The modern automobile is a complex electromechanical system controlled by control systems which consist of several interdependent electronic control units (ECUs). Analysis of the data generated by these modules is very important in order to observe the interesting patterns among data. At Volvo Cars Corporation today, diagnostic read-out data is retrieved from client machines installed at workshops in different countries around the world. The problem with this data is that it does not show a clear picture as what is causing what i.e. tracking the problem. Diagnostic engineers at Volvo Cars Corporation perform routine based statistical analysis of diagnostic read-out data manually, which is time consuming and tedious work. Moreover, this analysis is restricted to basic level mainly statistical analysis of diagnostic readout data. We present an approach based on statistical analysis and cluster analysis. Our approach focused on analysing the data from a pure statistical stand-point to isolate the problem in diagnostic read-out data, thereby helping to visualize and analyse the nature of the problem at hand. Different general statistical formulae were applied to get meaningful information from large amount of DRO data. Cluster analysis was carried out to get clusters consisting of similar trouble codes. Different methods and techniques were considered for the purpose of cluster analysis. Hierarchical and non-hierarchical clusters were extracted by applying appropriate algorithms. The results obtained from the thesis work show that the diagnostic read-out data consist of independent and interdependent fault codes. Groups were generated which consist of similar trouble codes. Furthermore, corresponding factors from freeze frame data which shows significant variation for these groups were also extracted. These faults, groups of faults and factors were later interpreted and validated by diagnostic engineers.

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.

<p>This master’s thesis describes the development of a model of the catalystsystem aiming at control by an MPC. A well functioning model, which is suitable in control purpose, is important while emission legislation become more and more hard to fulfill for the car manufacturers. Much research has been done in the field of physical modeling of the system, but in this work a linear adaptive time discrete ARX-model is developed and validated.</p><p>The systems tendency to change its dynamic during usage implies that the model must be adaptive. The developed model proved to be well functioning and shows promising conditions for the MPC design. The system and the model are analyzed in the time- and frequency domains and the model is both implemented and validated in a Saab 9-5.</p><p>The work has been performed both at Saab Automobile Powertrain AB in Södertälje and in Vehicular Systems Dept. of Electrical Engineering at Linköpings University.</p>

Engine knock has long been a well recognized phenomenon in the automotive industry. Detecting engine knock opens up the possibility for an indirect feedback of the engine's internal combustion without installing a pressure transducer inside the cylinder. Knock detection has mainly been used for spark advance control, making it possible to control the engine close to its knock limit in search for the optimal ignition timing. This application has to a lesser extent been applied to lightweight two-stroke engines, which is the focus of this study. The investigation features a modern chainsaw engine whose knock characteristics were first determined with a pressure transducer. The structural vibrations originating from the engine knock are filtered out of the signal from a remote located accelerometer. The knock intensity is compared with the signal from the pressure transducer which shows a correlation with an accepted extent between the two sensors. Parameters that affect the knock intensity have also been investigated. These include engine temperature, different types of fuel and ignition timings.

The modern automobile is a complex electromechanical system controlled by control systems which consist of several interdependent electronic control units (ECUs). Analysis of the data generated by these modules is very important in order to observe the interesting patterns among data. At Volvo Cars Corporation today, diagnostic read-out data is retrieved from client machines installed at workshops in different countries around the world. The problem with this data is that it does not show a clear picture as what is causing what i.e. tracking the problem. Diagnostic engineers at Volvo Cars Corporation perform routine based statistical analysis of diagnostic read-out data manually, which is time consuming and tedious work. Moreover, this analysis is restricted to basic level mainly statistical analysis of diagnostic readout data. We present an approach based on statistical analysis and cluster analysis. Our approach focused on analysing the data from a pure statistical stand-point to isolate the problem in diagnostic read-out data, thereby helping to visualize and analyse the nature of the problem at hand. Different general statistical formulae were applied to get meaningful information from large amount of DRO data. Cluster analysis was carried out to get clusters consisting of similar trouble codes. Different methods and techniques were considered for the purpose of cluster analysis. Hierarchical and non-hierarchical clusters were extracted by applying appropriate algorithms. The results obtained from the thesis work show that the diagnostic read-out data consist of independent and interdependent fault codes. Groups were generated which consist of similar trouble codes. Furthermore, corresponding factors from freeze frame data which shows significant variation for these groups were also extracted. These faults, groups of faults and factors were later interpreted and validated by  diagnostic engineers.

The objective of the work was to develop a basic control system for an advancedexperimental engine from scratch. The engine this work revolves around is a Saabvariable compression engine.A new control system is developed based on the naked engine, stripped of theoriginal control system. Experiments form the basis that the control system isbuilt upon. Controllers for throttles, intake manifold pressure for pressures lessthan ambient pressure and exhaust gas oxygen ratio are developed and validated.They were found to be satisfactory. The lambda controller is tested with severalparameter sets, and the best set is picked to be implemented in the engine. Modelsnecessary for the development and validation of the controllers are developed.These models include models for the volumetric efficiency, the pressure dynamicsof the intake manifold, the fuel injectors and wall wetting.

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No / This paper presents a calibration optimization study for a Gasoline Direct Injection engine based on a multidisciplinary design optimization (MDO) framework. The paper presents the experimental framework used for the GDI engine mapping, followed by an analysis of the calibration optimization problem. The merits of the MDO approach to calibration optimization are discussed in comparison with a conventional two-stage approach based on local trade-off optimization analysis, focused on a representative emissions drive cycle (NEDC) and limited part load engine operation. The benefits from using the MDO optimisation framework are further illustrated with a study of relative effectiveness of different camshaft timing control strategies (twin independent Versus fixed timing, exhaust only, inlet only and fixed overlap / dual equal) for the reference GDI engine based on the part load test data. The main conclusion is that the MDO structure offers an effective framework for the GDI steady state calibration optimization analysis.

After undergoing a study about current engine modelling and mapping approaches as well as the engine modelling requirements for different applications, a major problem found to be present is the extensive and time consuming mapping procedure that every engine has to go through so that all control parameters can be derived from experimental data. To improve this, a cycle-by-cycle modelling approach has been chosen to mathematically represent reciprocating engines starting by a complete dynamics crankshaft mechanism model which forms the base of the complete engine model. This system is modelled taking into account the possibility of a piston pin offset on the mechanism. The derived Valvetrain model is capable of representing a variable valve lift and phasing Valvetrain which can be used while modelling most modern engines. A butterfly type throttle area model is derived as well as its rate of change which is believed to be a key variable for transient engine control. In addition, an approximation throttle model is formulated aiming at real-time applications. Furthermore, the engine inertia is presented as a mathematical model able to be used for any engine. A spark ignition engine simulation (SIES) framework was developed in MATLAB SIMULINK to form the base of a complete high fidelity cycle-by-cycle simulation model with its major target to provide an environment for virtual engine mapping procedures. Some experimental measurements from an actual engine are still required to parameterise the model, which is the reason an engine mapping (EngMap) framework has been developed in LabVIEW, It is shown that all the moving engine components can be represented by a single cyclic variable which can be used for flow model development.

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