With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions. / UK Technology Strategy Board (TSB)

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Die modellbasierte Kalibrierung dynamischer Fahrmanöver an Prüfständen ermöglicht die systematische Optimierung von Steuergerätedaten über den gesamten Betriebsbereich des Fahrzeugs und begegnet somit der steigenden Komplexität in der Antriebsstrangentwicklung. Dabei werden mehrere empirische Black-Box-Modelle zur Abbildung der Zielgrößen für die nachfolgende Optimierung identifiziert. Der Einsatz der statistischen Versuchsplanung ermöglicht eine systematische Abdeckung des gesamten Eingangsbereiches. In jüngerer Vergangenheit werden in der Automobilindustrie vereinzelt Methoden des maschinellen Lernens eingesetzt, um die Anwendung der modellbasierten Kalibrierung zu vereinfachen und die Effizienz zu erhöhen. Insbesondere der Einsatz des aktiven Lernens führt zu vielversprechenden Ergebnissen. Mit diesen Methoden werden Modelle mit einer geringeren Anzahl an Messpunkten identifiziert, während gleichzeitig die erforderliche Expertise für die Versuchsplanerstellung reduziert wird. Eine Herausforderung stellt die simultane Identifikation mehrerer Regressionsmodelle dar, die für die Anwendung des aktiven Lernens auf die Fahrbarkeitskalibrierung erforderlich ist. Hierfür wird im Rahmen dieser Arbeit die aktive Ausgangsselektion (AOS) eingeführt und eingesetzt. Die AOS-Strategie bestimmt dabei das führende Modell im Lernprozess. Erste Veröffentlichungen zeigen das Potenzial der Verwendung von AOS. Statistisch signifikante Ergebnisse über die Effektivität gibt es bislang jedoch nicht, weswegen die weitere intensive Untersuchung von Strategien erforderlich ist. In der vorliegenden Arbeit werden regel- und informationsbasierte AOS-Strategien vorgestellt. Letztere wählen das führende Modell basierend auf allen während des Versuchs verfügbaren Informationen aus. Hier erfolgt erstmals die detaillierte Beschreibung und Untersuchung einer normierten modellgütebasierten Auswahlstrategie. Als Modellart werden Gauß’sche Prozessmodelle verwendet. Anhand von Versuchen wird überprüft, ob der Einsatz von AOS gegenüber gängiger statistischer Versuchsplanung sinnvoll ist. Darüber hinaus wird untersucht, ob die Berücksichtigung aller zur Versuchslaufzeit bekannten Informationen zu einer Verbesserung des Lernprozesses beiträgt. Die Strategien werden an Simulationsexperimenten getestet. Diese Simulationsexperimente stellen Grenzfälle echter Versuche dar, die für die Strategien besonders herausfordernd sind. Die Erstellung der Experimente wird anhand von Informationen aus realen Prüfstandsversuchen abgeleitet. Die Strategien werden analysiert und miteinander verglichen. Dazu wird eine anspruchsvolle Referenzstrategie verwendet, die auf den Methoden der klassischen Versuchsplanung basiert. Die Versuche zeigen, dass bereits einfache regelbasierte Strategien bessere Ergebnisse hervorbringen als die Referenzstrategie. Durch Berücksichtigung der momentanen Modellgüte und Abschätzung des Prozessrauschens zur Versuchslaufzeit ist eine weitere Reduktion der Messpunkte um mehr als 50% gegenüber der Referenzstrategie möglich. Da die informationsbasierte Strategie rechenintensiver ist, wird auch ein zeitlicher Vergleich mit unterschiedlichen langen Annahmen für die Fahrmanöverdauer am Prüfstand vorgenommen. Bei kurzen Manöverzeiten ist der Vorteil der informationsbasierten Strategie gegenüber der regelbasierten Strategie nur gering ausgeprägt. Mit zunehmender Manöverzeit nähert sich die abgeschätzte zeitliche Ersparnis jedoch der prozentualen Einsparung der Messpunkte an. Die aus den Simulationsexperimenten abgeleiteten Ergebnisse werden anhand eines realen Anwendungsbeispiels validiert. Die Implementierung an einem Antriebsstrangprüfstand wird dazu vorgestellt. Für die Versuche werden insgesamt 1500 Fahrmanöver an diesem Prüfstand durchgeführt. Die Ergebnisse der Versuche bestätigen die aus den Simulationsexperimenten abgeleiteten Ergebnisse. Die regelbasierte AOS-Strategie reduziert die Anzahl der Messpunkte im Durchschnitt um 65% im Vergleich zur verwendeten Referenzstrategie. Die informationsbasierte AOS-Strategie verringert die Anzahl der Punkte weiter auf 70% gegenüber der Referenzstrategie. Die Modelle der informationsbasierten Strategie sind bereits nach 50% der Punkte besser als die besten Modelle der regelbasierten Strategie. Die Ergebnisse dieser Arbeit legen den ständigen Einsatz der vorgestellten informationsbasierten Strategien für die modellbasierte Kalibrierung nahe. / Model-based calibration of dynamic driving maneuvers on test benches enables the systematic optimization of ECU data over the vehicle’s entire operating range and thus faces the increasing complexity in powertrain development. Several empirical black-box models are identified to represent the target variables for the succeeding optimization. The use of statistical experimental design enables systematic coverage of the entire input range. Recently, machine learning methods have been occasionally used in the automotive industry to simplify applying the process and increase its efficiency. In particular, the use of active learning leads to promising results. It leads to a reduction of the number of measurement points