Dissertations / Theses on the topic 'Optimal control management'
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Develle, Michael James II. "Optimal attitude control management for a cubesat." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4752.
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CubeSats have become popular among universities, research organizations, and government agencies due to their low cost, small size, and light weight. Their standardized configurations further reduce the development time and ensure more frequent launch opportunities. Early cubesat missions focused on hardware validation and simple communication missions, with little requirement for pointing accuracy. Most of these used magnetic torque rods or coils for attitude stabilization. However, the intrinsic problems associated with magnetic torque systems, such as the lack of three-axis control and low pointing accuracy, make them unsuitable for more advanced missions such as detailed imaging and on-orbit inspection. Three-axis control in a cubesat can be achieved by combining magnetic torque coils with other devices such as thrusters, but the lifetime is limited by the fuel source onboard. To maximize the mission lifetime, a fast attitude control management algorithm that could optimally manage the usage of the magnetic and thruster torques is desirable. Therefore, a recently developed method, the B-Spline-augmented virtual motion camouflage, is presented in this defense to solve the problem. This approach provides results which are very close to those obtained through other popular nonlinear constrained optimal control methods with a significantly reduced computational time. Simulation results are presented to validate the capabilities of the method in this application.ID: 030646253; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.A.E.)--University of Central Florida, 2011.; Includes bibliographical references (p. 45-49).
M.S.A.E.
Masters
Mechanical and Aerospace Engineering
Engineering and Computer Science
Aerospace Engineering; Space System Design and Engineering Track
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Hodge, D. J. "Problems of stochastic optimal control and yield management." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604120.
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We present a collection of results in the broad area of stochastic optimization. Our basic model is that of dynamic resource allocation via customer acceptance control. We begin by modelling optimal acceptance to a discrete-capacity service-on-demand system where customers arrive with differing demands and revenues. With strong restrictions on customer types we establish the optimal policy under general arrival processes. With weaker restrictions we establish monotonicity properties under stationary arrivals. We than look at a deterministic demand-curve approach to the same problem; resource allocation over time. We solve the problem of non-overlapping customer demands, for a number of different demand curves. Our main work concerns selling perishable goods via customer acceptance control. We look at the optimal boundary between accepting and declining customers of different types. Existing papers demonstrate this threshold but fail to observe its surprisingly linear nature. We study the problem of finding the best linear threshold and see that, as a heuristic, it performs very well. Our study of linear thresholds educes an interesting problem: sample-path analysis. The problem concerns the evolution of segments of the sample-path in inventory-time space with regions of different downward drift. We succeed in fully characterising the studied sample path segments, finding a remarkable dual use of an interesting identity. In the final chapters, we look at two further problems of stochastic optimization. The first is an innovative approach to modelling future demand, utilizing previous price requests. Using these dynamic demand estimations we demonstrate monotonicity properties of the optimal pricing policy. The second problem is the famous parking problem first introduced by Rényi in the fifties. We study a Markov chain queuing model for the availability of parking spaces. We derive the pay-offs from the class of very natural threshold policies, with respect to an ‘average distance from venue’ objective.
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Federico, Salvatore. "Stochastic optimal control problems for pension funds management." Doctoral thesis, Scuola Normale Superiore, 2009. http://hdl.handle.net/11384/85662.
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Pienaar, Gideon Johannes. "Optimal objective achievement via balance of control." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52978.
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Thesis (MMil)--University of Stellenbosch, 2002.ENGLISH ABSTRACT: Organisations need assurance that strategy is executed as planned and objectives are met, when matching organisational capabilities with the opportunities in the market. Control can give management such an assurance and is ultimately the responsibility of management. It is not however only management that may be interested with proper control in an organisation, all stakeholders wants some degree and form of control to safeguard their interest in an organisation. Management has a daunting task on their hands to ensure an organisation achieve its objectives as effectively, efficiently and economically as possible, while keeping all stakeholders informed and happy. Vast amounts of resources will be used to achieve objectives and management needs to strike a balance between protection of these resources and empowering employees to utilise these resources. A balance between the control systems over the different resources must also be in place. Management needs to have a balanced focus regarding the measurement of activities and behaviour relating to specific objectives, due to the time, cost, quality and innovation effects of control systems. A balance between costs and benefits of control systems must also be obtained. Control needs to be integrated as part of the management process to ensure optimal achievement of objectives. A holistic approach towards control and the usage of a sound control environment combined with relevant, organisational specific control systems that are flexible can ensure balance of control. All employees of an organisation have control responsibilities and must give inputs in the control process. Employees must view control as an aid and not as a stumbling block, when trying to achieve objectives. Management must be empowered and empower employees to have the relevant knowledge regarding control and control systems that can be used, when to use them and how to use them. These control systems must be continuously improved to ensure sustained, optimal achievement of objectives.
