasoline vehicle were worse, mainly due to the 3 way catalyst that makes a reliable
model very hard to develop. Simulation results for all emissions except CO2 show large
variability.
In a second validation . vehicles other than the 3 that were used to develop the model
were tested. As could be expected simulation and measurement differ a lot more now.
This implies that the model is not vast enough to represent the entire fleet.
c. Pro and contra
VETESS is able to predict exhaust for (diesel) PM as well as the classic pollutants. This is
an important advantage given the rising importance of PM for health issues. The model is
very user-friendly; a lot of time has been spent on creating an easy to use interface. One
only has to enter the desired car and driving cycle, to calculate the corresponding
emissions. This advantage is however of little importance when the model will be
integrated with a traffic model.
VETESS is only based on 3 vehicles and thus not representative for the rest of the fleet,
which could also be concluded from the external validation results. The model works
satisfactorily in predicting fuel consumption of both diesel and gasoline vehicles, as well
as for predicting diesel engine emissions. For the newer gasoline vehicles with 3 way-
catalyst the model is not sufficiently accurate. Before a car can be part of the VETESS
database it has to be measured accurately for a couple of weeks to determine its
emission matrix, which makes extension of the model expensive. The model can thus not
be used to evaluate vehicles other than the 3 specific ones that were used for its
development. Further extension of the model could be an option.

4.1.3 Mobilee
Mobilee is another European emissions model and the model developed an integrated
methodology for the evaluation of impacts of local traffic plans on accessibility, traffic
viability, noise nuisance and air quality. Mobilee used new methods to evaluate the
impact categories at the district or street level and in more detail than before. As a side
product of this project – developing emission models was not the main focus – a set of
emission functions was developed to allow using micro traffic simulation models as an
input. For noise emission, the Mobilee project used the existing NORD2000 functions (see
later).
a. Operation
The emission functions of Mobilee are approximated by mapping the instantaneous
emissions measured using the VOEM system with the parameters such as speed,
acceleration, product of speed and acceleration. Only the instantaneous value of these
input parameters is considered. These functions have been formulated based on a fleet of
twenty vehicles only and a maximum of six vehicles per vehicle class. Moreover, all these
vehicles are Euro 0, Euro 1 and Euro 2 vehicles. The emission functions for the Euro 3
and Euro 4 categories are approximated from COPERT III.
b. Validation
The model was validated for a particular type of vehicle (passenger car, 0 – 1.4 l cylinder
capacity, Euro 3 homologated) by comparing it with the emission results. Although the
correlation between measured and modeled seems quite poor, the model is able to locate
the emissions at the correct time (and hence place).
The poor correlation is mainly due to the huge differences between vehicles, even
between vehicles of the same category. The correlations of measured and modeled
emissions were found for 6 different passenger cars, all Euro 1 homologated and with a
cylinder capacity less than 1.4 l, and these were used to estimate the emissions for a
particular trip, driven in an urban area and taking about 20 minutes. Huge differences
between the measured and estimated emissions were found for CO and NOx. However,
the results for CO2 were estimated acceptably.
c. Pro and Contra
The Mobilee model is amongst all models that can be used in conjunction with micro
traffic simulation the simpler one since emissions are a straight forward function of speed
and acceleration. Moreover it is readily available with the project partners. However since
the database is based on only twenty vehicles total, and for some vehicle categories, the
functions are based on just one set of data, the prediction results from Mobilee functions
cannot be fully trusted. Validation tests revealed that it only yields acceptable results for
CO2 emissions. Moreover no measurements were done on Euro 3, 4 or 5 classes. This
makes the Mobilee emission functions quite outdated and not applicable to today’s
vehicle fleet. Mobilee was used in the tests presented here for the sole reason of
availability at the time that this report was written.
4.1.4 MODEM (Joumard et al, 1995)
a. Operation
MODEM, an American emissions model, is based on a matrix that contains speed v on its
rows and the product of speed and acceleration v*a on its columns. All data are provided
on a 1Hz-basis. Measurements were conducted on 150 cars, divided into 12 classes and
14 different driving cycles. For every combination of speed and acceleration the average
of all emission measurements are calculated, resulting in the emission value for this
matrix position.

