A turbo system is driven by the excess energy in the exhaust gases.  As a result, variation in exhaust temperature cause variations in  boost pressure. By using the information about the available exhaust  energy in the turbo controller directly through a feedforward  controller, an unexpected variation in turbo boost can be avoided. A  model based controller is developed that calculates the desired  turbine power from the boost pressure reference and then, by  observing the available exhaust energy, controls the generated  turbine power to match the desired power. A Mean Value Engine Model  has been used to make simulation with the developed controller  implemented. Steps between different boost pressure references are  used to evaluate controller performance. Tests in a car have also  been made to make sure the simulation results are consistent in a  real environment. / Turbosystem drivs av överskottsenergin i motorns avgaser. Dettainnebär att temperaturvariationer i avgaserna orsakar variationer igenererad turbineffekt och därmed ökat laddtryck från turbosystemet.Används informationen om den tillgängliga energin i avgaserna när manstyr turbinen så kan man motverka oväntade laddtrycksförändringar. Idenna rapport har en modellbaserad turboregulator med en framkopplingsom beräknar en önskad turbineffekt från givet referenstryckutvecklats. Sedan tas en styrsignal fram till turbinen som, genom attanvända informationen om den observerade energin i avgaserna, matcharden önskade turbineffekten. En model av en medelvärdesmotor haranvänds för att validera prestandan i regulatorn via stegsvar mellanolika referenstryck. Det har även utförts tester i bil för att avgöraom resultatet blir detsamma under verkliga förhållanden.

This is a final thesis done at BT, considering one of their forklift truck models called Reflex. The first part of this report is about a preliminary investigation investigating what kind of diagnostic systems BTwants to use, and also which demands there are to meet all expectations on such system. Secondly a diagnostic system, which will show if the drive wheel is worn out, will be presented. In the preliminary investigation, two kinds of diagnostic systems were mentioned. These were Model based diagnosis and Predictive analysis. Model based diagnosis is based on measurements made by sensors at the truck, while predictive analysis is based more on statistics and retrieved data about the lifetime of a truck in specific environments. The diagnosis system for the drive wheel is based on a model made in Matlab's Simulink. Due to poor documentation, rough simplifications in the model have been made. However, one can still see the differences of principle. The main thought was detecting a difference in the lowest torque level from the engine, varying the diameter of the drive wheel. By measurements made directly at the truck, different torques could be observed with varying diameter of the drive wheel, varying load on the truck and varying friction in the gearbox. Using hypothesis tests, it is possible to say whether the drive wheel is worn out or not. Results show that if the drive wheel diameter is reduced by 25 mm, torque is reduced by 7% and if the drive wheel diameter is reduced as much as 50 mm, a torque reduction of 11% would be achieved.

To simplify troubleshooting and reliability of a process, a diagnosis system can supervise the process and alarm if any faults are detected. A diagnosis system can also identify one, or several faults, i.e. isolate faults, that may have caused the alarm. If model-based diagnosis is used, tests based on observations from the process are compared to a model of the process to diagnose the process. It can be a hard task to find which tests to be used for maximal fault detection and fault isolation. Structural Methods require not very detailed knowledge of the process to be diagnosed and can be used to find such tests early in the design of new processes. Sensors are used to get observations of a process. Therefore, sensors placed on different positions in the process gives different possibilities for observations. A specific set of sensors are in this work called a sensor configuration. This thesis contributes with a method to predict and examine the fault detection and fault isolation possibility. By using these two diagnosis properties, a suitable sensor configuration is computed and tests to be used in a future diagnosis system are suggested. For this task an algorithm which can be used in the design phase of diagnosis systems, and a Matlab implementation of this algorithm are described. In one part of this work the Matlab implementation and the algorithm are used to study how a model-based diagnosis-system can be used to supervise the fuel system in an Unmanned Aerial Vehicle (UAV).