AFRIKAANSE OPSOMMING: Organisasies benodig versekering dat strategie uitgevoer word soos beplan en dat doelwitte bereik word, wanneer die organisasie se vermoë gepas word teen geleenthede in die mark. Beheer kan vir bestuur so 'n versekering gee en is eintlik bestuur se verantwoordelikheid. Dit is egter nie net bestuur wat belang kan stel in voldoende beheer nie, maar alle belanghebbers soek 'n mate en vorm van beheer oor 'n organisasie, om hul belange te beskerm. Bestuur het 'n uitdagende taak op hande om te verseker dat doelwitte doeltreffend, doelmatig en ekonomies bereik word, terwyl alle belanghebbers ingelig en gelukkig gehou word. Groot hoeveelhede hulpbronne word gebruik om doelwitte te bereik en bestuur moet 'n balans verkry tussen die beskerming van hulpbronne en die bemagtiging van werkers in die aanwending van die hulpbronne. 'n Balans tussen die beheer stelsels oor die verskillende hulpbronne moet ook in plek wees. Weens die tyd, koste, kwaliteit en innovasie effek van beheer moet bestuur 'n gebalanseerde fokus hê rakende die meting van aktiwiteite en gedrag betreffende spesifieke doelwitte. 'n Balans tussen die kostes en voordele van beheerstelsels moet ook verkry word. Beheer moet 'n ge-integreerde deel van die bestuursproses wees om optimale doelwit bereiking te verseker. 'n Holistiese benadering rakende beheer en die gebruik van 'n voldoende beheer omgewing, gekombineer met relevante, organisasie spesifieke beheer stelsels wat buigsaam is, kan balans van beheer verseker. Alle werkers in 'n organisasie het beheer verantwoordelikhede en moet insette verskaf in die beheer proses. Werkers moet beheer as 'n hulpmiddel ag en nie 'n struikelblok nie, wanneer doelwit bereiking nagestreef word. Bestuur moet bemagtig wees en werkers bemagtig om oor die nodige kennis rakende beheer en beheer stelsels te beskik, watter beheer stelsel gebruik kan word, wanneer om dit te gebruik en hoe om dit te gebruik. Die beheer stelsels moet aaneenlopend verbeter word om volgehoue, optimale doelwit bereiking te verseker.
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Zenios, Stefanos A. (Stefanos Andrea). "Health care applications of optimal control theory." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11042.
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Mensah, Edoe F. Kwatny Harry G. "Logic-based optimal control for shipboard power system management /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2765.
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Lee, Yu Chung Eugene. "Co-ordinated supply chain management and optimal control problems." online access from Digital Dissertation Consortium, 2007. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3299869.
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Jantharamin, Niphat. "Optimal control and management of photovoltaic power generation systems." Thesis, University of Leeds, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556249.
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Rathnayake, Upaka Sanjeewa. "Optimal management and operational control of urban sewer systems." Thesis, University of Strathclyde, 2013. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=25549.