When for a certain real-life trip speed and acceleration are known on a second-to-second
basis, this matrix can be used to determine the appropriate emission for every
combination of speed and acceleration. This way dynamic emission curves are developed.
b. Validation
The validation of MODEM is performed over 14 urban cycles, which were applied to about
150 gasoline vehicles. The average measured emissions for each of the 14 cycles were
obtained and they were compared to the predictions from MODEM model. The deviations
between the measured and calculated emissions values were computed for each of the
emissions species. While the CO emissions deviated between -15 to +14%, HC from -19
to +25%, NOx from -34 to +13% and CO2 from -16 to +8%. The variation ranges of the
pollutants corresponding to their absolute values are account to an average of 15% for
CO, 20% for HC, 10% for NOx , 2% for fuel consumption and CO2. However, it was also
observed that the model lacks reliability in number of cases and lead to erroneous
conclusions (Sturm et al.,1996).
c. Pro and contra
MODEM was one of the first models that questioned the average speed as being the only
parameter, and switched to an emission matrix based on instantaneous speed and
acceleration.
Sturm et al (1997), and references within, showed that the dynamics of a driving cycle
play an important role when measuring instantaneous emission. In MODEM the produced
emission values depend strongly on the used driving cycle. There is even large variability
between different drivers for the same cycle. Variations of 10 to 20% were measured.
The errors that arise for every matrix element are accumulated and can thus lead to a
completely wrong end result.
It is besides very hard to fill out all positions on the matrix. This would require extensive
measurements on very different driving cycles, which would be expensive. This means
that several matrix positions remain empty.
As mentioned before, gasoline cars with 3 way-catalysts suffer from short transient
emission peaks (10 to 100 times the normal value) that influence the resulting emission
value strongly. The influence of the short yet important transient periods on the total
emission value is underestimated in this model. The values in the matrix are measured in
steady-state. When the dynamics (gear change behavior, number of stops,..) of the
modeled (real-life) traffic situation don’t agree with the dynamics of the test cycles used
to develop the emission values, the results are bad. This is undesirable for an emission
model since it needs to be as generally applicable as possible. Furthermore the influence
of external factors like road gradient and airco use is not modeled.
4.1.5 CMEM (Barth et al, 2000)
a. Operation
CMEM is another American emissions model developed to avoid the problems with
interpolation and the lack of transient behavior in MODEM. Barth et al (2000) have
chosen to break up the formation process of emissions in different stages. These stages
are then modeled analytically. The model first estimates the fuel consumption from the
engine behavior and this is then turned into emission using another model. This is a
more deterministic approach that enables to investigate the cause of the emissions,
instead of solely measuring the consequences. Transient behavior can now be modeled
more accurately.
The analytic model contains a lot of parameters. Measurements were conducted on 344
vehicles and 3 different drive cycles. The most recent cars in the database were built in
1997. To keep things practical the fleet was divided into 26 vehicle categories. For every
category the average mass, engine capacity, hp, maximum engine speed, torque, etc.
are set.

An input file requires the vehicle category, stop time, secondary load (airco) and air
humidity as an input. Another activity file is required that contains speed, acceleration,
road gradient and airco on a second to second basis. The output of the model is emission
of CO2, CO, NOx en HC. CMEM is commercially available.
b. Validation
For the validation of a model it is important that measurement and simulation are
compared for a driving cycle that was not used for the development of the model. Only
this way the predictive capacity can be tested independently. The vehicles for the
validation are preferably not used in the database upon which the model is based
(external validation).
Barth et al (2001) mentioned a validation of their model with measurements on vehicles
that were already in the database, and thus cannot provide independent results (internal
validation). This validation was performed with so-called bag-values, in which emissions
over a complete driving cycle are compared. This gives an indication on the performance
of the model, but not on the correctness of the short-term analysis. The results for the
cycles that were not used for the development of the model (US06 driving cycle) are
worse than those for the other cycles. The correlation coefficient between measurement
and simulation for the US06 cycle was 0.72 for CO and circa 0.85 for NOx, CO2 and HC.
