Trachet, Madireddy_2009

WETENSCHAPSPARK 5

B 3590 DIEPENBEEK T ► 011 26 91 12 F ► 011 26 91 99 E ► [email protected] 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 Mobi l i te i t & Openbare Werken Spoo r Ve r kee r sve i l i g he 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 [email protected] I www.steunpuntmowverkeersveiligheid.be

Steunpunt Mobiliteit & Openbare Werken 3 RA-MOW-2010-001 Spoor Verkeersveiligheid

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:

OyHxCOOyxHC

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 VTB 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


compared with an average car with an emission matrix for diesel engines based on measurements of other cycles. For the gasoline vehicles the matrix was strangely enough based on the same cycles used for the validation. The results for diesel show a high accuracy for NOx and adequate results for HC, CO and PM. The results for PM show large variability. For gasoline vehicles the results were `OK` for CO and HC, but for NOx an overestimation was visible in the simulated values that was due to the low absolute values according to the investigators. For both validations only figures have been published, and no precise numbers.

c. Pro and contra

PHEM is the European model based on the largest database (32) vehicles. This database is larger than the one used by EMPA (25) or VETESS (3). This enables PHEM to represent all categories for both gasoline and diesel engines in its model. PHEM is (together with VETESS) the only model that simulates PM emissions, even though this is done by scaling HC emissions. According to North (2006) this leads to wrong results since these two emissions are caused by totally different processes (see also Table 1).

Still PM modeling is important in the current legislation. PHEM has also been equipped with a cold start correction mode, which is not yet included in VETESS or EMPA. Another advantage for PHEM is that it also has a truck version. When simulating traffic situations with a substantial amount of HD traffic, the model can thus readily be extended. This is an important asset concerning the extra emissions caused by HD traffic as mentioned in section 2.4.2

Despite the larger database, the average cars for the different categories are based on about 5 vehicles per category. This is not really a lot, given the large variability in emissions between vehicles of one Euro category. Another disadvantage of the lack of vehicles for testing is that not all matrix positions can be filled properly. There are not enough data available to fill the matrix positions for road gradients higher than 6%. The possibility to use older measurements to improve the model has been investigated, but not yet introduced. Just like for EMPA an independent reliable validation has not yet been published. In the first results however errors have been found in the NOx prediction that could not be explained properly. There is no internal validation published yet for Euro-4 vehicles and a transient correction for gasoline vehicles with 3 way catalyst is not (yet) available. An update of the model will be published soon, and it is commercially available through co-operation with TU Graz.

4.1.11 Versit+ (Smit et al, 2006)

a. Operation

Versit+ is a European emissions model developed by Netherlands Organization of Applied Scientific Research (TNO), in Delft, the Netherlands. Versit+ consists of a set of statistical models for detailed vehicle categories that have been constructed using multiple linear regression analysis.

The inputs for the model are vehicle category, vehicle positions and speed for all vehicles in the network. These data can be obtained from measurements (e.g. VEDETT) or from a microscopic traffic model. TNO offers a combined package containing VISSIM as a traffic model already combined with Versit+. The fleet has been divided into 80 vehicle classes based on Euro class, fuel type, injection type, gear shift method, weight and diesel particulate filter (DPF). Only categories containing a significant number of measurements were retained.

The model is based on a very large database (12 000 tests on 153 speed profiles) which takes into account all aspects of real-time driving behavior. In the earlier version of Versit, all emission measurements have been performed on the total cycle and not instantaneous. However, the latest version of Versit+ translates the total cycle results into velocity and acceleration dependant results. A dynamic variable is defined as a linear


combination of velocity and acceleration and for a given driving cycle, ten different regions (combination of velocity and acceleration) were defined. These regions define the driving behavior of the vehicle such as idling, accelerating, aggressive highway driving, etc. Different emissions functions based on speed and acceleration were developed for each of these regions and the output of these emission functions is afterwards corrected for airco use, cold start, ageing and high emitters. The correction factors for airco use are based on measurements by Weilenmann et al (2005) for Euro 3,4,5 and own measurements by TNO for Euro 1,2. The cold start correction and the ageing correction was based on own measurements by TNO. It has to be noted that the PM emissions also include the emissions from wear and tear of the vehicle tires and brakes.

b. Validation

An internal validation with only correlation measurements showed good results for the different diesel categories (average correlation of 0.9), but less satisfactory for gasoline (average 0.74). As a part of external evaluation, 10 reduced models were elaborated by eliminating 25% of the data randomly. These reduced models are then compared to the original model. The largest deviation was found for NOx of Euro-3 gasoline vehicles with indirect injection, with an average error of -20 to +30% with peaks up to 190%. Other categories showed lower deviations, with an average around +- 15%. Compared to COPERT IV, the Versit + algorithms provide increased accuracy with respect to prediction of emissions in specific traffic situations.

The model results were also externally validated recently with the data collected by VITO’s On Road Emissions and Energy Measurement (VOEM) system. A thorough introduction of the VOEM system and the procedure to measure emissions were explained by De Vlieger (1997). The instantaneous emissions from four diesel vehicles and one gasoline vehicle were obtained from VOEM measurements. The diesel vehicles tested are Citroen Berlingo, Citroen C4, Nissan Patrol and Opel Vivaro and the gasoline vehicle tested is VW Golf Plus. The PM results for Citroen C4 were not measured by VOEM because the vehicle had diesel particular filter (DPF) installed which reduced the PM levels so low that the equipment cannot measure. So, these data were not filled in.

The speed profiles of each of these vehicle tests are inputted into the Versit+, which gave the continuous emissions predictions for NOx, CO2 and PM. These results obtained from Versit+ were compared with the measured values by VOEM. For the diesel vehicles, the average correlation for CO2 is 0.80, 0.66 for NOx and 0.24 for PM. The poor correlation of PM amongst other is due to the fact that Versit+ also takes into account non-exhaust PM emissions, while VOEM included only exhaust PM. For the lone gasoline vehicle, the correlation of CO2 is 0.89 and that of NOx is 0.31. No PM measurements were recorded by VOEM for any gasoline vehicle since the levels are expected to be very low.

c. Pro and contra

The biggest advantage for the model is that it is based on a very large database. It takes into account the complex emission behavior of modern light duty vehicles with advanced exhaust systems. A division into 80 vehicle categories makes the statistic model more reliable than other (American) statistical models. The traffic stream and the speed profile are input parameters that can be obtained from the chosen traffic model such as Paramics or VISSIM. However, the accuracy of the emission predictions that can be obtained with Versit + is theoretically not as high as for instantaneous engine based models, since the dependence of emissions on past engine states is only included implicitly via the selected region in the speed - acceleration plane. The accuracy provided by this model seems still sufficient to test decisions regarding traffic management on their environment-friendliness at micro scale as will be shown in Section 4.3. Based on the way the model works, it can be expected that speed based emissions can be predicted very well, but the emissions that depend strongly on engine dynamics can most probably not be predicted very accurately with this emission model.


3.1.12. Overview of the different models

a. American models

Since there is a substantial difference between models based on an American fleet and models based on a European fleet, these have been summarized in different tables. A summary of the American models is given in Table-2.