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Combined sewer networks control, like many other real world problems, is usually identified with competing and conflicting objectives. Decision makers have a great need of selecting the best possible control strategy in minimizing the combined sewer overflows (CSOs) when controlling the sewer networks. However, this control strategy should be cost effective to produce a feasible control approach in real world. Cost effectiveness has become significantly important in present economic recession. Over the past decades, people have witnessed the control strategies based on minimization of CSOs. However, it is now, not only to minimize CSOs, but also to minimize the impact to the natural water from these CSOs. Therefore, this research explores the development of a holistic framework that is used for the multi-objective optimization of urban wastewater systems, considering flows and water quality in combined sewers and the cost of wastewater treatment. Pollution levels of several water quality parameters in dry weather flows and stormwater runoff are considered. Pollutographs for several water quality parameters are generated for the stormwater runoff. Temporal and spatial variations of the stormwater runoff are incorporated using these pollutographs for different land-uses. Furthermore, pollutographs are developed for different storm conditions, including single, two consecutive and migrating storms. Evolutionary algorithms are extensively used in solving the developed multiobjective optimization approach. Formulations for two different optimization approaches, one for the snapshot optimization and the other one for the dynamic optimization are developed. Simulation results from a full hydraulic model, including water quality routing are used in the optimization. The performance of the multi-objective optimization models are tested on a simple interceptor sewer system for several storm conditions. The proposed optimization approach for snapshot optimization gives the optimal CSO control settings where a single set of static control settings is used throughout the considered time period. However, the proposed optimization approach for dynamic optimization is capable of producing control strategies over the full duration of storm period. Furthermore, results for a number of alternative formulations in constraint handling for the developed multi-objective optimization approach are compared. They produce interesting findings. Overall, the constraint handling formulations developed outside the genetic (NSGA II) algorithm provides better control in combined sewer networks. In addition, the results of the multi-objective optimization demonstrate the benefits of the usage of optimization approach and its potential to establish the key properties of a range of control strategies through an analysis of the various tradeoffs involved. Solutions from the dynamic optimization approach highlight the usage of the real-time control in combined sewer systems. Given that the technology is there to measure water quality and flow rates, collect data and send feedbacks to the sewer system through central processing unit and the usage of high performance computers, the developed optimization model is capable of handing the present society's concerns in combined sewer systems. The model is capable of controlling the existing sewer networks according to the receiving water regulations and the fund availability of the wastewater treatment plants. However, further research is required to apply the developed multi-objective optimization approach in real-time control of urban sewer systems.
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Lu, Boran. "Power management of hybrid military vehicles using optimal control." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/13774.
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Master of ScienceDepartment of Electrical and Computer Engineering
Balasubramaniam Natarajan
Noel Schulz
With increasing costs for fuel there is a growing interest in improving fuel efficiency and performance of military vehicles by employing (1) hybrid drive train architecture; (2) reliable vehicle power system structure, and (3) effective power management strategies of multiple power sources (engine, battery and ultracapacitor) and vehicle electrical loads. However, current ruled-based power management strategies that focus primarily on traction fail to meet the rapidly increasing requirements of military vehicles, including: (1) better fuel economy; (2) the ability to support pulsed power weapon loads; (3) maintaining battery SOC for power offloading applications, and (4) the ability to perform load scheduling of vehicle non-traction electrical loads to save energy. In this thesis, we propose an optimal control based algorithm in conjunction with a rule-based control strategy to optimally manage three power sources (engine, battery and pulsed power supply module) and an effective power management solution for vehicle non-traction electrical loads such that: (1) all traction, non-traction and pulsed power needs are met; (2) power drawn from the engine for specific mission is minimized; (3) a certain desired battery SOC is guaranteed for offloading power, and (4) the ability to perform load scheduling based on different mission requirements. The proposed approach is validated using simulation of a mission specific profile and is compared with two other popular control strategies. The improvements in power efficiency, desired SOC level and ability to perform optimal load scheduling are demonstrated.
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O'Dell, Susan W. (Susan Wynne). "Optimal control strategies for a rail transit line." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10507.
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Ricard, Michael J. (Michael Jon). "Optimization of queueing networks : an optimal control appraoch." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11877.
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Hackett, Sean. "The optimal control of dynamic pest populations." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:5035e7a5-1d81-4288-8eb0-ec05b2fd95a2.
