Emission from cars today is one of the biggest environmental issues, hence stringent government standards have been introduced to decrease emission. Car companies do not only have to satisfy government standards, but also meet consumer demands on increased fuel economy and good drivablility. This report will introduce controllers designed to control the air/fuel ratio in an SI engine. The engine model used is simplified. The engine components modelled include the inlet manifold, fuel dynamics, combustion and exhaust sensor. Nonlinearities and delays are inherent in the engine dynamics and as such a Smith Predictor is utilised as the basis for controller structure to compensate for the delays. Here the Smith Predictor is combined with feedforwarding of the mass air charge, which is estimated from both the inlet and combustion models. Therefore different ways of merging the estimates are also explored. A real engine was not accesible, thus simulators were implemented using data sets provided by General Motors. Model errors were introduced to test the controllers performance. The proposed methods should be tested on a real engine to ensure that this isa viable approach, as the simulations show it maybe promising to use in practice.

Ion current sensing, where information about the combustion process in an SI-engine is gained by applying a voltage over the spark gap, is currently used to detect and avoid knock and misfire. Several researchers have pointed out that information on peak pressure location and air/fuel ratio can be gained from the ion current and have suggested several ways to estimate these parameters. Here a simplified Bayesian approach was taken to construct a lowpass-like filter or estimator that makes use of prior information to improve estimates in crucial areas. The algorithm is computationally light and could, if successful, improve estimates enough for production use. The filter was implemented in several variants and evaluated in a number of simulated cases. It was found that the proposed filter requires a number of trade-offs between variance, bias, tracking speed and accuracy that are difficult to balance. For satisfactory estimates and trade-off balance the prior information must be more accurate than was available. It was also found that similar a task, constructing a general Bayesian estimator, has already been tackled in the area of particle filtering and that there are promising and unexplored possibilities there. However, particle filters require computational power that will not be available to production engines for some years. / Vid jonströmsmätning utvinns information om förbränningsprocessen i en bensinmotor genom att en spänning läggs över gnistgapet och den resulterande strömmen mäts. Jonströmsmätning används idag för knack- och feltändningsdetektion. Flera forskare har påpekat att det finns än mer information i jonströmmen, bl.a. om bränsleblandningen och cylindertrycket och har även föreslagit metoder för att utvinna och använda den informationen för skattning av dessa parametrar. Här presenteras en förenklad Bayesisk metod i form av en lågpassfilter-liknande skattare som använder förkunskap till att förbättra estimat på relevanta områden. Algoritmen är beräkningsmässigt lätt och kan, om den är framgångsrik, leverera skattningar av förbränningsparametrar som är tillräckligt bra för att användas för sluten styrning av en bensinmotor. Skattaren, eller filtret, implementerades i flera varianter och utvärderades i ett antal simulerade fall. Resultaten visade på att flera svåra avvägningar måste göras mellan förbättring i varians, avvikelse och följning eftersom förbättring i den ena ledde till försämring i de andra. För att göra dessa avvägningar och få goda skattningar krävs bättre förhandskunskap och mätdata än vad som var tillgängligt. Bayesisk skattning är ett stort befintligt område inom statistik och signalbehandling och den mest generella skattaren är partikelfiltret som har många intressanta tillämpningar och möjligheter. De har hittills inte använts inom skattning av förbränningsparametrar och har således go potential för framtida utveckling. De är dock beräkningsmässigt tunga och kräver beräkningsresurser utöver vad som är tillgängliga i ett motorstyrsystem idag.

Due to governmental requirements on low exhaust gas emissions and the drivers request of fast response, it is important to be able to control the gas flow in a spark ignited engine accurately. The air into the cylinder is directly related to the torque generated by the engine. The technique with recirculation of exhaust gases (EGR) affect the air flow into the cylinder and increase the complexity of the control problem. In this thesis a mean value model for a spark ignited engine has been created. The basis was a diesel model from Linköping University that has been modified and parameterized with data from a test cell. The model has been used to study the gas exchange system with respect to the dynamic behaviors and nonlinearities that occur when the three actuators (throttle, wastegate and EGR-valve) are changed. Based on this analysis, some different control strategies have been developed and tested on the model. The presented results show that different control strategies give different behaviors and there is a trade-off between fast torque response and high precision for controlling the EGR-ratio. A control strategy is proposed containing two main feedback loops, prefiltering of the reference signal and a feedforward part.