When looking at the database all models are based on measurements of American vehicles that date from before 1997. CNEM provides the largest database, and its database was used for the development of EMIT (partly) and Poly as well. However American vehicles have different properties than European ones. For example the majority has an automatic transmission, diesel engines are rare and fuel consumption is higher due to lower oil prices. This is certainly the case for older vehicles as used in this database.

The division of the car fleet into different categories was done according to different criteria, where the Euro standards obviously have not been taken into account. Generally, the larger the database, the more categories with a significant number of measured vehicles, and more accurate the model will be.

None of the American models predicts PM emissions, except for the extension of VT-Micro that was done by North (2006). This is a problem since PM prediction currently is a very important health issue.

The steady state approach is statistical for all models except CMEM. When using a statistical approach the correctness of a model depends mainly on the relevance of its database. Since the American database upon which the models are based is not relevant in Europe, their applicability seems to be rather low. All statistical models use vehicle speed and acceleration to predict emissions, which makes it hard to correctly describe changes in engine behavior. CMEM is using an analytical approach and may be better suited to explain emissions from engine behavior.

Except for CMEM, none of the American models is able to correct emission values for transient behavior. Everything is based on steady-state emission measurements, which can lead to wrong results for cars with 3 way – catalysts. Only Poly makes an effort by including situations from the past into the model. In CMEM it is not clear how the transient effects have been modeled. As can be seen in Table-2,none of the models shows a good correspondence with reality. Validation results should be interpreted very carefully since they are often conducted by the same people that developed the model. The term internal validation that is used in table 2 refers to the fact that the cars and/or the driving cycles used in the validation were the same as the ones used to develop the model. This results normally in better results than for an independent, external validation. When looking solely at the external validation results VT-Micro seems to be the best choice. However one should point out that this external validation was only done for 1 vehicle, and thus the error depends on the extent to which this car deviates from the average car in its category in the respective models. Moreover the resulting error for VT-Micro is still very high.


CMEM EMIT VT-Micro /North

Poly

Vehicles in the database

32 20(static)

+5(dynamic)

3 3200

Number test cycles

3 3 17 / 6 3

Categories 26 2 7 /1 41

PM modeled? No No No /YES No

Frequency 1Hz 1Hz 1Hz 1Hz

Steady state approach

Analytic modeling of the engine

Statistic regression on

speed, acceleration


speed, acceleration


speed, acceleration

Transient

correction

`Used in developing the

equations`

No

No

Takes time steps of 10 sec earlier

into account

Cold start correction

Yes No No No

Validation

Internal: avg. error 67% (by

Poly)

External: avg. error 86 % (by VT-Micro), avg. error 300% (by

Tate)

Internal: avg. error 22%, 83% for HC

External: avg. error 82.3%

(by VT-Micro)

Internal : avg. error 8%, 17% for HC, 11% PM

External : avg. error 47%,

17% for specific vehicle

Internal: avg. error 58%

External : none

Table 2: Summary of American instantaneous emission models

b. European models

Most American models suffer from the same problem: an outdated database and no or too few attention to the necessary corrections for transient behavior. The statistical approach also makes the models sensible to changes in the fleet. In the European models another approach is chosen: emissions are stored in a matrix based on engine parameters, with transient corrections. The two models except VETESS that are most interesting for implementation in traffic simulation software are EMPA and PHEM. In Table 3 the properties of these 3 models have been summarized. The first European model, Modem, has not been included since it doesn’t contain any transient corrections and is thus dated compared to its successors. The letter D in Table 3 stands for Diesel, G for Gasoline.


PHEM EMPA VETESS VERSIT

Vehicles in the database

32 20(static)

+5(dynamic)

3 3200

Categories Euro-0, 1,2,3,4; G+D

Pre-Euro-1D; Euro-2D; Euro-

3G

Euro-2D; Euro-3D; Euro-4G

Euro-2,3,4 5;

PM modeled? YES (scaled HC) No Yes Yes

Emission Measurement

1Hz, Model for gas transport in 4

stages.

10Hz, Model for gas transport in

3 stages.

1Hz , correction for gas transport.

1 Hz , No correction

for gas transport

Transient

correction

Empirical correction matrices

Derivative of the manifold

pressure.

Change of torque, at

constant speed.

No

Cold start correction

Yes No No No

Validation Internal: Euro-3 G, very good agreement, except PM.

External: Euro-2 D, very good agreement, except NOx.

Internal: static model error till

35% (B), dynamic model

error < 5%.

External: Euro-3 D : CO2, NOx

10% error ; HC, CO no

numbers given.

Internal: Diesel error < 20 %, gasoline bad.

External: very bad agreement

between prediction and

reality.

External: Good

agreement, except for

PM.

Availability Yes (“payment”) Yes (“payment”)--

Yes Yes

Table 3: Summary of European instantaneous emission models

As can be observed in Table 3 there is a substantial difference regarding the used database. PHEM is the most extensive model, since it is based on the largest database. PHEM also represents all 3 Euro classes for diesel as well as gasoline. EMPA and VETESS are restricted to a limited number of available categories, and fewer cars have been used for each category. EMPA does not include Euro-4 vehicles. An important remark is that a subdivision with Euro category as a single parameter may still result in substantial differences between vehicles of the same category. Parameters such as engine capacity, transmission system, injection method, etc. have not been taken into account for the categorization of the fleet in any of the presented models. An important factor is the extension of the model to heavy duty traffic. This is possible in PHEM since this was originally developed for trucks. VETESS also has been validated for buses.

When predicting exhaust emissions, the most important for human health seems to be PM, which is included in the model for PHEM and VETESS, but not for EMPA. This is an important disadvantage for EMPA. When comparing PHEM and VETESS, the latter seems to be better for PM predictions, since PHEM doesn’t use instantaneous data for PM


predictions. The data for the entire trip are known, and the instantaneous HC data are rescaled using the total emissions of PM and instantaneous HC emissions.

The measurements upon which the models are based have been corrected for variable gas transport time in all three models. EMPA is the only model that works on a 10 Hz basis. This implies better accuracy at the cost of more data. Some emission peaks may not be registered in the PHEM and VETESS models due to their short duration.

The steady state approach is similar in all European models. As can be observed in Table 3 all models use a matrix to interpolate emission values based on engine parameters. Yet substantial differences can be noticed in applicability. For example PHEM and VETESS have a gear shift model that recalculates the required input (speed profile) to the input parameters of the matrix (engine speed). The engine power is calculated as well using the resistances and losses that occur. For EMPA direct input of engine speed and torque is required, which makes the model a lot less user friendly.

The transient correction is a difficult problem that has been solved differently in all models. EMPA and VETESS have chosen one single parameter to correct for the dynamic behavior. The change of torque was chosen in VETESS whereas EMPA took the derivative of the manifold pressure since this signal has less noise. The results for the dynamic model are better in the EMPA case, but they did not simulate Euro-4 vehicles. Since these vehicles are more strictly regulated they emit less and thus they are harder to model. Not modeling Euro-4 vehicles can be an explanation why the EMPA validation results for gasoline vehicles are better than those for VETESS. In PHEM a different approach was chosen for the dynamic model. Six parameters with a significant influence on the difference between static model and measurements have been derived empirically. A disadvantage of this approach is that extension of the model becomes more difficult, since new parameters will have to be derived empirically when new vehicles enter the database. The dynamic PHEM model has not been developed for gasoline cars with catalyst since not enough parameters could be derived. However this is exactly the category where dynamic correction is most needed. Because of the lack of transient models for the Euro-4 gasoline category it is hard to compare validation results for the different transient models with each other. In any case, this is a difficult point that needs more research in all of the presented models.