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In the management of agricultural insect pests, short-term costs must be balanced against long-term benefits. Controls should be selected to account for both their immediate and downstream effects upon the demography and genetics of the pest, enabling suppression today without threatening suppression tomorrow. The iterative, algorithmic method of dynamic programming can provide optimal solutions to problems of this type, in which actions are taken sequentially and each action may influence those which follow it. However, this approach is fundamentally constrained with regards to the magnitude of the problems it may solve. As questions of insect pest management can be subject to ecological and evolutionary complexities, this may place them beyond the scope of dynamic programming. When it is the intricacies of a problem that are of interest, it may be more productive to utilise approximate dynamic programming (ADP) methods which can attempt problems of arbitrary complexity, although at the expense of no longer guaranteeing optimality. In this thesis I first challenge a dynamic programming algorithm with the management of a hypothetical insect pest feeding upon a transgenic insecticidal crop. The model explores how different realisations of fitness costs to resistance influence the algorithms suggested actions. I then apply a brute-force variant of ADP, a lookahead policy, to the management of a stage-structured, continuously reproducing pest population. This was to explore the extent to which an algorithm with a limited temporal perspective is able to balance the timetable of pest demography against the timescale over which insecticidal sprays and bisex-lethal sterile insect releases unfold. This same decision framework is then applied to a modified problem in which resistance to insecticidal toxins may evolve and releases are now male-selecting. This was used to assess the efficacy with which simple lookahead policies utilise a control with delayed benefits (the male-selecting releases) and possible constraints on their capacity to respond to resistance evolution. Dynamic programming and ADP methods offer a versatile toolbox for accounting for the potential impacts of the evolutionary and ecological peculiarities of particular pests upon control decisions.
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Xabadia, i. Palmada Àngels. "Optimal management of natural resources. Accounting for heterogeneity." Doctoral thesis, Universitat de Girona, 2003. http://hdl.handle.net/10803/7699.
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Dynamic optimization methods have become increasingly important over the last years in economics. Within the dynamic optimization techniques employed, optimal control has emerged as the most powerful tool for the theoretical economic analysis. However, there is the need to advance further and take account that many dynamic economic processes are, in addition, dependent on some other parameter different than time. One can think of relaxing the assumption of a representative (homogeneous) agent in macro- and micro-economic applications allowing for heterogeneity among the agents. For instance, the optimal adaptation and diffusion of a new technology over time, may depend on the age of the person that adopted the new technology. Therefore, the economic models must take account of heterogeneity conditions within the dynamic framework.This thesis intends to accomplish two goals. The first goal is to analyze and revise existing environmental policies that focus on defining the optimal management of natural resources over time, by taking account of the heterogeneity of environmental conditions. Thus, the thesis makes a policy orientated contribution in the field of environmental policy by defining the necessary changes to transform an environmental policy based on the assumption of homogeneity into an environmental policy which takes account of heterogeneity. As a result the newly defined environmental policy will be more efficient and likely also politically more acceptable since it is tailored more specifically to the heterogeneous environmental conditions. Additionally to its policy orientated contribution, this thesis aims making a methodological contribution by applying a new optimization technique for solving problems where the control variables depend on two or more arguments --- the so-called two-stage solution approach ---, and by applying a numerical method --- the Escalator Boxcar Train Method --- for solving distributed optimal control problems, i.e., problems where the state variables, in addition to the control variables, depend on two or more arguments.
Chapter 2 presents a theoretical framework to determine optimal resource allocation over time for the production of a good by heterogeneous producers, who generate a stock externalit and derives government policies to modify the behavior of competitive producers in order to achieve optimality. Chapter 3 illustrates the method in a more specific context, and integrates the aspects of quality and time, presenting a theoretical model that allows to determine the socially optimal outcome over time and space for the problem of waterlogging in irrigated agricultural production. Chapter 4 of this thesis concentrates on forestry resources and analyses the optimal selective-logging regime of a size-distributed forest.
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SARAF, ADITYA P. "EFFICIENT ALGORITHMS FOR OPTIMAL ARRIVAL SCHEDULING AND AIR TRAFFIC FLOW MANAGEMENT." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1190986679.
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Le, rhun Arthur. "Stochastic optimal control for the energy management of hybrid electric vehicles under traffic constraints." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX084/document.
