Dissertations / Theses on the topic 'Diesel engine modeling'

Stricter emission legislation for heavy trucks in combination with the customers demand on low fuel consumption has resulted in intensive technical development of engines and their control systems. To control all these new solutions it is desirable to have reliable models for important control variables. One of them is the air mass-flow, which is important when controlling the amount of recirculated exhaust gases in the EGR system and to make sure that the air to fuel ratio is correct in the cylinders. The purpose with this thesis was to use system identification theory to develop a model for the air mass-flow through the compressor. First linear black-box models were developed without any knowledge of the physics behind. The collected data was preprocessed to work in the modeling procedure and then models with one or more inputs where built according to the ARX model structure. To further improve the models performance, non-linear regressors was developed from physical relations for the air mass-flow and used to form grey-box models of the air mass-flow.In conclusion, the performance was evaluated through comparing the estimated air mass-flow from the best model with the estimate that an extended Kalman filter together with a physical model produced.

Hårdare utsläppskrav för tunga lastbilar i kombination med kundernas efterfrågan på låg bränsleförbrukning har resulterat i en intensiv utveckling av motorer och deras kontrollsystem. För att kunna styra alla dessa nya lösningar är det nödvändigt att ha tillförlitliga modeller över viktiga kontrollvariabler. En av dessa är luftmassflödet som är viktig när man ska kontrollera den mängd avgaser som återcirkuleras i EGR-systemet och för att se till att kvoten mellan luft och bränsle är korrekt i motorns cylindrar. Syftet med det här examensarbetet var att använda systemidentifiering för att ta fram en modell över luftmassflödet förbi kompressorn. Först togs linjära svartboxmodeller fram utan att ta med någon kunskap om den bakomliggande fysiken. Insamlade data förbehandlades för att passa in i modelleringsproceduren och efter det skapades i enlighet med ARX-modellstrukturen modeller med en eller flera insignaler. För att ytterligare förbättra modellernas prestanda togs icke-linjära regressorer fram med hjälp av fysikaliska relationer för luftmassflödet. Dessa användes sedan för att skapa gråboxmodeller av luftmassflödet. Avslutningsvis utvärderades prestandan genom att det estimerade luftmassflödet från den bästa modellen jämfördes med det estimat som ett utökat kalmanfilter tillsammans med fysikaliska ekvationer genererade.

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.

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.

Continuously throughout the process of developing Engine Control Units (ECU), the ECU and its control functions need to be dimensioned and tested for the engine itself. Since interaction between an ECU and a physical engine is both expensive and inflexible, software models of the engine are often used instead. One such test system, where an ECU interacts with software models, is called Hardware-in-the-Loop (HiL). This thesis describes a model constructed to facilitate implementation on a HiL testbed.

The model, derived in Matlab/Simulink, is a Cylinder-by-Cylinder Engine Model (CCEM) reconstructing the angle synchronous torque of a diesel engine. To validate the model, it has been parameterised for the DaimlerChrysler engine OM646, a straight turbocharged four cylinder diesel engine, and tested towards measured data from a Mercedes-Benz C220 test vehicle. Due to hardware related problems, validation could only be performed for low engine speeds where the model shows good results. Future work around this theme ought to include further validation of the model as well as implementation on HiL.

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.

The objectives in this thesis are to present a model of a variable venturi in an exhaust gas recirculation (EGR) system located in a heavy duty diesel engine. A new legislation called EURO~4 will come into force in 2005 which affects truck development and it will require an On-Board Diagnostic system in the truck. If model based diagnostic systems are to be used, one of the advantages is that the system performance will increase if a model of a variable venturi is used.

Three models with different complexity are compared in ten different experiments. The experiments are performed in a steady flow rig at different percentage of EGR gases and venturi areas. The model predicts the mass flow through the venturi. The results show that the first model with fewer simplifications performs better and has fewer errors than the other two models. The simplifications that differ between the models are initial velocity before the venturi and the assumption of incompressible flow.

The model that shows the best result is not proposed by known literature in this area of knowledge and technology. This thesis shows that further studies and work on this model, the model with fewer simplifications, can be advantageous.

Having an engine model that is accurate but not too complicated is desirable when working with on-board diagnosis or engine control. In this thesis a four state mean value model is introduced. To make the model usable in an on-line automotive application it is discrete and simulated with a fixed step size solver. Modelling is done with simplicity as main object. Some simple static models are also presented.

To validate the model measuring is carried out in a Scania R124LB truck with a 12 liter six-cylinder turbo charged diesel engine. In general, for this relatively simple model, the mean errors must be considered low. The inlet manifold pressure mean error during highway driving is 3.4\%.