Cold start correction is only available in PHEM, which gives this model an advantage over its competitors. To make use of this advantage cold starts should be included in the traffic simulation model. This is easy in micro simulation models such as Paramics.

Regarding validation, the best results have been achieved by the EMPA and PHEM model. For EMPA very encouraging validation results for the dynamic model of Euro-3 gasoline cars were presented. The static model also performs satisfactorily. PHEM presented good validation results as well, but only figures were shown. The results for VETESS are less satisfactory. Regarding fuel consumption and diesel emissions of NOx and PM the model performs well (error 10-20%). For other applications such as gasoline vehicles or HC and CO for diesel the results are less satisfactory. Validation using other vehicles showed large variance between predictions and measurements. An important remark is that the bad results mainly occur when predicting emissions of the Euro-4 gasoline vehicle. The other 2 models do not have a dynamic model for this category.

An important aspect when choosing an emission model is its availability. Since EMPA will only be updated and available for research from 2009, this model is not an option since the work in this part of the project is scheduled from January 2008. VETESS is available free of charge since it has been developed in house, due to the cooperation of VITO in the project. PHEM is available through a cooperation project that should be set up with TU Graz. It is expensive; so additional budget will be needed.

With the available knowledge, an emission model now has to be chosen. Bearing in mind the fact that no PM is modeled and the late availability, EMPA can be excluded. When choosing between PHEM and VETESS, PHEM seems to be the better option due to its larger database, its correction for gas transport time and its better validation results.


VETESS however is available in house, and uses more reliable instantaneous measurements for PM.

4.2 Larger scale exhaust emission models

4.2.1 Introduction

An alternative for the models that predict emissions on a second-by-second basis are the so-called traffic situation based models. These models try to combine the 3 levels for traffic modeling (micro, meso and macro) as efficiently as possible within the same framework. This is important because the different levels of the emission models will then be compatible with each other for a larger scale project. The most detailed level of those models, the micro level, is not as detailed as it would be for instantaneous models. With the term micro level these models refer to the emission on a certain traffic situation during 10-15 minutes. It should not be confused with the term microscopic traffic model such as Paramics, which is a model that predicts the traffic situation at a second-by-second level, and does not predict any emissions without additional plug-in.

Since these traffic situation based emission models also use a speed profile that can be obtained from micro traffic simulations as an input, they can be treated as a realistic alternative for instantaneous emission models for a few of the applications envisaged. A necessary condition is that their accuracy on small scale is good enough to test decisions regarding traffic management on their durability. The two most evolved of these larger scale models are described here.

4.2.2 MOVES (Koupal et al, 2005)

a. Operation

MOVES is an American project developed by the U.S. EPA, the Environment Protection Agency. The data upon which this model has been based were collected from on-road measurements. This means that the measurements have not been conducted in standard test conditions. A matrix based on speed and VSP (Vehicle Specific Power) is used. The holes in the matrix are filled with extrapolation or calculated out of an estimation of the fuel consumption. This last method is applied where not enough measurements are available for extrapolation and this method is analogous to the method used in CMEM. Correction functions for airco use and cold start have been added afterwards. The model can thus be perceived as the American version of the European matrix-based models, but on a larger scale (not instantaneous).

There is a subdivision into categories dependant on fuel, engine technology, age, weight and engine capacity. Apart from the standard emissions, CH4 and N2O are modeled as well. The model can be applied to predict emissions in traffic simulation on macro, meso and micro scale. An important remark is that micro scale has been defined as emissions on a certain position in traffic (street,..) during 15 minutes. Traffic light and other crucial micro scale traffic situations are only included in the model as an average increase in emissions. Thus many aspects of traffic management cannot be investigated using this model.

b. Validation

For the validation of the method 20% of the available road trips were used as a validation sample, and the predicted emissions were compared to the measurements. This can be seen as an internal validation since the same cars were used as the ones that drove the trips used to develop the model. All errors were lower than 5%, but trips with a low average velocity generally showed a larger error than trips with a high average velocity.


c. Pro and contra

The validation gives relatively good results. The method is however based on measurements of emissions of vehicles in real-life traffic in the US, which can lead to wrong results when applied in Europe. Analogous to the other American models, neither the fleet nor the driving behavior upon which the model is based are comparable to the European situation. Another disadvantage is the lack of accuracy. Input from Paramics as second-by-second speed profiles is possible, but the output is not detailed enough to justify any decisions regarding emissions due to traffic management on micro-scale. The model was meant for the American market to replace Mobile 5, which is an emission model implemented in macro-scale traffic models. The model will later be available free of charge for research (currently only a demo version is available).

4.2.3 COPERT (EEA, 2009)

a. Operation

COPERT is a software program that calculates emissions from road transport (and off-road sector) as a function of speed. The software has emissions functions based on vehicle speed for each of the Euro classes, based on fuel types and vehicle sizes. The pollutants that are calculated include both regulated (CO, NOx, VOC, PM, CO2) and unregulated (NO, NH3, SO2). Apart from the pollutants, COPERT can also compute fuel consumption. The European Environment Agency (EEA) maintains the state of art inventory of all atmospheric emissions and COPERT has been the most reliable tool for accessing these emissions inventories. The emissions functions of COPERT has been used throughout Europe for maintaining transport emissions inventories on regional and national scale.

b. Validation

The COPERT model was evaluated by using data from on-road remote sensing emission measurements on a large number of vehicles at three different sites in Gothenburg, Sweden. The data contained fuel-specific emissions of CO, NO and HC as well as speed and acceleration data for all the vehicles. For gasoline passenger cars, a total of approximately 20, 000 records with valid CO and HC remote sensor readings, and 16 000 records with valid NO readings were available for the COPERT III evaluation. For diesel passenger cars and heavy-duty vehicles the remote sensing data contained 1100 and 650 records with valid NO readings, respectively. Average fuel-specific emission factors derived from measurements were compared with corresponding emission factors derived from COPERT III calculations for urban, hot stabilized conditions and average speed around 45 kmph. The results show a good agreement between the two methods for gasoline passenger cars' NO, emissions for all COPERT III sub sectors (i.e. cylinder volume classes) and technology classes (e.g. EURO 1, 2, 3). In the case of CO emissions, the agreement was less favorable, with the model over-predicting emissions for all but one of the technology classes. For gasoline passenger cars, HC the model prediction was reasonably good, although with a slight tendency for the model to over estimate the emissions. There was also a relatively good prediction for NO for diesel passenger cars. Finally, NO, emission factors for heavy-duty vehicles according to the COPERT III model were systematically lower than those from the remote sensing measurements, and in particular the reduction between EURO 2 and EURO 3 tended to be overestimated by the model. The study has demonstrated the potential and usefulness of on-road optical remote sensing for emission model evaluation purposes. No validation of COPERT IV was published yet.

c. Pro and Contra

The biggest advantage of COPERT is that it is very easy to use. The polynomial functions are available for each of the vehicle classes, fuel types and euro classes. It is very useful for large scale inventory level modeling for total cycle for a trip. However, the biggest


drawback with COPERT is that it uses speed as the only variable to estimate emissions. Hence, with COPERT it is not possible to accurately measure the instantaneous emission responses such as acceleration, driving behaviors and driving conditions. In other words, dynamic emissions cannot be reliably predicted using COPERT.