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Cette thèse aborde la conception d'un Système de Gestion Énergétique (EMS), prenant en compte les contraintes de trafic, pour un véhicule hybride électrique. Actuellement, les EMS sont habituellement classé en deux catégories ceux proposant une architecture en temps réel cherchant un optimum local, et ceux qui recherchent un optimum global, plus coûteux en temps de calcul et donc plus approprié à un usage hors ligne. Cette thèse repose sur le fait que la consommation énergétique peut être modélisée précisément à l'aide de distributions de probabilité sur la vitesse et l'accélération. Dans le but de réduire la taille des données, une classification est proposé, basé sur la distance de Wasserstein, les barycentres des classes pouvant être calculés grâce aux itérations de Sinkhorn ou la méthode du Gradient Stochastique Alterné. Cette modélisation trafic a permis à une optimisation hors ligne de déterminer le contrôle optimal (le couple du moteur électrique) qui minimise la consommation de carburant du véhicule hybride sur un segment routier. Dans la continuité, un algorithme bi-niveau tirant avantage de cette information afin d'optimiser la consommation sur l'ensemble du trajet. Le niveau supérieur d'optimisation, étant déterministe, est suffisamment rapide pour une implémentation en temps réel. La pertinence du modèle de trafic et de la méthode bi-niveau est illustré à l'aide de données trafic générées par un simulateur, mais aussi grâce à des données réelles collectées prés de Lyon (France). Enfin, une extension de la méthode bi-niveau au problème d'éco-routage est envisagé, utilisant un graphe augmenté pour déterminer l'état de charge lors du chemin optimalThe focus of this PhD thesis is to design an optimal Energy Management System (EMS) for a Hybrid Electric Vehicle (HEV) following traffic constraints.In the current state of the art, EMS are typically divided between real-time designs relying on local optimization methods, and global optimization that is only suitable for off-line use due to computational constraints.The starting point of the thesis is that in terms of energy consumption, the stochastic aspect of the traffic conditions can be accurately modelled thanks to (speed,acceleration) probability distributions.In order to reduce the data size of the model, we use clustering techniques based on the Wasserstein distance, the corresponding barycenters being computed by either a Sinkhorn or Stochastic Alternate Gradient method.Thanks to this stochastic traffic model, an off-line optimization can be performed to determine the optimal control (electric motor torque) that minimizes the fuel consumption of the HEV over a certain road segment.Then, a bi-level algorithm takes advantage of this information to optimize the consumption over a whole travel, the upper level optimization being deterministic and therefore fast enough for real-time implementation.We illustrate the relevance of the traffic model and the bi-level optimization, using both traffic data generated by a simulator, as well as some actual traffic data recorded near Lyon (France).Finally, we investigate the extension of the bi-level algorithm to the eco-routing problem, using an augmented graph to track the state of charge information over the road network
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Liu, Kai, and 劉愷. "Optimal dispatch and management for smart power grid." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46336680.
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Afful-Dadzi, Anthony. "Robust Optimal Maintenance Policies and Charts for Cyber Vulnerability Management." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354126687.
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Kashif, Muhammad. "Application of Stochastic Optimal Control in Finance." Doctoral thesis, Università degli studi di Bergamo, 2018. http://hdl.handle.net/10446/77397.
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Asset allocation theory and practice has been applied to many problems of institutional investors. In this dissertation, we consider the following two problems:
Abstract i) Optimal portfolio and spending rules for endowment funds.
Abstract ii) Capital adequacy management for banks in the Lévy market.
Abstract Part I: We investigate the role of different spending rules in a dynamic asset allocation model for an endowment fund. In particular, we derive the optimal portfolios under the consumption-wealth ratio rule (CW strategy) and the hybrid rule (hybrid strategy) and compare them with a theoretically optimal (Merton's) strategy for both spending and portfolio allocation. Furthermore, we show that the optimal portfolio is less risky with habit as compared with the optimal portfolio without habit. Similarly, the optimal portfolio under hybrid strategy is less risky than both CW and Merton's strategy for given set of constant parameters. Thus, endowments following hybrid spending rule use asset allocation to protect spending. Our calibrated numerical analysis on US data shows that the consumption under hybrid strategy is less volatile as compared to other strategies. However, hybrid strategy comparatively outperforms the conventional Merton's strategy and CW strategy when the market is highly volatile but under-performs them when there is a low volatility. Overall, the hybrid strategy is effective in terms of stability of spending and intergenerational equity because, even if it allows fluctuation in spending in the short run, it guarantees the convergence of spending towards its long term mean.