Today’s society depends on complex and technically advanced mechanical systems, often containing a variety of different components. Despite careful development andconstruction, some of these components may eventually fail. To avoid unnecessary damage, for example environmental or financial, there is a need to locate and diagnose these faults as fast as possible. This can be done with a diagnostic system, which should produce an alarm if there is a fault in the mechanical system and, if possible, indicate the reason behind it. In model based diagnosis, a mathematical model of a fault free system is used to detect if the monitored system contain any faults. This is done by constructing fault indicators, called fault tests, consisting of equations from different parts of the model. Finding these parts is a time-consuming and demanding task, hence it is preferable if as much as possible of this process can be automated. In this thesis an algorithm that finds all parts of a system that can be used to create these fault tests is presented. To make this analysis feasible, in industrial applications, a simplified version of a system model called a structural model is used. Since the models considered in this thesis are implemented in the mathematical software Simulink, a method for transforming Simulink models into analytical equations and structural models is described. As a way of increasing the diagnostic performance for a model based diagnostic system, information about different faults, called fault models, can be included in the model. However, since the models in this thesis are implemented in Simulink, there is no direct way in which this can be preformed. This thesis describes a solution to this problem. The correctness of the algorithms in this thesis are proved and they have been applied, with supreme results, to aScania truck engine model.

Hydrogen fuel cell technology has emerged as an efficient and clean alternative to internal combustion engines for powering vehicles, and hydrogen powertrains will aid in addressing key environmental issues such as urban air quality and global warming. This work demonstrates the effectiveness of a „hardware-in-loop‟ (HIL) simulation system for validating the safety and effectiveness of control algorithms for a hydrogen fuel cell hybrid passenger vehicle. A significant amount of the work completed in conjunction with the thesis topic was the design and construction of the fuel cell vehicle for competition. Producing a „rolling test bench‟ that generates data to be used to create HIL simulation models required nearly two years of work before an acceptable level of reliability was reached to produce usable data. Some detail will be given in this thesis regarding the infrastructure modifications required to safely build a hydrogen fuel cell vehicle, as well as the design challenges faced in the integration of a fuel cell power module, two electric drive motors, a nickel metal hydride battery, and required power electronics into a small sport utility vehicle originally designed for an internal combustion powertrain. The virtual control validation performed involved designing dynamic models of the systems of interest and performing real-time simulation to ensure that the appropriate controller response is observed. For this thesis, emphasis was placed on several key vehicle control topics. Communication robustness was evaluated to ensure that the complicated vehicle communication network could effectively handle traffic from the six powertrain sub-controllers. Safety algorithms were tested for appropriate response to fault conditions. Control systems were developed and tuned offline reducing the amount of time required for in-vehicle development and testing. Software-in-the-loop simulation was used to check initial code integrity and to validate the hardware-in-the-loop vehicle models. The methodology presented in this work was found to be sufficient for a thorough safety and rationality evaluation of control strategies for hybrid fuel cell vehicles.

Vehicle design and control is an attractive area of research in that it embodies a convergence of societal need, technical limitation, and emerging capability. Environmental, political, and monetary concerns are driving the automotive industry towards sustainable transportation, manifested as increasing powertrain electrification in a gradual transition to fossil-free energy vectors. From an electrochemical degradation and control systems perspective, this transition introduces significant technical uncertainty. Initial indications are that the initial battery designs will have twice the required capacity due to degradation concerns. As the battery is a major contributor to the cost of these vehicles the over-sizing represents a significant threat to the ability of OEMs to produce cost-competitive vehicles. This potential barrier is further amplified when the combustion engine is removed and battery-electric or fuel-cell hybrid vehicles are considered. This thesis researches the application of model-based design for optimal design of fuel cell hybrid powertrains considering power source degradation. The intent is to develop and evaluate tools that can determine the optimal sizing and control of the powertrain; reducing the amount of over-sizing by numerically optimization rather than a sub-optimal heuristic design. A baseline hybrid fuel cell vehicle model is developed and validated to a hybrid fuel cell SUV designed and built at the University of Waterloo. Lithium-ion battery degradation models are developed and validated to data captured off a hybrid powertrain test stand built as part of this research. A fuel cell degradation model is developed and integrated into the vehicle model. Lifetime performance is modeled for four hybrid control strategies, demonstrating a significant impact of the hybrid control strategy on powertrain degradation. A plug-in variation of the architecture is developed. The capacity degradation of the battery is found to be more significant than the power degradation. Blended and All-electric charge-depleting hybrid control strategies are integrated and lifetime performance is simulated. The blended charge-depleting control strategy demonstrated significantly less degradation than the all-electric strategy. An oversized battery is integrated into the vehicle model and the benefit of oversizing on reducing the battery degradation rate is demonstrated.

