An air hybrid vehicle is an alternative to the electric hybrid vehicle that stores the kinetic energy of the vehicle during braking in the form of pressurized air. In this thesis, a novel compression strategy for an air hybrid engine is developed that increases the efficiency of conventional air hybrid engines significantly. The new air hybrid engine utilizes a new compression process in which two air tanks are used to increase the air pressure during the engine compressor mode. To develop the new engine, its mathematical model is derived and validated using GT-Power software. An experimental setup has also been designed to test the performance of the proposed system. The experimental results show the superiority of the new configuration over conventional single-tank system in storing energy. In addition, a new switchable cam-based valvetrain and cylinder head is proposed to eliminate the need for a fully flexible valve system in air hybrid engines. The cam-based valvetrain can be used both for the conventional and the proposed double-tank air hybrid engines. To control the engine braking torque using this valvetrain, the same throttle that controls the traction torque is used. Model-based and model-free control methods are adopted to develop a controller for the engine braking torque. The new throttle-based air hybrid engine torque control is modeled and validated by simulation and experiments. The fuel economy obtained in a drive cycle by a double-tank air hybrid vehicle is evaluated and compared to that of a single-tank air hybrid vehicle.

Short-term load forecasting (STLF) is an important part of the power generation process. For years, it has been achieved by traditional approaches stochastic like time series / but, new methods based on artificial intelligence emerged recently in literature and started to replace the old ones in the industry. In order to follow the latest developments and to have a modern system, it is aimed to make a research on STLF in Turkey, by neural networks. For this purpose, a method is proposed to forecast Turkey&rsquo / s total electric load one day in advance. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to make use of the available past data for adapting the weights and to further adjust these connections according to the changing conditions. It is also suggested to tune the step size iteratively for better accuracy. Since a single neural network model cannot cover all load types, data are clustered due to the differences in their characteristics. Apart from this, special days are extracted from the normal training sets and handled separately. In this way, a solution is proposed for all load types, including working days, weekends and special holidays. For the selection of input parameters, a technique based on principal component analysis is suggested. A traditional ARMA model is constructed for the same data as a benchmark and results are compared. Proposed method gives lower percent errors all the time, especially for holiday loads. The average error for year 2002 data is obtained as 1.60%.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1990. / Includes bibliographical references (leaves 338-348) / Microfiche. / xx, 348 leaves, bound ill. (some col.) 29 cm

Autonomous robots typically rely on internal representations of the environment, or maps, to plan and execute their tasks. Several types of maps have been proposed in the literature, and there is general consensus that different types have different advantages and limitations, and that each type is more suited to certain tasks and less to others. Because of these reasons, it is becoming common wisdom in the field of mobile robotics to use hybrid maps that integrate several representations, usually of different types. Hybrid maps provide scalability and multiple views, allowing for instance to combine robot-centered and human-centered representations. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make it something more than the sum of its parts. There is no systematic analysis of the different ways in which different maps can be combined, and how they can be made to cooperate. This makes it difficult to evaluate and compare different systems, and precludes us from getting a clear understanding of how a hybrid map can be designed or improved. The investigation presented in this thesis aims to contribute to fill this foundational gap, and to get a clearer understanding of the nature of hybrid maps. To help in this investigation, we develop two tools: The first one is a conceptual tool, an analytical framework in which the main ingredients of a hybrid map are described; the second one is an empirical tool, a new hybrid map that allows us to experimentally verify our claims and hypotheses. While these tools are themselves important contributions of this thesis, our investigation has resulted in the following additional outcomes: • A set of concepts that allow us to better understand the structure and operation of hybrid maps, and that help us to design them, compare them, identify their problems, and possibly improve them; • The identification of the notion of synergy as the fundamental way in which component maps inside a hybrid map cooperate. To assess the significance of these outcomes, we make and validate the following claims: 1. Our framework allows us to classify and describe existing maps in a uniform way. This claim is validated constructively by making a thorough classification of the hybrid maps reported in the literature. 2. Our framework also allows us to enhance an existing hybrid map by identifying spots for improvement. This claim is verified experimentally by modifying an existing map and evaluating its performance against the original one. 3. The notion of synergy plays an important role in hybrid maps. This claim is verified experimentally by testing the performance of a hybrid map with and without synergy.

A novel decomposition method that is referred to as Contribution-based Decomposition is presented in this thesis. The influence of variables on the values of objective functions and/ or constraints is interpreted as their contributions. Based on contributions of variables, a design problem is decomposed into a number of sub-problems so that variables have similar relative contributions within each sub-problem. The similarity in contributions among variables will lead to an even pressure on the variables when they are driven to better solutions during an optimisation process and, as a result, better solutions can be obtained. Due to nonlinearity of objectives and/ or constrains, variables??? contributions may vary significantly during the solution process. To cope with such variations, a Dynamic Contribution-based Decomposition (DCD) is proposed. By employing DCD, decomposition of system problems is carried out not only at the beginning, but also during the optimisation process, and as a result, the decomposition will always be consistent with the contributions of the current solutions. Further more, a random decomposition is also developed and presented to work in conjunction with the Dynamic Contribution-based Decomposition to introduce re-decompositions when it is required, aiming to increase the global exploring ability. To solve multidisciplinary engineering optimisation problems more efficiently, new solvers are also developed. These include a mixed discrete variable Pattern Search (MDVPS) algorithm and a mixed discrete variable Genetic Algorithm (MDVGA). Inside the MDVGA, new techniques including a flexible floating-point encoding method, a non-dominance ranking strategy and heuristic crossover and mutation operators are also developed to avoid premature convergence and enhance the GA???s search ability. Both MDVPS and MDVGA are able to handle optimisation problems having mixed discrete variables. The former algorithm is more capable of local searching and the latter has better global search ability. A hybrid solver is proposed, which incorporates the MDVPS and the MDVGA and takes advantage of both their strengths. Lastly, a Dynamic Sub-space Optimisation (DSO) method is developed by employing the proposed Dynamic Contribution-based Decomposition methods and the hybrid solver. By employing DSO, decomposed sub-problems can be solved without explicit coordination. To demonstrate the capability of the proposed methods and algorithms, a range of test problems have been exercised and the results are documented. Collectively the results show significant improvements over other published popular approaches.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 77-81). Also available via the Internet.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

Zugl.: Dresden, Techn. Universiẗat, Fak. Verkehrswissenschaften; Diss., 2008.

Thesis (Ph. D.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographical references (p. 51-54).