Abstract Part II: We investigate the capital adequacy management and asset allocation problems for a bank whose risk process follows a jump-diffusion process. Capital adequacy management problem is based on regulations in Basel III Capital Accord such as the capital adequacy ratio (CAR) which is calculated by the dividing the bank capital by total risk-weighted assets (TRWAs). Capital adequacy management requires a bank to reserve a certain amount for liquidity. We derive the optimal investment portfolio for a bank with constant absolute risk aversion (CARA) preferences and then the capital adequacy ratio process of the bank is derived, conditional on the optimal policy chosen.
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Ingalls, Brian Paul. "Conflict resolution in air traffic management using the methods of optimal control theory." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq24857.pdf.
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Duku, Leju. "Evaluating the optimal innovative cost control techniques used in the South African construction industry." Master's thesis, Faculty of Engineering and the Built Environment, 2020. http://hdl.handle.net/11427/32604.
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The execution of construction projects commands a myriad of technological, human, organisational and natural resources. However, the construction and engineering undertaking of these projects are frequently overshadowed by economic difficulties, such as the high costs of construction materials, that have a negative impact on project costs. Cost overruns have been determined as a phenomenon continually plaguing the construction industry in both private and public sectors, and very few projects are completed within cost parameters. This research evaluated the barriers to the use of innovative cost control techniques during the construction phase, and determined the level of cost overruns on construction projects in South Africa; identified innovative cost control techniques used by construction firms on construction projects; established the optimal innovative cost control technique used in the South African construction industry; and uncovered the relationship between the level of use of innovative cost control techniques on construction projects and cost overrun. Questionnaires were the chosen instrument for data collection and were circulated via Survey Monkey. A total of 123 questionnaires were returned, and they provided the base for the computation of study results. Statistical tools employed in the study included percentages, mean item score (MIS), and frequency distributions. A scatter plot was used to distinguish whether there was a correlation between the cost performance of projects and level of innovativeness by establishing a line of best fit through the set of the two variables. A line of best fit in the positive direction indicates that increased levels of innovativeness improves the cost performance of projects, while a line of best fit in the negative direction indicates that increased levels of innovativeness does not enhance project performance. The relationship between the level of innovative cost control techniques usage in construction projects and cost overrun was determined to be negative. This led to the conclusion that construction professionals are limiting themselves and are not exploring alternative or innovative cost control techniques. They were focused on project efficiency and productivity rather than cost overruns. Innovative cost control techniques identified in the study were Earned Value Analysis (EVA), Last Planner System (LPS), 4D Scheduling, Fuzzy Project Scheduling, Integrated critical path and Line of Balance, and Reserve Analysis. Study findings determined that the critical contributors to cost overruns included tight project budgets, project complexity, a high frequency of change orders by clients and financial difficulties encountered by contractors. Perceived barriers to the implementation of innovative cost control techniques in projects by participants included a poor scope definition, a lack of training and technical skill of project personnel, poor understanding of cost analysis and variables involved in cost planning. It also emerged that projects cannot meet project objectives, and construction organisations are not making use of the right tools and techniques to monitor and control construction costs. The research findings have shown that professionals have limited knowledge of innovative cost control techniques. This also concludes that they are not taking advantage of the features of new innovative techniques to tackle complex projects. This, therefore, means that complex projects will continue to experience cost overruns. This study concludes that top management of construction organisations are not training their staff to embrace new technologies and innovation. To address the barriers to the use of innovative techniques, there should be increased investment on the part of construction organisations toward affording their workforce the relevant training, knowledge and technical skill required to implement the modern techniques for cost control identified in the report. The cidb should organise seminars and workshops on the usefulness and importance of innovative cost control techniques, and workers should embrace self-development and change. Government should implement policies on the use of innovative cost control techniques for their projects, and construction organisations should develop capacity in line with innovative cost control techniques.
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McGibbon, Fraser J. "Free flight to managed airspace transfer using an optimal time-based scheduling method." Thesis, University of Glasgow, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272861.
