Due to its impact on human health and the nature surrounding us, diesel engine emissions have been significantly reduced over the last two decades. This reduction has been enforced by the legislating organs around the world that gradually have made the manufacturers transform their engines to today’s complex high-tech products. One of the most challenging areas to meet the legislations is transient operation where the inertia in gas-exchange system makes transition from one load to another problematic.   Modern engines have great potential to minimize the problems associated with transient operation. However, their complexity also imposes a great challenge regarding optimization and systematical testing of transient control strategies in an engine test bed could be both expensive and time consuming.   The objective of this project is to facilitate optimization of transient control strategies. This should be done by identifying appropriate measurement methods for evaluation of transients and by providing models that can be used to optimize strategies off-line.   Measurement methods for evaluation of transients have been tested in several experiments, mainly focusing on emission but also regarding e.g. EGR flow. Applicable instruments for transient emission measurements have been identified and used. However, no method to measure soot emissions cycle resolved has yet been found. Other measurements such as EGR flow and temperatures are believed to have significantly decreased accuracy during transients.   A model for prediction of NOx emissions have been used and complemented with a new approach for soot emission predictions that has been developed in this project. The emission models have been shown to be applicable over a wide range of operating conditions with exception for highly premixed combustion. It has also been shown that models developed for steady state conditions can be used for transients operation.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

<p> </p><p>Due to its impact on human health and the nature surrounding us, diesel engine emissions have been significantly reduced over the last two decades. This reduction has been enforced by the legislating organs around the world that gradually have made the manufacturers transform their engines to today’s complex high-tech products. One of the most challenging areas to meet the legislations is transient operation where the inertia in gas-exchange system makes transition from one load to another problematic.</p><p> </p><p>Modern engines have great potential to minimize the problems associated with transient operation. However, their complexity also imposes a great challenge regarding optimization and systematical testing of transient control strategies in an engine test bed could be both expensive and time consuming.</p><p> </p><p>The objective of this project is to facilitate optimization of transient control strategies. This should be done by identifying appropriate measurement methods for evaluation of transients and by providing models that can be used to optimize strategies off-line.</p><p> </p><p>Measurement methods for evaluation of transients have been tested in several experiments, mainly focusing on emission but also regarding e.g. EGR flow. Applicable instruments for transient emission measurements have been identified and used. However, no method to measure soot emissions cycle resolved has yet been found. Other measurements such as EGR flow and temperatures are believed to have significantly decreased accuracy during transients.</p><p> </p><p>A model for prediction of NOx emissions have been used and complemented with a new approach for soot emission predictions that has been developed in this project. The emission models have been shown to be applicable over a wide range of operating conditions with exception for highly premixed combustion. It has also been shown that models developed for steady state conditions can be used for transients operation.</p>

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Yes / This paper introduces a hybrid dynamic modelling approach for the prediction of NOx emissions for a Diesel engine, based on a multi-physics simulation platform coupling a 1-D air path model (GT-Suite) with in-cylinder combustion model (CMCL Stochastic Reactor Model Engine Suite). The key motivation for this research was the requirement to establish a real time stochastic simulation capability for emissions predictions early in engine development, which required the replacement of the slow combustion chemistry solver (SRM) with an appropriate surrogate model. The novelty of the approach in this research is the introduction of a hybrid approach to metamodeling that combines dynamic experiments for the gas path model with a zonal optimal space-filling design of experiments (DoEs) for the combustion model. The dynamic experiments run on the virtual Diesel engine model (GT- Suite) was used to fit a dynamic model for the parameters required as input to the SRM. Optimal Latin Hypercubes (OLH) DoE run on the SRM model was used to fit a response surface model for the NOx emissions. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real time simulations of transient drive cycles to be executed. The performance of the proposed approach was validated on a simulated NEDC drive cycle against experimental data collected for the engine case study, which proved the capability of methodology to capture the transient trends for the NOx emissions. The significance of this work is that it provided an efficient approach to the development of a global model with real time transient modelling capability based on the integration of dynamic and local DoE metamodeling experiments.