4.3 Instantaneous noise emission models

4.3.1 Introduction

In the framework of traffic noise prediction and noise mapping, only the level of noise emission is of interest (total or in spectral bands). All vehicle noise sources are considered to be incoherent; this makes it possible to separate noise emission and propagation calculations. Vehicle noise emission not only depends on vehicle parameters such as the driving speed, acceleration, gear etc., but also on the type and age of the road surface. This is a major difference with particle emission, and has as a consequence that measurements are difficult to realize with vehicles driving on a test bank.

Noise emission models have evolved from a traffic flow based to a single vehicle based approach. The former models try to predict the traffic noise source power, based on average flow parameters. The minimum information needed is the traffic intensity and the average vehicle speed for the main vehicle categories and for each period of the day. Roads are divided into segments, which are made small enough (usually not smaller than 10m) so that it can be assumed that the traffic noise emission level does not vary (or only very little) inside a segment; these sections are considered to be acoustically homogeneous. Note that this does not hold true in the vicinity of intersections, for which corrections have to be applied. The main advantage of this approach is that emission coefficients can be estimated using statistical pass-by (SPB) measurements along existing roads.

Single vehicle based emission models however treat each vehicle as a moving noise source, consisting of a number of sub-sources, located at different heights above the road surface. The strength of these sources may depend on various vehicle related parameters as well as on road surface parameters; usually a semi-analytical approach is used. Single vehicle based emission models have the advantage that they can be naturally combined with point-to-point propagation models.

Historically, noise mapping standards have been a national affair, with most countries having their own regulations and noise emission and propagation models. As a consequence and in contrast to most particle emission models, noise emission models are mostly published and free to use. During the past decade, a considerable amount of effort has been spent at harmonizing noise emission models across Europe. The two main European research projects coordinating this effort were Harmonoise and its follow-up project Imagine (www.imagine-project.org). The Imagine model is partly based on noise measurements made in the framework of the Nord2000 project (Kragh et al, 2001). The Nord2000 model for the Nordic countries was the most elaborate in the pre-Imagine era. Therefore both the Nord2000 and the Imagine model will be discussed.

4.3.2 Nord 2000 (Kragh et al, 2001)

a. Operation

Nord2000 was the first single vehicle based noise emission model, developed in the framework of a major revision of the Nordic environmental noise prediction model. The goal of this reference model was very ambitious: the model would provide a complete separation of tyre/road noise, engine noise and aerodynamic noise, which is only significant at high speeds and is usually negligible for road vehicles; 5 vehicle categories were proposed, some of which have sub-categories; 8 main road categories were


proposed; corrections for 6 different types of driving conditions were outlined. The model is based on 2 data sets of test track measurements. The primary set of measurements was performed in Denmark and consists of about 3000 car samples and 1000 truck samples. The secondary set was performed in Sweden and consists of about 800 car samples and 200 truck samples.

b. Pro and contra

Although based on a substantial amount of measurements, only data for 3 main vehicle categories were published, for one road surface (typical Danish road surface), and one driving condition (cruising). Furthermore, it is proposed to use only a single directed noise source for each vehicle. A major shortcoming is that only vehicles with a speed of at least 30 km/h can be modeled, as a consequence of the measurement method used. Together with the fact that acceleration corrections are not available, this model is of rather limited use for traffic noise prediction in urban area. However, up to 2004, it was the only single vehicle based emission model publicly available.

4.3.3 Harmonoise and Imagine (Peeters and van Blokland, 2007)

a. Operation

These models were developed in the framework of the Harmonoise and Imagine projects5. Imagine is a fine tuned version of the previous Harmonoise model. The model is based on extensive measurement campaigns performed at several locations in Europe, including statistical pass-by measurements and close-proximity (CPX) measurements carried out on vehicles in real traffic. Rolling noise (combined with aerodynamic noise) and propulsion (engine) noise are effectively separately modeled. The base model holds for the reference condition of a vehicle cruising at constant speed on a (virtual) reference road surface, with an air temperature of 20°C. This makes it possible for different EU member countries to develop corrections for specific road surfaces.

Model coefficients are published in one-third octave bands, ranging from 25 Hz to 10 kHz, and for 3 main vehicle categories: passenger cars, medium heavy and heavy vehicles. A further subdivision of the heavy vehicle class is made using a correction for the number of axles. 80% of the rolling noise sound power is assigned to a point source at a height of 0.01m above the road surface, and 20% is assigned to a point source at 0.30m (passenger cars) or 0.75m (heavy vehicles) above the road surface; the opposite is true for the propulsion noise. Furthermore, corrections on the rolling noise are given for different air temperatures and road surface types, for surface age and wetness, and for the use of studded tyres. Corrections on the propulsion noise are given for accelerating/decelerating vehicles; corrections for different driving conditions are therefore not necessary. To take into account the screening of the car body and the horn effect of tyre/road noise, each point source also is assigned a specific frequency dependent horizontal and vertical directivity.

b. Pro and contra

The imagine project provides much better data than the Nord2000. The initial limitations have been improved and the model has been extended with different sorts of possible corrections. Imagine can therefore be considered the best instantaneous noise emission model currently available. The model is available free of charge for all EU members. A possible disadvantage is the lack of a gear shift model. However a correction for accelerations is included, that can take the influence of an aggressive driving style on the noise emission into account.


5. CA S E S T U D I E S

5.1 Introduction

5.1.1 Background

When an emission model is chosen, this has to be implemented in microscopic traffic simulation software such as Paramics. Using this software with an emission plug-in, the possible improvement in emission and noise production by road vehicle streams that can be gained by suitable traffic management and planning can be investigated. The implementation in Paramics of all emission models presented in chapter 3 would be an extensive task. Therefore the advantages of this approach are first investigated in some case studies.

De Coensel et al (2005) have implemented the Harmonoise model, described in section 3.3.3, in Paramics. Vehicles are simulated individually as cellular automata in the micro simulation model. With each vehicle, a noise emission module is associated, which constructs its noise emission sources at each time step. The resulting output after simulation is a set of time-varying noise emissions. To study the applicability of this approach for exhaust emissions, this emission plug-in has been extended to exhaust emissions. For this purpose equations that yield exhaust emissions as a polynomial function of instantaneous speed and acceleration are needed. In the framework of the Mobilee project (De Nocker et al, 2005) regression functions for exhaust emissions were elaborated based on on-road exhaust emission measurements. These regression functions have now been implemented in the Paramics emission plug-in. The regression functions only use the vehicle speed and vehicle accelerations as parameters. They are thus not able to take into account the influence of (transient) engine behavior or catalyst function. Therefore they are far less sophisticated than the exhaust emission models presented in chapter 3. However they can be used as an indicator for the modeling possibilities that an exhaust emission plug-in in Paramics can provide.

