<p>The emission legislations and the new On Board Diagnostics (OBD) legislations are becoming more strict and making the demands on control and fault detection higher. One way to</p><p>control and diagnose the engine is to use a control/diagnose strategy based on physical models and therefore better models are necessary. Also, to be competitive and meet the markets demand of higher power, longer durability and better fuel economy, the models needs to be improved continuously. In this thesis a mean value model of the intake system that predicts the charge air temperature has been developed. Three models of different complexity for the intercooler heat-exchanger have been investigated and validated with various results. The suggested intercooler heat-exchanger model is implemented in the mean value model of the intake system and the whole model is validated on three different data sets. The model predicts the intake manifold temperature with a maximum absolute error of 10.12 K.</p>

Yes / The natural gas as an alternative fuel has economical and environmental benefits. Bi-fuel engines powered by gasoline and compressed natural gas (CNG) are an intermediate and alternative step to dedicated CNG engines. The conversion to bi-fuel CNG engine could be a short-term solution to air pollution problem in many developing countries. In this paper a mathematical model of a bi-fuel four-stroke spark ignition (SI) engine is presented for comparative studies and analysis. It is based on the two-zone combustion model, and it has the ability to simulate turbulent combustion. The model is capable of predicting the cylinder temperature and pressure, heat transfer, brake work , brake thermal and volumetric efficiency, brake torque, brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). The effect of engine speed, equivalence ratio and performance parameters using gasoline and CNG fuels are analysed. The model has been validated by experimental data using the results obtained from a bi-fuel engine. The results show the capability of the model in terms of engine performance optimization and minimization of the emissions. The engine used in this study is a typical example of a modified bi-fuel engine conversion, which could benefit the researchers in the field.

<p>Over the last years, the emission and on board diagnostics legislations for heavy duty trucks are getting more and more strict. An accurate engine model that is possible to execute in the engine control system enables both better diagnosis and lowered emissions by better control strategies. </p><p>The objective of this thesis is to extend an existing mean value diesel engine model, to include turbo compound. The model should be physical, accurate, modular and it should be possible to execute in real time. The calibration procedure should be systematic, with some degree of automatization. </p><p>Four different turbo compound models have been evaluated and two models were selected for further evaluation by integration with the existing model. The extended model showed to be quite insensitive to small errors in the compound turbine speed and hence, the small difference in accuracy of the tested models did not affect the other output signals significantly. The extended models had better accuracy and could be executed with longer step length than the existing model, despite that more complexity were added to the model. For example, the mean error of the intake manifold pressure at mixed driving was approximately 3.0%, compared to 5.8% for the existing model. The reasons for the improvements are probably the good performance of the added submodels and the systematic and partly automatized calibration procedure including optimization.</p>

<p>Because of new emission and on board diagnostics legislations, heavy truck manufacturers are facing new challenges when it comes to improving the engines and the control software. Accurate and real time executable engine models are essential in this work. One successful way of lowering the NOx emissions is to use Exhaust Gas Recirculation (EGR). The objective of this thesis is to create a mean value model for Scania's next generation EGR system consisting of a poppet valve and a two stage cooler. The model will be used to extend an existing mean value engine model. Two models of different complexity for the EGR system have been validated with sufficient accuracy. Validation was performed during static test bed conditions. The resulting flow models have mean relative errors of 5.0% and 9.1% respectively. The temperature model suggested has a mean relative error of 0.77%.</p>

Today's legislation on exhaust gas emissions for heavy duty diesel (HDD) vehicles is more stringent than ever and will be even more tough in the future. More over, in a few years HDD vehicles have to be equipped with OBD (On-Board Diagnostics). This place very high demands on the manufacturers to develop better engines and strategies for OBD. As an aid in the process models can be used. This thesis presents extensions of an existing diesel engine model in Matlab/Simulink to be able to simulate emissions during standardized european test cycles. Faults in the sensor and actuator signals are implemented into the model to find out if there is an increase or decrease in the emissions. This is used to create a fault tree where it can be seen why predefined emission thresholds are exceeded. The tree is an aid when developing OBD. The results from the simulations showed that almost no faults made the emissions cross the thresholds. The only interesting faults were faults in the ambient temperature sensor and the injection angle actuator. This means that the OBD-system only needs to monitor a few components which implies a smaller system and less work.

Over the last years, the emission and on board diagnostics legislations for heavy duty trucks are getting more and more strict. An accurate engine model that is possible to execute in the engine control system enables both better diagnosis and lowered emissions by better control strategies. The objective of this thesis is to extend an existing mean value diesel engine model, to include turbo compound. The model should be physical, accurate, modular and it should be possible to execute in real time. The calibration procedure should be systematic, with some degree of automatization. Four different turbo compound models have been evaluated and two models were selected for further evaluation by integration with the existing model. The extended model showed to be quite insensitive to small errors in the compound turbine speed and hence, the small difference in accuracy of the tested models did not affect the other output signals significantly. The extended models had better accuracy and could be executed with longer step length than the existing model, despite that more complexity were added to the model. For example, the mean error of the intake manifold pressure at mixed driving was approximately 3.0%, compared to 5.8% for the existing model. The reasons for the improvements are probably the good performance of the added submodels and the systematic and partly automatized calibration procedure including optimization.

Because of new emission and on board diagnostics legislations, heavy truck manufacturers are facing new challenges when it comes to improving the engines and the control software. Accurate and real time executable engine models are essential in this work. One successful way of lowering the NOx emissions is to use Exhaust Gas Recirculation (EGR). The objective of this thesis is to create a mean value model for Scania's next generation EGR system consisting of a poppet valve and a two stage cooler. The model will be used to extend an existing mean value engine model. Two models of different complexity for the EGR system have been validated with sufficient accuracy. Validation was performed during static test bed conditions. The resulting flow models have mean relative errors of 5.0% and 9.1% respectively. The temperature model suggested has a mean relative error of 0.77%.

<p>Today's legislation on exhaust gas emissions for heavy duty diesel (HDD) vehicles is more stringent than ever and will be even more tough in the future. More over, in a few years HDD vehicles have to be equipped with OBD (On-Board Diagnostics). This place very high demands on the manufacturers to develop better engines and strategies for OBD. As an aid in the process models can be used. </p><p>This thesis presents extensions of an existing diesel engine model in Matlab/Simulink to be able to simulate emissions during standardized european test cycles. Faults in the sensor and actuator signals are implemented into the model to find out if there is an increase or decrease in the emissions. This is used to create a fault tree where it can be seen why predefined emission thresholds are exceeded. The tree is an aid when developing OBD. </p><p>The results from the simulations showed that almost no faults made the emissions cross the thresholds. The only interesting faults were faults in the ambient temperature sensor and the injection angle actuator. This means that the OBD-system only needs to monitor a few components which implies a smaller system and less work.</p>

The emission legislations and the new On Board Diagnostics (OBD) legislations are becoming more strict and making the demands on control and fault detection higher. One way to control and diagnose the engine is to use a control/diagnose strategy based on physical models and therefore better models are necessary. Also, to be competitive and meet the markets demand of higher power, longer durability and better fuel economy, the models needs to be improved continuously. In this thesis a mean value model of the intake system that predicts the charge air temperature has been developed. Three models of different complexity for the intercooler heat-exchanger have been investigated and validated with various results. The suggested intercooler heat-exchanger model is implemented in the mean value model of the intake system and the whole model is validated on three different data sets. The model predicts the intake manifold temperature with a maximum absolute error of 10.12 K.

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.