The second-by-second values were also compared, but here only the bias between
measurement and simulation is mentioned, and no data concerning correlation or RMS-
error.
Rakha et al (2004) compared CMEM to VT-Micro (see 3.1.7). The investigators concluded
that CMEM showed some strange results. For example the NOx values did not show a
(typical) descent at high velocities, and strange peak behavior was noticed at low
velocities and high accelerations. The model also systematically overestimates the MOE
(measure of effectiveness) for all accelerations. Rakha et al (2004) suspect that these
shortcomings for CMEM are due to the fact that only instantaneous speed is used to
describe the engine condition. There is however no detailed description of the internal
operation of the CMEM model available that can prove this presumption.
Tate et al (2005) validated CMEM before they integrated it into the simulation program
DRACULA. CMEM was validated in 2 ways. In both cases only CO was measured. A first
validation took place on a test bench with a ford escort 98, on the FTP75 driving cycle. A
total overestimation by CMEM of 30 % was noticed. A second validation was performed
on a Ford Mondeo 2003, an Euro-3 car. In CMEM, which is based on a database with the
most recent car being one from 1997, this car was catalogued in the Tier 1 category. The
test showed an overestimation of the CO values by more than 300%. The investigators
blame this on the significant improvements technology has undergone in the last decade.
This investigation shows that it is not possible to transfer emission models based on an
outdated database to the current European standards.
c. Pro and contra
Due to the analytic approach it becomes now possible to perform transient corrections.
The model is based on a large database which makes the model very representative for
the fleet. The breakdown into 26 categories makes the risk rather small that a vehicle
deviates a lot from the average used for its category.
The model requires however a lot of input data that are not always available. When a
large number of parameters have to be estimated, this can result in a large (cumulated)
error. The analytic model is too much a black box, which makes it unclear how the
transient corrections are modeled precisely. CMEM also requires a rather large
computation time. Only American cars built before 1997 were used in the model. This
results in a significant overestimation of emissions produced by more recent cars
equipped with modern technologies, as was shown in the validation by Tate et al (2005).
Since CMEM is the most widespread instantaneous emission model in the US, it is

validated a lot for comparison with other, newer models. These validations showed in
most cases large errors between model and reality.
4.1.6 EMIT (Cappiello et al, 2002)
a. Operation
EMIT is an American statistical model that uses the same database as CMEM. The
breakdown in vehicle categories used in CMEM was maintained. Contrary to CMEM now
the base of the model is a statistical analysis of all measured emissions. No attempt has
been made to model different engine processes. EMIT consists of 2 different modules. In
a first module emissions are estimated as they leave the engine. In a second module
these emissions are turned into the values as they leave the tailpipe (after the catalyst).
The separate modeling of catalyst behavior is however purely empirical and not based on
the chemical phenomena that control catalyst efficiency. Each module consists of a
number of polynomial equations of a certain emission as a function of instantaneous
speed and acceleration. The constants that appear in this equation are only calibrated for
2 out of the in total 26 categories used by Cappiello et al (2002). The only required
inputs for the model are speed and acceleration on a second-to-second basis and the
vehicle category. The model outputs are emissions for CO2, CO, HC and NOx at a
frequency of 1 Hz. The computation time is a lot lower than for CMEM.
b. Validation
Validation was performed by Cappiello et al (2002) on a driving cycle (US06) that was
not used for the measurements upon which the model is based. This results in good
prediction for fuel consumption and CO2. The maximum error was 5.3 % and correlation
between prediction and measurement was higher than 0.95. The results for CO are less
reliable, with an average error of 17%, while the results for HC are unreliable with a
maximum error for 1 category of 83.4 % and a correlation of 0.22. The low reliability for
HC predictions is probably due to the non-modeling of time dependant effects.
c. Pro and contra
The biggest advantage of EMIT is that it runs faster than CMEM. Due to the faster
implementation the model also shows less nuances than CMEM. However a separate
empirical catalyst module is introduced, which allows to take into account the behavior of
the catalyst better than for CMEM. There is no possibility to take road gradient or airco
use into account and time-history effects are not accounted for either. This is due to the
fact that the model is statistic and only has current speed and acceleration as an input.