Die Entwicklung eines Emissionsmoduls für primäres marines Aerosol (PMA; bestehend aus Meersalz und organischem Material) war Hauptgegenstand der vorliegenden Arbeit. Dieses wurde in das Chemie-Transportmodell „COSMO-MUSCAT“ eingebaut und löste dort das vorherige einfach gehaltene Modul (nur Meersalz) ab, welches entsprechend früherer Studien zu hohe Meersalzkonzentrationen berechnete. Das neue Emissionsmodul wurde umfangreich getestet und gegen die Messdaten von verschiedenen Stationen in Europa, einem Bernerimpaktor auf São Vicente (Kap Verden) und einem Aerosolmassenspektrometer sowie einem Digitelfilter während der Fahrt ANT-XXVII/4 des Forschungsschiffes Polarstern validiert. Bei den Untersuchungen kristallisierte sich die Emissionsparametrisierung von Long et al. (2011) als die am Besten geeignete für COSMO-MUSCAT heraus. Weiterhin wurde der Einfluss der Wassertemperatur an der Meeresoberfläche auf die PMA-Emission untersucht. Dabei konnte gezeigt werden, dass dieser Effekt insbesondere für größere Aerosolpartikel (2,5 µm < Dp) relevant ist. Die Nichtbeachtung der Temperaturkorrektur würde in diesem Größenbereich zu einer Überschätzung der Emissionsflüsse und folgend der Konzentration von PMA über kalten Gewässern führen. Beim erstmaligen Vergleich verschiedener Funktionen zur Beachtung des Temperatureffektes erzielte die Funktion von Sofiev et al. (2011) die besten Ergebnisse. Als weitere Neuerung wurde das mit dem PMA emittierte organische Material in das Emissionsmodul eingebaut. Auch hierfür erfolgten Vergleichsstudien verschiedener Parametrisierungen und Ansätze. Allerdings standen nur unzureichende Messungen zur Verfügung, da sie keine Aufteilung in primäres (mit PMA emittiert) und sekundäres (in Gasphase gebildet) organisches Material lieferten. Daher war eine Aussage zur Güte der Funktionen kaum möglich. Die Simulationen zeigten jedoch die Bedeutung der verschiedenen Ansätze zur Berechnung der Emissionsflüsse von organischem Material. So kann bspw. der Einfluss der Emissionsfunktion den Einfluss der Parametrisierung zur Aufteilung in Meersalz und organisches Material deutlich übersteigen. Letztlich bleibt die Frage der richtigen Eingangsdaten für die Emission von primärem organischen Material offen. Es zeigte sich, dass die Abhängigkeit der Anreicherung von organischem Material im PMA von der Chlorophyll a-Konzentration im Oberflächenwasser nicht zwingend gegeben sein muss. Daher ist es notwendig sie in der Berechnung der Emissionsflüsse durch weitere/andere Parameter zu ergänzen/ersetzen. Dies ist Gegenstand eines neuen Forschungsprojektes, bei dem das neue Emissionsmodul angewendet und weiterentwickelt wird.

In order to improve the air quality in Uppsala, a proposition to introduce one of two new emission zones (EZ), starting in the year 2020, has been proposed. In what is called Environment Zone Class 2 (EZ2), only cars that meet emission class Euro 5 and higher are allowed and in Environment Zone Class 3 (EZ3), only electric, fuel cell and gas vehicles are allowed. The purpose of this thesis is to examine how EZ: s would affect the air quality, regarding nitrogen oxides (NOx) and particles (PMx), within the zone of the city of Uppsala. Using the traffic simulation software PTV Vissim and the emissions modeling software EnViver, four scenarios have been created, two representing today's fleet of vehicles and two examining a modified fleet. Scenario 1 examines an exclusion of all non EZ2 vehicles (Euro 4 and lower) within the zone and scenario 2 examines an EZ2 solely on the road Kungsgatan. Scenario 3 and 4 examine an EZ2 and EZ3 where all cars that do not currently meet the requirements for each EZ are replaced with ones that do. The results indicate that all proposals, except scenario 2, lead to a reduction of NOx and PM2 within the zone. Scenario 1 shows a decrease by 51% for NOx and 57% for PM10, scenario 3 shows a decrease by 17% and 24% respectively and scenario 4 shows a decrease by 66% and 43% respectively. For scenario 2 the emissions show an increase by 10% and 7% each within the zone.