necessary for model identification. At the same time, the required expertise for experimental design is reduced. The simultaneous identification of multiple regression models, which is required for a broad application of active learning to drivability calibration, is challenging. In this work, active output selection (AOS) is introduced and applied to face this challenge. An AOS strategy determines the leading model in the learning process. First publications show the potential of using AOS. However, no statistically significant results about the effectiveness are available to date, which is why these strategies need to be studied in more detail. This work presents rule- and information-based AOS strategies. The latter select the leading model based on all current information available during the experiment. For the first time, this publication provides a detailed description and investigation of a normalized model-quality-based selection strategy. Gaussian process models are used as model type. Experiments are conducted to verify whether the use of AOS is reasonable compared to common designs of experiments. Furthermore, we analyze whether taking into account all information known at the time of the experiment helps to improve the learning process. The strategies are first tested on computer experiments. These computer experiments represent borderline cases of real experiments, which are particularly challenging for the strategies. The experiments are derived using information from real test bench experiments. The strategies are analyzed and compared with each other. For this purpose, a sophisticated reference strategy is used, which is based on the methods of classical designs of experiments. The experiments show that even simple rule-based strategies lead to better results than the reference strategy. By considering the current model quality and estimating the process noise during experiment runtime, a further reduction of the measurement points by more than 50% compared to the reference strategy is possible. Since the information-based strategy is more computationally expensive, we perform a time comparison with different assumptions for the driving maneuver duration at the test bench. For short maneuver times, the advantage of the information-based strategy in comparison to the rule-based strategy is only small. As the maneuver time increases, the estimated time reduction approaches the percentage savings of the measurement points. The results derived from the computer experiments are validated using a real application example. The implementation on a powertrain test bench is presented for this purpose. For the experiments, a total of 1500 driving maneuvers are performed on this test bench. The results of the experiments confirm the results of the computer experiments. The rule-based AOS strategy reduces the number of measurement points by 65% on average compared to the reference strategy used. The information-based AOS strategy further reduces the number of points to 70% compared to the reference strategy. The results of this work suggest the use of the presented information-based strategies for model-based calibration.

Engine mapping is the process of empirically modelling engine behaviour as a function of adjustable engine parameters, predicting the output of the engine. The aim is to calibrate the electronic engine controller to meet decreasing emission requirements and increasing fuel economy demands. Modern engines have an increasing number of control parameters that are having a dramatic impact on time and e ort required to obtain optimal engine calibrations. These are further complicated due to transient engine operating mode. A new model-based transient calibration method has been built on the application of hierarchical statistical modelling methods, and analysis of repeated experiments for the application of engine mapping. The methodology is based on two-stage regression approach, which organise the engine data for the mapping process in sweeps. The introduction of time-dependent covariates in the hierarchy of the modelling led to the development of a new approach for the problem of transient engine calibration. This new approach for transient engine modelling is analysed using a small designed data set for a throttle body inferred air ow phenomenon. The data collection for the model was performed on a transient engine test bed as a part of this work, with sophisticated software and hardware installed on it. Models and their associated experimental design protocols have been identi ed that permits the models capable of accurately predicting the desired response features over the whole region of operability. Further, during the course of the work, the utility of multi-layer perceptron (MLP) neural network based model for the multi-covariate case has been demonstrated. The MLP neural network performs slightly better than the radial basis function (RBF) model. The basis of this comparison is made on assessing relevant model selection criteria, as well as internal and external validation ts. Finally, the general ability of the model was demonstrated through the implementation of this methodology for use in the calibration process, for populating the electronic engine control module lookup tables.