The engine operation state is not included in the model. Since the model is statistic it
depends also strongly on the used database, which (as for CMEM) only contains vehicles
produced before 1997. With the simplification of CMEM some important parameters have
not been considered, which leads to some peaks with low agreement between predicted
and measured values in the validation (e.g. for HC).
4.1.7 VT-Micro (Rakha et al, 2004a)
a. Operation
Just like EMIT, VT-Micro, another American emissions model is based on a statistic
approach, but now there is only 1 set of polynomial equations that calculate the tailpipe-
out emissions directly. The model presented by Rakha et al (2004b, 2004d) is an
improved version of the original model presented by Rakha et al (2002). The model is
split into separate equations for positive and negative acceleration, because these show
different characteristics towards emission behavior.
The model is based on measurements on 69 cars and trucks that have been tested on 17
driving cycles. For the subdivision of the fleet into vehicle categories the statistic CART
algorithm was used. This resulted in 3 important variables that determine a vehicle class:
age, cylinder content and number of kilometers. An important condition was that at least
5 (out of 69) vehicles should belong to a single category. Thus irrelevant categories

containing measurements of only 1 or 2 measured cars are avoided. Eventually the fleet
was subdivided into 5 vehicle and 2 truck categories. A validation on 1 driving cycle
showed that this resulted in less variation between cars of the same category than in the
case for CMEM.
In the improved VT-Micro model the time shift between emission and the engine situation
(i.e. speed and acceleration) that is causing it, is accounted for. The average time shift
between emitted and measured value was determined to be 6-8 seconds. The influence
of the road gradient is also accounted for in the term for acceleration, and is thus
included in the model.
The VT-Micro model was extended to PM10 by North (2006). He was one of the first to
perform instantaneous emission measurements for PM. He used only 1 vehicle, a `99
diesel. The model was calibrated based on measurements with this vehicle on 6 different
driving cycles. The validation showed significant deviations, especially for situations with
negative acceleration and high speed. Therefore the model was adjusted, with a
subdivision of equation according to vehicle specific power (VSP) instead of acceleration.
The results were better (see validation) yet not sufficient.
b. Validation
In Rakha et al (2004b) VT-Micro is compared with CMEM for 2 cases. In a first test the
two are compared based on data from driving cycles used to develop VT-Micro. In this
case VT-Micro predicted the measured emissions a lot better than CMEM, which is logical.
In a second case, independent measurements were used on 50 vehicles and an
aggressive driving cycle that was not used for the development of one of the models. VT-
Micro shows better agreement with the measurements here as well. To verify this one
should consult the figures in the mentioned article, since no numbers have been
published.
Rakha et al (2004b) compared the emission models CMEM, EMIT and VT-Micro using
instantaneous emission measurements on the road during real traffic situations. This was
done with 1 vehicle equipped with an On-road Emission Measurement unit (OEM 2100).
None of the 3 models appeared to be able to perform accurate predictions. The average
error for all emissions was 85.7% for CMEM, 82.3% for EMIT and 43.6 % for VT-Micro.
The largest error was usually occurring in the prediction of HC, while VT-Micro appeared
to be relatively accurate for NOx (11.3% error).
One has to notice that when comparing models regarding 1 single vehicle the correctness
of the classification of the vehicle in its category is very important. When the VT-Micro
model was calibrated to the used car instead of the average car of its category, the total
error was only 14%. When using the model on larger scale it is however impossible to
model every vehicle separately, so a classification into categories becomes inevitable.
The deviations between model prediction and measurement in this validation are
however very high for all models investigated.