To obtain reliable predictions that enable investigation on the influence of traffic management on emissions, three important conditions regarding the used model need to be fulfilled.

First of all the used emission model should approximate reality as good as possible. The noise emission model that will be used is Harmonoise, which already has been implemented in Paramics. A lot of attention has been spent on the selection of an appropriate exhaust emission model in the previous chapter. In a first case study, the exhaust emission predictions of Mobilee and VETESS are compared. This way the advantages of the models presented in chapter 3 over simple statistical regression functions can be shown.

A second important condition for the success of this study is that the traffic simulation software should simulate the traffic behavior in a realistic way. The behavior of the traffic stream in a simulation will influence the resulting emissions. The correctness of the traffic simulation software Paramics is investigated in a second case study. Here the emissions predicted by Mobilee combined with Paramics for a specific modeled traffic situation are compared with the emissions predicted by Mobilee combined with actual speed and acceleration measurements on one vehicle in this specific traffic situation.

The third condition is that the used combination of traffic simulation software and emission model is sensitive enough to enable an investigation on the influence of traffic management on emissions. This sensitivity is tested in a third case study, where emissions resulting from normal and aggressive driving behavior are compared in VETESS.


5.1.2 Methodology

A part of Gent-Brugge, a suburban area near the city of Gent, was chosen for the case studies in Paramics. The area contains local streets with low and medium amounts of traffic, and a district road connecting the city of Gent with other suburban areas. The E17 highway crosses in the south. The area has a mainly residential function; road traffic and the daily life of the inhabitants are the main sources of emissions. A micro simulation network of the case study area was set up and calibrated. The micro model was then validated based on rides in the area. For this, a normal car was equipped with a speed meter and GPS. Acceleration data were calculated afterwards from the speed data. Two different trajectories through the network were chosen. The first trajectory represents the local traffic that has its destination in the study area. The second trajectory represents the sneak traffic through the area, from and to the city of Gent. Both trajectories were driven two times: once with a normal driving style and once with an aggressive driving style.

Figure 3: Two trajectories in Gent-Brugge area : local traffic (left) and sneak traffic (right)

5.2 Case study 1a: Performance of exhaust emission models

5.2.1 Methodology

In this first case study the Paramics micro simulation of the network in Gent-Brugge was not yet used. However the speed and acceleration data from the ride in the area have been used to calculate the predicted exhaust emissions with Mobilee and VETESS. It is useful to get an indication on how both emission models relate to each other. This knowledge can be used when using Mobilee emission predictions in a later stage of the investigations.

For this analysis the speed and acceleration data from aggressive driving in the first trajectory in the Gent-Brugge area were used.

In Mobilee, the curves for an average diesel and an average petrol vehicle are used to predict the instantaneous emissions corresponding to the aggressive drive. The same speed and acceleration data from the aggressive drive in Gent-Brugge are used as an input for VETESS. Since VETESS can only provide simulations for three vehicles, the simulations for a Euro-3 diesel vehicle (Skoda Octavia) and a Euro-4 gasoline vehicle (VW Polo) represent their respective vehicle categories.


5.2.2 Results

The exhaust emissions of VETESS and Mobilee correlate highly for CO2, but for other emissions they differ significantly. In Figure 4, a 3 minute extract of the CO2 exhaust is shown for a diesel vehicle on the aggressive Gent-Brugge cycle using both VETESS and Mobilee.

As can be seen, both signals have similar time behavior, where VETESS yields slightly higher values. In Table 4, the correlation coefficients as well as relative difference between VETESS and Mobilee predictions are shown for the average diesel and petrol vehicle. In this table the correlation coefficient is calculated as the ratio between the covariance of the VETESS and Mobilee emission rates and the product of their standard deviations. The relative average difference is calculated as the ratio between the average difference and the Mobilee average emission rate, where the average difference (g/s) is the difference between the total emissions of Mobilee and VETESS in the cycle (in gram), divided by the duration of the cycle (in seconds).

CO2 CO NOx HC PM

Correlation for diesel

0.90 0.00 0.77 0.05 0.88

Relative avg. difference for diesel

8% 92% 17% 80% 82%

Correlation for petrol

0.93 0.56 0.12 0.26 N/A

Relative avg. difference for petrol

6% 80% 88% 99% N/A

Table 4: Correlation coefficients and relative error between VETESS and Mobilee exhaust emission predictions for data from sneak traffic Gent-Brugge cycle, aggressive ride.

Since no on-road exhaust emissions were measured for the Gent-Brugge trip, it is not possible to tell which model is closest to reality. However one can observe that the CO2 emissions predicted by both cycles show a good correlation (>0.9) and a low average error (<10%). As can be observed from Table 4, the data from most other emissions than CO2 show almost no correlation and a very high relative average error. An exception is made for the predictions of NOx, which are acceptable for diesel vehicles. As can be seen in Figure 5, PM predictions for diesel show good correlation, but Mobilee predicts absolute values that are a lot higher than those of VETESS, resulting in a high relative error (Figure 6).

Other diesel predictions such as HC and CO emissions show no correlation at all. For example CO has a constant emission value in Mobilee, regardless of the input speed. This indicates the biggest problem of Mobilee: it only takes speed and acceleration as an input and cannot take transient effects into account. This is mainly a problem in the prediction of petrol engine emissions, which show even worse correlation with VETESS. This can be explained by the larger influence that transient effects have on these emissions. The relatively good results for CO2 and NOx should be tempered by the fact that the maximum speed reached in this city cycle is only 60 km/h. Mobilee emission curves were derived for speeds up to 70 km/h and hence emission obtained at higher driving speed is expected to be unreliable.


Figure 4: CO2 predictions for Mobilee and VETESS on sneak traffic, aggressive Gent-Brugge drive with a diesel vehicle.

Figure 5: NOx predictions for Mobilee and VETESS on sneak traffic aggressive Gent-Brugge drive with a diesel vehicle.

CO2 exhaust diesel Mobilee vs Vetess

0.0E+00

2.0E+00

4.0E+00

6.0E+00

8.0E+00

1.0E+01

1.2E+01

0 50 100 150 200 250 300

time (s)

CO

2 (g

)CO2 MobileeCO2 Vetess

NOx exhaust dieselMobilee vs Vetess

0.0E+00

2.0E-02

4.0E-02

6.0E-02

8.0E-02

1.0E-01

1.2E-01

0 50 100 150 200 250 300

time (s)

NO

x (g

)

NOx MobileeNOx Vetess


Figure 6: PM predictions for Mobilee and VETESS on sneak traffic aggressive Gent-Brugge ride with a diesel vehicle.

5.2.3 Conclusions

VETESS and Mobilee emission predictions show bad correlation for all petrol car emissions and for HC and CO emissions in diesel vehicles. The average difference between both models is very high for all predictions except CO2 and NOx for diesel. These differences are most probably due to inaccurate modeling in Mobilee, that doesn’t take transient effects into account. Nevertheless Mobilee provides a good correlation and a low relative difference with VETESS for CO2 predictions. Therefore CO2 predictions from Mobilee can be used as a reference to try out an emission plug-in for micro-simulations in the following case studies.