The diploma thesis deals with the selection of an eligible supercharger for cooperation with the internal combustion SI engine Honda K20A2. The first part is dedicated to the research referring to an issue of charging and a brief description of particular supercharger types. However, the main part of the thesis consists in the formation of proposal calculation and the tentative choice of the eligible type of supercharger. The part of the work is also the description of the assigned engine and the measurement of its parameters. The most extensive part of the thesis deals with the making of computational model of the atmospheric and supercharged version of the engine and the consecutive simulations in the GT-Power software. Finally, gained results are evaluated and the computational study of the cooperation of the selected supercharger with assigned combustion engine is made in the last part of the diploma thesis.

In this master’s thesis, a turbine model and a common control structure for theturbocharger for SI and CI-engines is developed. To design the control structure,simulations are done on an existing diesel engine model with VGT. In order tobe able to make simulations for engines with a wastegated turbine, the model isextended to include mass flow and turbine efficiency for that configuration. Thedeveloped model has a mean absolute relative error of 3.6 % for the turbine massflow and 7.4 % for the turbine efficiency. The aim was to control the intake manifoldpressure with good transients and to use the same control structure for VGTand wastegate. By using a common structure, development and calibration timecan be reduced. The non-linearities have been reduced by using an inverted turbinemodel in the control structure, which consists of a PI-controller with feedforward.The controller can be tuned to give a fast response for CI engines and a slowerresponse but with less overshoot for SI engines, which is preferable.

The high demand for an increase in performance and at the same time loweringthe emissions is forcing the automotive industry to increase the efficiency of thevehicles. This demand lead to a problem called knock, which often is the limitingfactor when increasing the efficiency of the engine. Knock occurs when theunburned gases inside the combustion chamber self-ignites due to the increasingpressure and temperature.This thesis investigates if it is possible to predict knock with a physicallybased knock model. The model consist of several sub-models such as pressuremodel, temperature model and knock model. The models are built by using measureddata and the goal is to get an independent knock prediction model that canfind the limited ignition angle that will cause knock.The results shows that an analytic pressure model can simulate a measuredpressure curve. But when it comes to predicting knock, there is an uncertaintywhich can be improved by changing the modelling strategy and making the modelsmore accurate.

This diploma thesis deals with the proposal of 3D model of single SI engine designated for research purposes. The work is mainly concentrated on the construction of the engine composition that consists of crank case, crankshaft and balance unit. It contains the research of experimental engines and constructions of basic parts of standard SI engines. The aim is the proposal of all-purpose construction of crank case and the following control with the help of FEM system.

A diagnosis procedure is an algorithm to detect and locate (isolate) faulty components in a dynamic process. In 1994 the California Air Resource Board released a regulation, called OBD II, demanding a thorough diagnosis system on board automotive vehicles. These legislative demands indicate that diagnosis will become increasingly important for automotive engines in the next few years. To achieve diagnosis, redundancy has to be included in the system. This redundancy can be either hardware redundancy or analytical redundancy. Hardware redundancy, e.g. an extra sensor or extra actuator, can be space consuming or expensive. Methods based on analytical redundancy need no extra hardware, the redundancy here is generated from a process model instead. In this thesis, approaches utilizing analytical redundancy is examined. A literature study is made, surveying a number of approaches to the diagnosis problem. Three approaches, based on both linear and non-linear models, are selected and further analyzed and complete design examples are performed. A mathematical model of an SI-engine is derived to enable simulations of the designed methods.

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.

The propulsion of the spark-ignition engine has been investigated and developed during the past century to improve driveability, minimize fuel consumption and emissions, resulting in highly engineered and computerized powertrains. Well balanced engine maps containing coordinated set-points and model-based information sharing have solved the cross-coupling between different control loops. During transitions between the operating conditions a disadvantageous transient behavior that affects the engine performance may occur. By implementing an MPC as a superior controller a nearly optimal control solution was accomplished. A digital twin of the SI engine was designed through collected measurements and system modeling. The twin made it possible to investigate and elaborate different cost functions in a simulation environment before applying the controller in real-time. By utilizing MPC together with the engine maps a strong relationship between the throttle and iVVT actuator was achieved, which removed the cross-coupling between the actuator control loops and reduced the unfavorable transient behavior.