North (2006) extended the model to PM and also conducted an extra validation. The
results for the original VT-Micro model showed a total error of 12% for CO,10 for CO2,
70% for HC, 4% for NO and 14% for PM. Validation of a second, adjusted model with
VSP instead of acceleration as divisive parameter, showed a total error for PM of 11%,
which is still significant. The RMS error was even higher: 46% of the average value. The
validation was performed with the same vehicle used for calibrating the model for
different test cycles.
c. Pro and contra
The biggest argument pro VT-Micro is the better result in the validation compared to
CMEM and EMIT. This validation was however performed on 1 single vehicle, and the
result is influenced by the correctness of its classification in the different models. The
database of vehicles on which the model is based is smaller than the one used by CMEM
and only consists of vehicles produced before 1996, which makes the method outdated
as well. Just like all other statistical models VT-Micro strongly depends on the relevance

of its database. Just like for EMIT there is no correction for transient engine situations. As
was shown in the validation VT-Micro cannot be considered to be accurate, even though a
significant improvement compared to previous models can be noticed.
4.1.8 Poly (Qi et al, 2004)
a. Operation
Poly is another American statistical model that wants to emphasize the influence of
acceleration and deceleration. Therefore the accelerations of times previous to the
modeled time are accounted for in the equation that determines the instantaneous
emission value.
The classification of the vehicle fleet is based on the size of the car (3 categories), the
age (6 sub categories) and the type of emitter (1 normal type and 4 high emitter types).
For this last criterion the classification by Barth et al (2000) was used. Not all resulting
vehicle classes are maintained because some categories are not represented in the fleet.
Eventually a categorization of the fleet into 41 different types was the result and different
governing equations are developed for each type.
The model equations depend on the specific power, the speed, time and road gradient.
The influence of the previous time steps is accounted for by variables that indicate the
acceleration rate and the deceleration rate on these time steps. There are also 2 extra
variables that represent the durations of deceleration and acceleration.
The calibration of the constants in the model equation was performed using the same
database of 344 vehicles that was used for the development of CMEM.
b. Validation
Qi et al (2004) validated their model by comparing emission measurements and
predictions for 10 vehicles on a driving cycle that was not used for the development of
the model. The results were compared with predictions of CMEM and Integration for the
same 10 cars. Integration is a traffic micro-simulation program similar to Paramics, but
integrated with VT-Micro. The results of VT-Micro in this study were very bad with some
unrealistic peak where the error rises above 1000%. These results have been considered
irrelevant and are neglected here.
The average RMS-error of Poly over all measurements and for all emissions was 0.47
whereas CMEM had an average error of 0.65. The error on the total emission was 58%
for VT-Micro and 67 % for CMEM. The correlation coefficient was 0.53 for both models.
Further conclusions that can be drawn from this validation that a very low correlation
coefficient results from the prediction of HC in both models (0.25), and Poly generally
gives better results than the other 2 models. Here the results for VT-Micro have to be
treated with reserves, since they were most probably obtained with version 1.0, in which
no classification into different vehicle categories had been made. This might explain the
poor results. North (2006) concluded from the better results of Poly that taking into
account previous time steps influences the result in a positive way.
c. Pro and contra
Poly is a statistical model that tries to take transient effects into account, by including the
speed and acceleration on previous time steps into the model. The road gradient is also
taken into account. The validation shows that this seems to have a positive effect on the
results, even though still significant deviations between reality and model can be noticed.
This is especially the case for the prediction of HC emissions. Since Poly is based on the
same database as CMEM it can also be considered an outdated model. It requires input
from previous time steps, thus a continuous inflow of data is necessary. The model is not
capable of predicting CO emissions or fuel consumption.

4.1.9 EMPA (Ajtay and Weilenmann, 2004b)
a. Operation
EMPA, is a European emissions model developed in the framework of the Artemis project
(Joumard et al, 2007). The model consists of an emission matrix based on engine-
parameters such as engine speed and torque instead of speed and acceleration. During
extensive tests, every matrix position is coupled to an emission value. These emission
values are then interpolated during the later application of the model.