4.3 Case study 1b: Performance of VERSIT+ on standard drive cycle using VOEM

The accuracy of Versit+ is tested with the help of the measured data available from VITO’s On Road Emissions and Energy Measurement (VOEM) system. This data was collected from the tests conducted on four diesel vehicles and a gasoline vehicle. The diesel vehicles tested are Citroen Berlingo, Citroen C4, Nissan Patrol and Opel Vivaro, while the Volkswagen Golf is the only gasoline vehicle tested. It should be noted that the values of PM were not big enough to be measured for Citroen Berlingo, a diesel car which uses a DPF and gasoline vehicle, Volkswagen Golf.

The speed profiles of each of these vehicle tests are inputted into the Versit+, which gave the spatial continuous emissions predictions for CO2, NOx and PM. The spatial emissions are then converted into temporal emissions based on instantaneous vehicle speed data. The temporal emissions of CO2, NOx and PM on a second-by-second basis are then compared with the instantaneous VOEM data.

The drive cycle selected is MOL_30, a 30 minute cycle which is a combination of ten minutes of city driving, ten minutes of suburban driving and ten minutes of highway driving. This cycle is chosen because it is more representative of the real-world driving than NEDC (New European Drive Cycle). However for Volkswagen Golf, the lone gasoline

PM exhaust dieselMobilee vs Vetess

0.0E+005.0E-031.0E-021.5E-022.0E-022.5E-023.0E-023.5E-024.0E-02

0 50 100 150 200 250 300

time (s)

PM (g

)PM MobileePM Vetess


vehicle, the test data were available only from NEDC cycle and hence that has to be used for analysis.

The measured results from each of the vehicles are compared against the predicted results by Versit+. For example, the results for one vehicle, Citroen Berlingo tested on MOL_30 drive cycle with pure diesel were shown in Figure 7. VERSIT+ gives the spatial distribution of the emissions predicted. These spatial distributions are then converted into a temporal distributions based on the vehicle speed-time trace. These were plotted in Figure 7.1 along with the corresponding measured values. While the correlation of CO2 was the highest (at about 0.90), the correlation of NOx was around (0.80) and that for the PM was the lowest at 0.53. The other tests conducted on Citroen Berlingo gave similar results. It can be observed from the figure that the PM was predicted to be uniform by Versit+ during the periods of high acceleration and deceleration from 1200 to 1800 seconds. The poor correlation for PM is because the PM formation is highly complex and depends on several factors such as unburned fuel (in spite of high combustion efficiency of diesel engines) locally in some parts of the combustion chamber, engine transients and some specific fuel properties such as sulfur content, aromatic content and cetane number, etc. The other reason could be the use of Exhaust Gas Recirculation (EGR) which induces more complexity because EGR can reduce the amount of excess air that could result in incomplete burning of carbon soot particles. Moreover VOEM does not take into account non-exhaust PM whereas Versit+ does. Hence PM was predicted poorly.

The comparative results from all the tests conducted on all the five vehicles are presented (Table. 5 and Figure 7.2). It can be observed that the results for all the diesel vehicles showed some similarities with very good predictions of CO2 (Average R2 of 0.80), decent predictions for NOx (Average R2 of 0.66) and poor PM predictions (Average R2 of 0.24). The results from two of the tests were bad, maybe because of the misalignment of the data, which means that the data is not time-aligned. If those two bad tests were eliminated, the average correlation shoots up to 0.86, 0.72 and 0.32 respectively for CO2, NOx and PM for the diesel vehicles. For the gasoline vehicle, the average correlation of CO2 was 0.90 and for NOx, it is 0.29.


Figure 7.1: Comparison of the instantaneous data obtained from Versit+ with that of the measured

data for Citroen Berlingo.

0 200 400 600 800 1000 1200 1400 1600 18000

10

20

30 R2=0.93

time (s)

CO

2 (g

/s)

versit+voem

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0.05

0.1

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0.2

0.25 R2=0.8

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x (g

/s)

voemversit+

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0.005

0.01

0.015 R2=0.53

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(g/s

)

versit+voem


VOEM_Test ID Vehicle Fuel DriveCycle CO2 NOx PM

1 D07618b Opel

Vivaro Diesel Mol_30 0.54 0.41 0.01

2 D07618c Opel


3 D07618d Opel


4 D07618e Opel

Vivaro Diesel Mol_30 0.94 0.84 0.33 5 D07908cB Berlingo Diesel Mol_30 0.93 0.8 0.53 6 D07909d Berlingo Diesel Mol_30 0.94 0.78 0.47 7 D08319cNEDC Berlingo Diesel Mol_30 0.94 0.78 0.46

8 D08319dNEDC Nissan Patrol Diesel Mol_30 0.24 0.23 0.06

9 D08319eMOL Nissan Patrol Diesel Mol_30 0.93 0.86 0.32

10 D08319fMOL Nissan Patrol Diesel Mol_30 0.94 0.69 0.21

11 D08319gMOL Nissan Patrol Diesel Mol_30 0.73 0.43 0.13

12 D08515c_NEDC Nissan Patrol Diesel Mol_30 0.3 0.15 0.08

13 D08515f_MOL30 Citroen

C4 Diesel Mol_30 0.88 0.85 N/A

14 D08516b_MOL30 Citroen


15 D08A06dWNEDC Citroen


16 D08A08cWNEDC VW Golf

Plus Gasoline NEDC 0.89 0.31 N/A

17 D08A08dWNEDC VW Golf

Plus Gasoline NEDC 0.9 0.28 N/A

Table 5: Correlation coefficients between Versit+ predictions and the measured data from VOEM for various tests on a given vehicle. The fuel used and the drive cycle for each of the tests are also

presented.


Figure 7.2: Average correlation (R2) of the instantaneous data obtained from Versit+ with that of the measured data for different vehicles.

5.4 Case study 2: Accuracy of Paramics

5.4.1 Methodology

In this second case study the accuracy of Paramics is tested. Rather than to check the traffic model for instantaneous speed and acceleration, it was decided to validate the calculation by means of the final output: emissions. An emission plug-in is used with Harmonoise as the noise emission model and Mobilee for the exhaust emissions. Mobilee is used because it is available and easier to implement in Paramics than VETESS. Only noise and CO2 emission predictions in Paramics are calculated. CO2 emission predictions from Mobilee have shown to correlate with those from VETESS (see 4.2). These results can thus give an indication on the performance of future emission plug-ins using more sophisticated emission models such as VETESS or PHEM.

First the accuracy of Paramics for noise predictions is tested. When simulating the Gent-Brugge network in Paramics, every simulation step a different amount of vehicles is passing through the network. With each vehicle, a noise emission module is associated, which constructs its noise emission sources at each time step and at each location. Consequently a range of emission results is available at each time step. To evaluate if the traffic simulation of Paramics is realistic, the maximum and minimum emissions are plotted at the location where the test vehicle is at each time step as well as the average emission for all vehicle present in the simulation network at that specific location. The used time step in Paramics is 0.5 seconds. If Paramics provides an accurate reproduction of the actual traffic stream, the noise emissions based on the actual speed of the measured vehicle during the test drive should lie within the range of modeled vehicles.

