Automotive design is a specialized discipline in which designers are challenged to create emotionally appealing designs. From a practice perspective, this requires that designers apply their hermeneutic as well as reflective design thinking skills. However, due to the increasing demand for new car models, it is not always possible to keep generating new car designs without some form of assistive means. Therefore, it is common practice to use Automated Morphing Systems (AMS) to facilitate and accelerate the design process in the automotive industry. However, AMS, which is an efficient algorithmic driven tool for form generation, lacks the emotional knowledge of human beings, as well as the ability to introduce a “creative” and preferably a “winning” design. The purpose of this research is to study designers' reasoning about product (automotive) form, their form generation activity, and the implications of these. The research objective is to understand how designers generate forms driven by their implicit values, beliefs and attitudes towards designing, and how these are supported by their visualization and representation skills. Four research questions have been formulated in order to get a firm answer posed in this research. Generation of measurable and testable data – which involved both qualitative and quantitative research to gather and analyze implicit and explicit designer’s knowledge – constituted the main empirical effort for this thesis. A design research methodology framework consisting of three different parts was used in this data gathering exercise. These parts are: descriptive study I, prescriptive study, and descriptive study II. They involved methods such as surveys, observation studies and evaluation studies. Master’s students’ evaluations as well as the designers’ own interpretations of their sketches – which represent the sequence of morphed forms – were considered essential aspects of the empirical studies. The findings of this study can be summarized as follows: Approaches in form development among designers vary due to their experiences, which affect their sketching abilities, activities, and implicit thinking patterns. In their sketching and form development activities, designers emphasize the most informative views, such as façade and three quarter front views, compared to other views of the car. Rather than adopt a uniform transformation strategy which includes the entire car, they also select what elements to morph. In manual form generation, designers contribute with their personal and creative input in the development of the forms of the overall car, its selected items, and regions that determine the overall character of the car. Major differences in the morphing approaches applied by designers and automated CAD systems reside in the recognition and interpretation of the meaning of form elements. Considering the inability of AMS to morph selectively and inconsistently, as well as to introduce ambiguity and variance, it is suggested here that AMS may be useful only for convergent transformation, which typically occurs during the later stages of the styling process. Although perceptions vary according to how representations are presented in the morphing process, the Perceptual Product Experience (PPE) framework can still be considered a useful tool for establishing familiarity, for understanding quality characteristics and the nature of the product, and, finally, for determining meanings and assessing the values of form elements. In conclusion, the work presents a descriptive model for practice-based design thinking about form development in automotive design. Manual interpolative morphing has been the focal area of study. The study categorizes meaning with respect to designer perception. Based on the study of manual morphing exercises, a new methodology of analyzing form syntactics, pragmatics and semantics related to design thinking, form development, and automotive design has been developed.

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.<p>The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.<p>The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.<p>In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).<p>An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.<p>One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.<p>The significant original contributions resulting from this research include:<br> -collection and summarization of previous work,<br> -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement,<br> -development of a more accurate model for the variability of the transport delay in modern port injection engines,<br> -developing a fuel-air controller requiring minimal knowledge of the engine's parameters,<br> -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods,<br> -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system,<br> -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains,<br> -development of a limit cycle model for the new neural controller, and<br> -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.

In order to analyze and increase the performance of a top fuel dragster, a dynamic model of the car was developed in this thesis. The drag racing car is moving mainly in a rectilinear/one dimensional mode, nevertheless, a five-degree-of-freedom model was needed to properly capture its dynamic behavior. This is a 2D model of the car dynamics in the vertical plane. Longitudinal, vertical, and pitching chassis motions were considered, as well as drive-train dynamics. The aerodynamics of the car, the engine characteristics, and the force due to the combustion gases were incorporated in the model. Further, the rear tires deflection due to the torque and the vertical load was included in the analysis, considering the effect of the angular speed on it. Also, a simplified model of the traction characteristics of the rear tires was developed. With this model, the traction is calculated as a function of the slip ratio and the speed. Since the diameter of the dragster rear tires is not constant, but increases with the angular speed, the angular momentum equation was considered. This leads to deriving an equivalent mass moment of inertia of the rear tires. This moment of inertia is a function of the angular speed. The model not only considered the instantaneous radius of the tires, but the rate of deformation was also taken into consideration. In this manner, the torque applied to the rear tires, and the angular acceleration produced on them, were related by the simple torque-angular acceleration equation of rigid body dynamics. The complete model to analyze the dynamic of the vehicle involves a set of nonlinear, coupled differential equations of motion, which were numerically integrated using a digital computer. Several simulation runs were made to investigate the effects of the aerodynamics, and the engine initial torque in the performance of the car. The simulation results show that the model captured to a significant degree the dynamic behavior of the dragster. They also suggest that a reduction in the elapsed time during a race can be possible under appropriate conditions. The proposed dynamic model of the dragster can be used to improve the aerodynamics, the engine and clutch set-ups of the car, and to possibly facilitate the redesign of the dragster. This model can be adapted to perform analyses of other types of drag racing vehicles, such as pro-stock cars and motorcycles. Used in conjunction with accurate and more complete data available to racing teams, the model can be quite useful in improving the performance of current top fuel dragsters and "funny" cars.