A first important innovative aspect of the EMPA model compared to earlier emission
matrices such as VETESS is the modeling of gas transport dynamics. These dynamics
refer to the variable time shift that the exhaust gases undergo from the moment they
leave the tailpipe until the moment they reach the measurement installation. In other
models where this is not compensated for, every measured instantaneous emission is
connected to `the wrong second` of the engine state. Within the EMPA model a complete
model for the transport of emissions during measurements was developed, which makes
the total model a lot more accurate. This model for gas transport consists of 3 parts: the
tailpipe, where gases are mixed, the dilution of the gas to the analyzer, and the response
time of the analyzer itself. The gas transport can, depending on the engine speed, take
10 to 25 seconds. The gas transport model has a maximum time error of 2.5 seconds,
which means an enormous improvement. A more accurate emissions matrix is the result.
A disadvantage is that the volume flow of the exhaust gases is a required input of the
model, which requires extra measurements.
Another important change regarding to previous models is based on a theorem from
Weilenmann (2001). He stated that a lot of the emission peaks in modern cars are very
transient and last only 0.1 seconds. Moreover a lot of engine parameters such as
manifold pressure, change at a higher frequency than 1 Hz. In this model everything is
handled on a 10 Hz basis, so more accurate predictions become possible.
♦ Static model
The static EMPA model is based on measurements for 20 vehicles (3 pre- Euro-1
gasoline, 7 Euro-2 diesel and 10 Euro-3 gasoline with 3 way catalyst) on 16 different
drive cycles. First a static emission matrix was developed with two parameters: engine
speed n and brake mean effective pressure Bmep. Bmep is defined as a scaled function
of the engine torque, divided by the displacement volume (see Eq.2).
d
mep
V
T
B
π4⋅
=
Equation 2
This parameter can be used to compare two different engines in a similar state. This
cannot be done using the torque T, since two vehicles with the same torque can still be in
a different gear or engine state. Interpolation between different matrix nodes is done
with bilinear interpolation between 2x2 neighbors, a method that gave better results than
Shepard interpolation.
The static model is performing well for diesel and gasoline cars without 3 way catalyst
(see validation). Gasoline cars with 3 way catalyst however get their emissions mostly
from short transient peaks. For these cars a dynamic model is necessary.

♦ Dynamic model
To improve the results for gasoline cars, the model was extended with an extra
parameter, the derivative of the manifold pressure dp. This way a 4 dimensional matrix is
created with emissions depending on 3 parameters. The dynamic model was developed
by measurements on 5 extra vehicles with 3 way catalyst: 2 Euro-2 and 3 Euro-3
vehicles. Dynamic matrices were only developed for CO, NOx and HC since CO2 already
showed satisfactory results using the static model.
The parameter dp was added to make the model dynamic, analogous to the change of
torque used in VETESS. The manifold pressure was chosen because this signal shows less
noise than the torque, and moreover is measurable directly. The torque on the other
hand is a derived variable. One has to point out that the resulting model is still based on
engine parameters and thus only takes the catalyst influence into account through the
measured emissions. An extra catalyst model based on the chemical phenomena that
take place in the catalyst is necessary and is currently being developed at EMPA.
b. Validation
♦ Static model
The model was validated in 3 different stages as described Ajtay (2005). The validation
was performed on the same 20 vehicles used for development of the model.
In a first cross-validation a static matrix was developed using data from 15 cycles, and a
16th
cycle was used for validation. The results for the average RMS-error are very good.
For pre-Euro-1 cars an absolute error of 8 % was measured for HC, for Euro-2 diesel this
was 14% and for Euro-3 gasoline already 36%. The results for other emissions were a lot
better with an absolute error for CO of respectively 9%, 13% en 21%. These values are
an average of the errors of all individual vehicles in each category. The model was also
compared with a classic v-v*a matrix as in Modem, thus without a correction for the
dynamic gas transport and on a 1 Hz basis. The results were a lot better for the new
model.