For CO2 emissions a similar approach is used. The only difference is that now the CO2 emissions are calculated with the Mobilee emission functions instead of the Harmonoise functions that were used for noise calculations.

0

10

20

30

40

50

60

70

80

90

100

Ave

rage

R2 *1

00

Opel Vivaro Cittroen Berlingo Nissan Patrol Citroen C4 VW Golf Plus

Average correlation between VOEM and Versit+

CO2NOxPM


5.4.2 Results

a. Noise emission

In Figure 8, the results of the noise simulations are shown for the test vehicle performing an aggressive ride of the second Gent-Brugge cycle (normal traffic). A 3 minute period is highlighted from the cycle.

Figure 8: Paramics accuracy for noise emission on Gent-Brugge cycle for normal traffic, aggressive

drive

On Figure 8, the dark blue line shows a 3-minute time series of the calculated noise emission level of the car during a ride, and thus represents also the noise emission level of the car at various places along the path. These data have been calculated using Harmonoise with as input the instantaneous speed and acceleration of the test vehicle in real traffic. The acceleration has been calculated from the measured speed. The purple line shows the mean value of the noise emission of all simulated cars that pass on the same place along the path in Paramics. These data are an average of all noise emission data that have been calculated using Harmonoise with as input the simulated speed of every single vehicle simulated in Paramics. The yellow lines and light blue lines finally show the minimum and maximum emission levels that were encountered for a simulated car along the path.

The first part of the time series represents a wide stretch of road were the speed limit is 70 km/h. Mostly the ride emission is within the simulation limits. Following that part, there is a large junction, where the path takes a left turn, so the car has to slow down and stop one or more times. The last part is a more urban like stretch of road where the speed limit is 50 km/h. The start and the end of the plotted time series are shown in Figure 8. The blue peaks beneath the lower yellow line are explained by the fact that the car during the ride had to slow down for an obstacle such as a person crossing the street, a car parking, or because a parked car is blocking part of the street, and one has to slow down for an oncoming car. These situations are not accounted for by the micro traffic model.

Paramics accuracy : noise emission

75.0080.0085.0090.0095.00

100.00105.00

120 170 220 270

time (s)

nois

e (d

B)

noise ride noise sim_meannoise sim_minimum noise sim_maximum


b. CO2 emission

In Figure 9, the results of CO2 simulations are shown for a vehicle on an aggressive ride of the second Gent-Brugge cycle. A 3 minute period is highlighted from the cycle.

Figure 9 : Paramics accuracy for CO2 emission of a petrol vehicle on Gent-Brugge cycle for normal

traffic, aggressive ride.

All lines in Figure 9 have similar meaning as in Figure 8. The dark blue line stays within the yellow and light blue boundaries and matches the purple line, which stands for the mean vehicle in Paramics, closely. However the dark blue line that represents the CO2 emissions calculated with Mobilee using the measured speed and acceleration in some cases exceeds the maximum CO2 emission from Paramics. When looking more closely, this mostly happens after a CO2 peak: the maximum in Paramics drops fast, while the emission peak from the real measurements stays on a higher level for several seconds. This happens 4 times in the plotted time cycle, each time corresponding to the time period following a sharp corner or a traffic light (see Figure 10 ). Since CO2 emissions for this case study are calculated with Mobilee, the only parameters with an influence on emissions are velocity and acceleration. The CO2 emissions based on the measurements stay on a high peak level for a longer time than the Paramics based emissions at those situations where acceleration is assumed to have an influence on the emissions. Therefore it is reasonable to assume that the simulated vehicles in Paramics might accelerate too fast after a deceleration to reach their cruising speed. The vehicles in the simulation thus reach their cruising speed, and the corresponding lower emission level, faster than the measured vehicle. This accuracy error in Paramics can however be assessed easily by changing settings in Paramics.

Paramics accuracy : CO2 emission petrol vehicle

0

5

10

15

120 170 220 270

time(s)

CO

2 (g

)

CO2 ride CO2 sim_meanCO2 sim_minimum CO2 sim_maximum


Figure 10: Locations where CO2 from measurements exceeds maximum simulated CO2 emission (Paramics) for local traffic trajectory traffic (red dots), aggressive ride.

In the second part of the ride, the effect of slowing down for an obstacle such as a person crossing the street, a car parking, or because a parked car is blocking part of the street and one has to slow down for an oncoming car that was observed in the noise emission is also present. In contrast to the noise results, now the CO2 peak just after stopping is more detectable than the reduced emission during stopping or slowing down. This difference is also due to the use of the dB scale for noise.

5.4.3 Conclusions

The results in this case study show that a micro traffic simulation program such as Paramics is able to simulate the traffic stream in such a way that the predicted emissions for the average Paramics car match the predicted emissions for a car on the real circuit closely. The emissions of the real vehicle stay within the maximum and minimum emissions in Paramics, except for some cases. These cases can be identified as situations where the car in actual traffic had to slow down due to unforeseen circumstances, which were not included in the Paramics model. In future applications of micro traffic models in this project, care will be taken to include as much as possible these situations. In the CO2 case one can observe that Paramics in some cases accelerates too fast after traffic lights or turning roads, resulting in too short duration for emission peaks compared with the emissions based on speed measurements. This can however be assessed by changing the settings in Paramics. The conducted case study made use of Mobilee emission functions for the CO2 prediction and Harmonoise for the noise prediction. It shows that a micro-traffic simulation model such as Paramics can provide an accurate traffic simulation upon which reliable emission predictions can be based, if the used emission model accurately models reality.


5.5 Case study 3: Sensitivity of the models

5.5.1 Methodology

The eventual goal of the implementation of an instantaneous emission model into a micro traffic simulation model such as Paramics is to be able to assess the impact of traffic management on emissions. Similarly measured results obtained from VEDETT will be used to study dependence of emission on local typology. It is therefore necessary that the used emission models are sensitive enough to indicate differences in emissions before and after traffic management decisions have been implemented.

The sensitivity of the instantaneous emission models can be tested in several ways. In a first test, the trajectories in Gent-Brugge were driven in an aggressive and a normal way. The corresponding speed data were used as input in VETESS for CO2 emissions and in Harmonoise for noise emissions. The difference in emissions between an aggressive and a normal cycle can thus be investigated. In order to provide a clear overview the emission levels for noise and CO2 have been plotted along the trajectory with a color code indicating their amplitude. This way the differences induced by aggressive and calm driving can be compared visually. In the input for VETESS, the gear shift model was set to aggressive driving for the aggressive cycles. This setting enables the model to correctly simulate the change in gear shifting behavior that corresponds with the different input data.

5.5.2 Results

a. Noise emissions

As can be seen in Figure 11 Harmonoise is sensitive for changes in driving behavior. The noise emissions change at the places of high velocity and acceleration due to an aggressive driving style.

With an aggressive driving style noise emissions rise to a higher level. Extra noise emissions are mainly produced at the parts in the trajectory where extra speed can be gained, such as the district road, with a speed limit of 70 kmph, and the other straight roads in the network. This shows the speed dependency of the noise emissions. The overall difference between aggressive and normal driving in the local traffic and sneak traffic is quantified in Table 6. The aggressive driving style resulted in a time gain of around 5 minutes for both types of traffic, and an increase in average noise emission of up to 2.5 dBA. The total noise emission over the whole trajectory was raised with at most 1 dBA. Thus Harmonoise seems to be sensitive enough to show the influence of a change in driving style on the noise emissions.