This Thesis project is a part of the research conducted in Solar industry. ABSOLICON Solar Concentrator AB has invented and started production of the prospective solar concentrated system Absolicon X10. The aims of this Thesis project are designing, assembling, calibrating and putting in operation the automatic measurement system intended to evaluate distribution of density of solar radiation in the focal line of the concentrated parabolic reflectors and to measure radiation from the artificial source of light being a calibration-testing tool.On the basis of the requirements of the company’s administration and needs of designing the concentrated reflectors the operation conditions for the Sun-Walker were formulated. As the first step, the complex design of the whole system was made and division on the parts was specified. After the preliminary conducted simulation of the functions and operation conditions of the all parts were formulated.As the next steps, the detailed design of all the parts was made. Most components were ordered from respective companies. Some of the mechanical components were made in the workshop of the company. All parts of the Sun-Walker were assembled and tested. The software part, which controls the Sun-Walker work and conducts measurements of solar irradiation, was created on the LabVIEW basis. To tune and test the software part, the special simulator was designed and assembled.When all parts were assembled in the complete system, the Sun-Walker was tested, calibrated and tuned.

The number of electronic control units in heavy-duty vehicles has grown dramatically overthe last few decades. This has led to the use of communication buses to reduce the complexityand weight of the networks. There are reasons to believe that the de facto standardcommunication interface in the automotive industry, the Controller Area Network, is obsoletein some areas. Hence an evaluation of available communication interfaces is needed.This study focuses on lower levels of the Open Systems Interconnect (osi) model. Initially atheoretical study is presented in order to give an overview of automotive embedded systemsin general and different communication interfaces in particular. Ethernet and FlexRay areidentified as two interfaces of interest for future use in Scanias vehicles. The former is new inautomotive applications but is believed to become popular over the years to come. A possibleuse of this interface could be as a backbone to take the load off other interfaces. The use ofFlexRay in Scanias vehicles is limited because of the modular system used and the staticscheduling needed. It could however be used between mandatory ecus where the nodes andthe messages are all known beforehand.The report also contains the result from emission measurements on a number of interfacesperformed using a stripline antenna in a shielded enclosure. Strong conclusions can not bedrawn since it’s hard to tell what the transceivers, circuit boards and interfaces contributedto in the spectra with the method used. The FlexRay hardware is worse than for the otherinterfaces. Similarities can be seen between low-speed and high-speed can but it could becharacteristics of the transceivers used rather than the interface itself.

Framtagning av en ny infästningen mellan damasken och växelspaken till företaget Kongsberg Automotive AB.

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.<p>The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.<p>The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.<p>In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).<p>An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.<p>One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.<p>The significant original contributions resulting from this research include:<br> -collection and summarization of previous work,<br> -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement,<br> -development of a more accurate model for the variability of the transport delay in modern port injection engines,<br> -developing a fuel-air controller requiring minimal knowledge of the engine's parameters,<br> -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods,<br> -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system,<br> -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains,<br> -development of a limit cycle model for the new neural controller, and<br> -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller.

This paper begins by showing the need and motivation for an examination of automotive design education. That is followed by an overview of the current state of the art in the field as exemplified by the programs of Art Center College of Design, College for Creative Studies, Academy of Art University and other institutions offering undergraduate transportation design programs in the United States. Particular attention is given to the integration of new technology into the design curriculum. Once the baseline has been established and the range of current practice revealed, possibilities for the future are explored through the insight of leading educators, software developers, and professional design studios. Not to be overlooked but slightly tangential to the thrust of this paper is the great importance of the student population and educational theory. Ultimately this paper will grapple with the opportunities presented by the introduction of computer based design tools into the traditional industrial design education process. Proposals will be made with regard to new curricula that better serve the students, the academic institutions that prepare them, and the industry to which the graduates matriculate.

We analyze issues arising from demand management in decentralized decision-making environments. We consider logistics systems and supply chains, where companies' operations are handled with independent entities whose decisions affect the performance of the overall system. In the first study, we focus on a logistics system in the sea cargo industry, where demand is booked by independent sales agents, and the agents' capacity limits and sales incentives are determined by a central headquarters. We develop models for the central headquarters to analyze and optimize capacity allocation and sales incentives to improve the performance of the decentralized system. We use network flow problems to incorporate agent behavior in our models, and we link these individual problems through an overall optimization problem that determines the capacity limits. We prove a worst-case bound on the decentralized system performance and show that the choice of sales incentive impacts the performance. In the second study, we focus on supply chains in the automotive industry, where decentralization occurs as a result of the non-direct sales channels of the auto manufacturers. Auto manufacturers can affect their demand through sales promotions. We use a game theoretical model to examine the impact of retailer incentive and customer rebate promotions on the manufacturer's pricing and the retailer's ordering/sales decisions. We consider several models with different demand characteristics and information asymmetry between the manufacturer and a price discriminating retailer. We characterize the subgame-perfect Nash equilibrium decisions and determine which promotion would benefit the manufacturer under which market conditions. We find that the retailer incentives are preferred when demand is known. On the other hand, when demand is highly uncertain the manufacturer is better off with customer rebates. We extend this research by analyzing a competitive setting with two manufacturers and two retailers, where the manufacturers' promotions vary between retailer incentives and customer rebates. We find an equilibrium outcome where customer rebates reduce the competitor's profits to zero. We observe in numerical examples that the manufacturers are able to increase their sales and profits with retailer incentives, although this can be at the expense of the retailers' profits under some situations.

This thesis comprises two parts : the first one is the design and implementation of FlexRay automotive communication system physical layer; the second part is the design of a high speed pipelined tree-structured carry lookahead adder (CLA). The first part of this thesis is to introduce the physical layer specification of FlexRay automotive communication system. Then, it is realized in an SOC by a typical 0.18 um CMOS process. The second topic is to propose a novel CANT logic. By the CANT logic, a pipelined tree-structured carry lookahead adder is designed and implemented. The dynamic bulk biasing technique is utilized to increase the switching speed of inverting circuits such that the delays of the inverting and non-inverting circuit is very close. The proposed architecture can be easily expanded to long data words CLA. Post-layout simulations reveal that the 32-bit CLA using the proposed CANT logic can operate up to 7.2 GHz by using the UMC 90 nm process.