In a second stage, described in Ajtay et al (2006), the model was validated on meso-
scale. The error of the average vehicle for each category was compared with the error for
the individual vehicles in that category. The error for the average car was as significantly
lower and the correlation coefficient was higher than for individual vehicles. This indicates
that the error for individual cars is random, and doesn’t indicate a structural bias in the
model. The RMS-error for the average pre-Euro-1 vehicle was circa 0.01 for NOx and HC,
and 0.001 for CO2 and CO. For Euro-2 diesel there was an error of 0.1 for HC and CO,
and 0.001 for NOx and CO2. For Euro-3 gasoline however the error was 0.1 for NOx and
0.5 for HC. Regarding correlation there were very good results for CO2 and CO (R2
> 0.9
for all categories), while the results of gasoline vehicles for NOx (R2
=0.6) and (HC
(R2
=0.4) are less reliable. The vehicles used for this validation are the same as those
used for the development of the model, which can partly be an explanation for the good
results. The unsatisfactory results for gasoline vehicles in both validations of the static
model gave rise to the development of the dynamic model.
♦ Dynamic model
The dynamic model, extended with a third parameter dp, was at first validated similar to
the validation of the previous (static) model. The 5 vehicles were tested on 15 cycles for
development of the matrices and then validated on a 16th
cycle, where the 16th
cycle was
permutated. The results show an enormous improvement, with a total relative error
lower than 5% for all emissions, and all correlations being higher than 0.95. One has to
notice however that the number of cars (5) is not representative for the entire fleet.
In a second validation, 4 vehicles (3 Euro-3 gasoline and 1 Euro-3 diesel) were tested on
the 16 cycles and 18 extra external traffic situations with varying gradient and gear shift
strategies (Ajtay, 2005 and Ajtay and Weilenmann, 2005). Gasoline vehicles are the

same as those that were used for development of the model. The gasoline vehicle results
were very good judging from the published figures (no numbers were published). For
diesel vehicles the static model is used, since this already showed satisfactory results due
to the lack of catalyst. There was a relative error of 8% for CO2 and 12% for NOx, but a
slightly higher error for CO and HC (only figures given). According to the investigators
these less satisfactory results are due to the low absolute values that have to be
measured for CO and HC. Since the analyzer is calibrated for values that are significantly
higher, the measured values are within the margin of the analyzer. It is however also
possible that the less satisfactory results for diesel are due to the fact that the tested
vehicle was not used in the development of the static model, so this is an external
validation.
c. Pro and contra
The predictions of emissions for engines with 3 way catalysts give more reliable results
than in other models such as VETESS or PHEM. Due to the elaboration of the gas
transport model and the high frequency of the used data, the model seems to be very
realistic in its exhaust emission predictions. This also shows in the validation results,
which seem to be better than the ones published by PHEM (see 3.1.9) or VETESS. The
dynamic model showed satisfactory results, even for gasoline vehicles with 3 way
catalyst. This is the hardest category to model due to the unpredictable catalyst
behavior, and thus the published results were encouraging.
The EMPA model uses a newer and for Europe, more relevant database than the
American models, but it still consists of only 25 vehicles which may be not enough to
represent the entire fleet. This is certainly valid for the dynamic model that is based on
the measurement of merely 5 vehicles. This goes however at the expense of more
detailed and thus more expensive input of data at a frequency of 10 Hz. This frequency is
needed to measure and thus model some very rapidly changing transient peaks that
influence the total emission value significantly. The model is not yet user friendly since
the input requires knowledge of both engine speed and torque, plus the derivative of the
manifold pressure for the dynamic model. These are not standard parameters that can be
derived from traffic simulations. New development will be needed to calculate these
parameters from speed and/or acceleration. No independent external validation has been
published. All validations mentioned were conducted with the same vehicles used to
develop the model, except for 1 Euro-3 diesel vehicle. No model for Euro-4 or Euro-5 has
yet been proposed, so in the future extension of the model will be required. Another
important disadvantage is that no emissions of PM have been included in the model even
though they are becoming increasingly important in the European legislation.
Furthermore the model will only be commercially available from 2009. Especially due to
the last two points the EMPA model is less attractive than its competitors.