Aim: The aim of this study was to investigate the relation between innovation and system complexity, and suggest improvements for an organization to handle innovation and complexity better. Method: The analysis was based on theoretical models about organizational structure, development models and knowledge management. The models were selected to highlight theoretical extremes rather than practical usefulness to avoid practical obstacles in the theoretic evaluations. The work progressed as a strategy development flow based on a model including four phases; analysis, objectives and recommendations, options and Implementation. Result & Conclusions: To significantly improve how complexity and innovation are managed it isn’t sufficient to focus on improvements in one part, such as processes. Organizational goals, external environment, organizational structure, development model, knowledge management and internal culture have to be considered and balanced to achieve significant improvements. For the organization studied it was clear that there was a difference in the official description of the organization and how it worked in practice. Suggestions for future research: Metrics are important to measure value and improvement. Balanced metrics describing how well an organization is adapted to its goals and environment is an area for future work. The effect of Model-Based design on organizational structure is another interesting topic for further research.   Contribution of the thesis: The recommendations and objectives developed in this study can be used to improve an organization with respect to both internal and external environment.

Accompanying with the Internet growth explosion, more and more information disseminates on the Web. The large amount of information, however, causes the information overload problem that disturbs users who desire to search and find useful information online. Information retrieval and information filtering arise to compensate for the searching and comprehending ability of the users. Recommender systems as one of the information filtering techniques emerge when users cannot describe their requirements precisely as keywords. Collaborative filtering (CF) compares novel information with common interests shared by a group of people to make the recommendations. One of its methods, the Model-based CF, generates predicted recommendation based on the model learned from the past opinions of the users. However, two issues on model-based CF should be addressed. First, data quality of the rating matrix input can affect the prediction performance. Second, most current models treat the data class as the nominal scale instead of ordinal nature in ratings. The objective of this research is thus to propose a model-based CF algorithm that considers data reliability and data scale in the model. Three experiments are conducted accordingly, and the results show our proposed method outperforms other counterparts especially under data of mild sparsity degree and of large scale. These results justify the feasibility of our proposed method in real applications.

It is important to study gene expression and change patterns over a time period because biologically related gene groups are likely to share similar patterns. In this study, similar gene expression and change patterns are found via model-based clustering method. Fourier and wavelet coefficients of gene expression data are used as the clustering variables. A two-stage model-based method is proposed for stepwise clustering of expression and change patterns. Simulation study is performed to investigate the effectiveness of the proposed methodology. Yeast cell cycle data are analyzed.

One of the promises of High Level Architecture (HLA) is the reusability of simulation components. Although HLA supports reusability to some extent with mechanisms provided by Object Model Template (OMT), when the developer wants to use an existing federate application within another federation with a different Federation Object Model (FOM) problem arises. She usually has to modify the federate code and rebuilt it. There have been some attempts to solve this problem and they, in fact, accomplish this to some extent but usually they fall short of providing flexible but also a complete mapping mechanism. In this work, a model based approach that mainly focuses on Declaration, Object and Federation Management services is explored. The proposed approach makes use of Model Integrated Computing (MIC) and .NET 2.0 technologies by grouping federate transitioning activities into three well-defined phases, namely, modeling, automatic code generation and component generation. As a side product, a .NET 2.0 wrapper to Runtime Infrastructure (RTI) has been developed to help developers create IEEE 1516 compatible .NET 2.0 federates in a programming language independent way.