Figure 11: Sensitivity of Harmonoise: Noise emissions for calm (left) and aggressive (right) driving on the Gent-Brugge trajectory for local traffic

Duration

Avg. noise emission

per second: LW (dBA)

Total noise

emission: SEL (dBA)

Avg. CO2 emission

per second (g)

Total CO2 emission

(g)

Local traffic, normal drive

12:15 91.7 120.4 1.37 1008

Local traffic, aggressive

drive

08:52 94.3 121.6 2.81 1497

Sneak traffic,

normal drive

24:31 91.9 123.5 1.66 2438

Sneak traffic, aggressive

drive

19:37 93.6 124.3 2.60 3058


Table 6 : CO2 and noise emissions for aggressive vs. calm driving

b. CO2 emissions

As can be observed in Figure 12, VETESS is also sensitive for a change in driving style. In contrast to the noise emissions in Figure 1, the extra CO2 emissions are mainly produced at corners, or places where the direction of the road changes. This shows that CO2 emissions don’t depend on speed alone, but also acceleration and other parameters play an important role.

Figure 12 : Sensitivity of VETESS: CO2 emissions for calm (left) and aggressive (right) driving on the Gent-Brugge trajectory for sneak traffic.

Again the overall differences are quantified in Table 6.. Aggressive driving produces up to 1.5 gram CO2 extra per second, which is an increase of more than 100% for the sneak traffic trajectory. The total CO2 emissions increase for both trajectories with about 500 g. This is a significant amount given the original CO2 exhaust of only 1008 grams for the sneak traffic cycle. Thus VETESS is sensitive enough to show the influence of a different driving style on the produced CO2 emissions.

5.5.3 Conclusions: Model Sensitivity

VETESS and Harmonoise both seem to be sensitive enough to show the difference in emissions induced by aggressive driving versus calm driving. Both noise and CO2 emissions increase significantly with a more aggressive driving style. This can be seen both visually on the map as numerically due to an increased average and total emission. Furthermore the places where extra emissions are produced correspond to what can be expected based on the origin of the emissions as explained in section 3.1 . Noise emissions are higher on parts of the traffic where higher speed can be reached, whereas CO2 emissions rise mostly at places where the ride changes direction and near intersections. Therefore Harmonoise and VETESS, or another instantaneous emission model presented in chapter 3, combined with a micro traffic model such as Paramics or measurements of speed and acceleration using a system like VEDETT are sensitive enough to predict the effect of traffic management on emissions.


6. CO N C L U S I O N S

Several emissions models were considered for this study, however the American models were discarded since they were based on a fleet that is totally different from the European in fuel, transmission and fuel used. This difference is so important that dependence on speed and acceleration would not be representative for the Flemish situation. The models based on European fleet are PHEM, EMPA, VETESS, Mobilee, COPERT IV, and VERSIT+. Although VETESS seems a promising model and it is available in house, it was rejected because it is based on only three vehicles. Mobilee although already implemented as a plug in for Paramics and available in house, is also limited in the number of cars it is based on and moreover these cars are not very recent. Hence this model is also rejected. COPERT is usable only for total cycle emissions and thus cannot be used for most of the traffic management alternatives that will be analyzed.

EMPA does not include PM emissions which was one of the necessary criteria for model selection. Also EMPA is based mostly on American fleet, only five European vehicles were considered. Moreover, no independent external evaluation of EMPA is published yet.

This narrows our choices to PHEM and VERSIT+.

Being an engine based model, PHEM would theoretically be the model that allows to scan the widest range of traffic management alternatives. However PHEM is based on a database of about 32 vehicles and 5 vehicles per category. Given the large variability in emissions between vehicles of one Euro category, this is barely sufficient. Hence, there are not enough data available to fill the matrix positions in European fleet for road gradients higher than 6%. Just like for EMPA an independent reliable validation has not yet been published. This problem gets more compounded if the vehicle tested is using an emission reduction technology such as PM filters or after-treatment. Due to the complexity of the model and the fact that it has not been made commercially available, it is not very user-friendly.

VERSIT+ is based on database of 3,200 vehicles and about 80 vehicle categories and the model is totally based on European fleet. It also considers the categorical division based on size, fuel, Euro class and use of emission reduction technologies. The external validation of the model is done using the data collected by VITO and the results showed quite good agreement. Furthermore, VERSIT+ is a user-friendly calculation tool that is commercially available. Moreover VERSIT+ has been combined with VISSUM – a micro traffic simulator – by its producer. Based on these considerations and the limited amount of time and resources that can be spend on learning and tuning a more complicated model, VERSIT+ was selected as the best candidate for future research in WP8.3.

It should be noted that neither of above mentioned models are suitable for estimating emissions from vehicles with new technologies such as (full) hybrid electric vehicles. Hence this cannot be a decisive factor in choosing the model for the study. The gasoline blending with ethanol and bio diesel blending in diesel have only been implemented in Belgium very recently. Also, we expect similar effects of traffic management measures using these blends compared to conventional fuels. In addition, the fraction of full hybrid vehicles on the road will remain small the coming 5 to 10 years (2020).

Furthermore, only Tank to Wheel emissions will be considered since the objective of the study focuses primarily on the localized environmental impact of emissions. Hence the offsite emissions involved in the fuel production are not relevant to the scope of this study.

The selection of a noise emission model is far more straight-forward. The Harmonoise/Imagine model is a state of the art model that is a candidate for European Harmonized model. It has been implemented and used together with Paramics successfully in several applications by INTEC-UGent.


7. FU R T H E R R E S E A R C H

Within the ‘Steunpunt’-project we will apply the tool box ‘Paramics, VERSIT+ and Harmonoise’ to analyse the effect of traffic management measures on air pollutants (PM2.5 and NOx), CO2 green house gas emissions and noise emissions. In addition, beside traffic flows from the traffic model, also traffic profiles obtained from our own on the road measurements with VEDETT (Vehicle Embedded Device for data-acquisition Enabling Tracking and Tracing) will be integrated. As a result typical infrastructural elements could be examined. The model and measuring results could also give input on indicators to evaluate safety (e.g. speed violation). These could be further interpreted by safety experts within the ‘Steunpunt’-consortium.

On the longer run the following activities are recommended to further the scope of the research and its applicability to traffic management:

• The localized impact of the traffic can be checked by using pollutant and noise dispersion models which can be interfaced with the emission model. This can help make further recommendations.

• The use of bio fuel blending, with different percent of blends can be integrated into the model.

• Currently hybrid vehicles or electric vehicles are negligible in the fleet mix, but in the future, there will be an increase in this mix. This can be a challenge for the researchers to adapt the model to accommodate these new additions.

• The non-exhaust particulate matter are now modeled by Versit+ as a part of PM emissions. These are the emissions that result from wear and tear of tires and brakes. In future, if these emissions can be isolated and modeled as non-exhaust emissions, that would be interesting.


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