4.1.10 PHEM (Zallinger et al, 2005)
a. Operation
Just like the EMPA model PHEM (Passenger car and Heavy duty Emissions Model) was a
European emissions model that was also developed in the framework of the Artemis
project (Joumard et al, 2007). It was originally meant for trucks but was extended to
passenger cars by Zallinger et al (2005). Similar to VETESS, this model consists of an
emission matrix with as main parameters the engine speed n and the effective power P.
The inputs for this matrix are thus engine parameters. The effective input that is required
by PHEM are different files for characterization of the vehicle, the driving cycle, the
emission matrix to be used and the load curve. Input is required at a frequency of 1 Hz.
Engine speed is then derived from the vehicle speed when the wheel diameter and gear
proportions are known. An important asset is the detailed gear shift model that was
developed at TU Graz, described in Zallinger et al (2005a). Gear shift happens at a
certain engine speed that depends on the speed and the required power. Besides this a

great number of corrections have been inserted to simulate realistic driving behavior. As
was shown in Pelkmans et al (2004), errors in the gear shift model can lead to large
deviations in the resulting emission. The gear shift model was validated and showed very
good agreement with reality.
From the driving cycle and the road gradient the effective power is calculated after
simulation of airco, the driving resistance and transmission losses. This eventually ends
up with a static matrix of emission as a function of n and P, analogous to VETESS. This
matrix is then corrected for transient engine state and for cold starts.
The emission values included in the matrix were obtained from measurements on 32
cars. Similar to what was done at EMPA, the dynamics of the gas transport when
measuring emissions on a test bench was accounted for, as elaborated in Le Anh et al
(2005). This method is different from the one used in EMPA, with a subdivision into 4
stages. For diesel engines an extra measurement of the exhaust volume flow is also a
necessary input for the gas transport model.
The emission matrices were developed based on dynamic tests in the laboratory, since
no steady state tests were available within the Artemis project. Engine speed and power
were calculated as was explained before, and then coupled to the corresponding (time
shift corrected) measured emission. From these data for all 32 vehicles an average
matrix was developed for the categories Euro 0 till IV, for diesel as well as gasoline
vehicles.
When constructing the matrix the Shepard method for interpolation is used, explained in
Zallinger et al, 2005b. This method uses the 3 points in the matrix that are closest to the
desired combination of n and P. Extra weight factors have been introduced for a correct
interpolation at low load and high speed. A breakdown between positive and negative
power has been introduced, since emission behavior is different for different engine
states. The interpolation method was tested and it gave satisfactory results.
Eventually a transient correction matrix was created for every category. Contrary to
EMPA or VETESS no fixed extra parameter is used for all vehicle categories but a more
empirical approach is chosen. For all vehicles the difference between the prediction of the
static model and the measured emission was investigated and pertinent parameters were
searched that showed a statistically significant influence on this difference. In the end 6
parameters were retained that have been stored in separate n-P matrices. These
parameters can all be calculated by PHEM from the engine speed and the power course.
An example of such a parameter is the amplitude of the power 3 seconds before a
transient change larger than 3% of the maximum power. This parameter shows to have
a significant influence on the CO-emission. The final emission is then calculated by
adding the value coming from the dynamic correction function to the emission value from
the static matrix. The correction functions are different for each emission and for each
vehicle class, and contain at most 3 different transient parameters. An important remark
is that no transient correction is available for gasoline engines yet (December 2007). This
is however the most important category to be corrected. For example the EMPA model
doesn’t even use dynamic correction for diesel engines since they don’t contain a 3 way
catalyst.
A cold start module, described by Hausberger et al (2002) is also included in the model.
This module calculates the temperature of the cooling water and the temperature of the
3 way catalyst from an estimation of the heat losses occurring in the engine. For these
temperatures a correction matrix is developed for each emission, analogous to the
transient correction matrices. The cold start module was validated on 4 vehicles, with a
general result that diesel engines (no 3 way catalyst) were modeled more accurately
than gasoline engines.
b. Validation
The model was validated by Zallinger and Hausberger (2004). In this validation 5 Euro-2
diesel and 6 Euro-3 gasoline vehicles were tested on 12 cycles. The results were

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