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Scheer, Natalie [Verfasser], Hanspeter [Akademischer Betreuer] Schmidli, and Josef [Akademischer Betreuer] Steinebach. "Optimal stochastic control of dividends and capital injections / Natalie Scheer. Gutachter: Hanspeter Schmidli ; Josef Steinebach." Köln : Universitäts- und Stadtbibliothek Köln, 2011. http://d-nb.info/1038065259/34.
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POZZI, ANDREA. "Optimal Control and Reinforcement-Learning Strategies for Advanced Management of Lithium-ion Battery Packs." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1436359.
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The use of mathematical models and predictive control in advanced battery management is fundamental in order to achieve high performance, especially in the case of large battery packs in which several cells are arranged both in series and parallel connections. A basic requirement for the development of predictive control is the exploitation of an accurate model. Within this context, as a first contribution, this Thesis provides a thorough investigation of optimal design of experiments methodologies which are able to enhance the identifiability of electrochemical battery models, relying both on the concept of Fisher information, which exhibits a local nature, and global sensitivity analysis. A further contribution is provided in the battery control field, where dissipation and ageing-aware fast-charging strategies are developed for a single lithium-ion cell. Subsequently, model-predictive control is exploited for both the state-of-charge balancing of series-connected cells and the optimal charge of a whole battery pack. In the latter case cells connected in series and parallel arrangements are considered and a sensitivity-based linearization of the overall model is proposed in order to maintain the computational burden at a reasonable level.
Finally, the use of model-free reinforcement learning is considered with application to battery fast charging and optimal velocity planning for autonomous hybrid electric vehicles in an urban context.
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Brand, Bastian [Verfasser]. "Management control systems : subcomponents, optimal design and the role of time as a contingency / Bastian Brand." Mainz : Universitätsbibliothek Mainz, 2014. http://d-nb.info/1046248340/34.
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Brand, Bastian [Verfasser]. "Management Control Systems - Subcomponents, optimal design and the role of time as a contingency / Bastian Brand." München : Verlag Dr. Hut, 2013. http://d-nb.info/1045988987/34.
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AbdelMeguid, Hossam Saadeldin. "Pressure, leakage and energy management in water distribution systems." Thesis, De Montfort University, 2011. http://hdl.handle.net/2086/4905.
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A fast and efficient method to calculate time schedules for internal and boundary PRVs and flow modulation curves has been developed and implemented. Both time and flow modulation can be applied to a single inlet DMA. The time modulation methodology is based on solving a nonlinear programming problem (NLP). In addition, Genetic Algorithms (GA) has been proposed and investigated to calculate the optimal coefficients of a second order relationship between the flow and the outlet pressure for a PRV to minimize the background leakage. The obtained curve can be subsequently implemented using a flow modulation controller in a feedback control scheme. The Aquai-Mod® is a hydraulic device to control and modulate the outlet pressure of a PRV according to the valve flow. The controller was experimentally tested to assess its performance and functionality in different conditions and operating ranges. The mathematical model of the controller has been developed and solved, in both steady state and dynamic conditions. The results of the model have been compared with the experimental data and showed a good agreement in the magnitude and trends. A new method for combined energy and pressure management via integration and coordination of pump scheduling with pressure control aspects has been created. The method is based on formulating and solving an optimisation NLP problem and involves pressure dependent leakage. The cost function of the optimisation problem represents the total cost of water treatment and pumping energy. Developed network scheduling algorithm consists of two stages. The first stage involves solving a continuous problem, where operation of each pump is described by continuous variable. Subsequently, the second stage continuous pump schedules are discretised using heuristic algorithm. Another area of research has been developing optimal feedback rules using GA to control the operation of pump stations. Each pump station has a rule described by two water levels in a downstream reservoir and a value of pump speed for each tariff period. The lower and upper water switching levels of the downstream reservoir correspond to the pump being “ON” or “OFF”. The achieved similar energy cost per 1 Ml of pumped water. In the considered case study, the optimal feedback rules had advantage of small number of ON/OFF switches, which increase the pump stations lifetime and reduce the maintenance cost as well.
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Zhang, Yunlong. "Optimal traffic control for a freeway corridor under incident conditions." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-145027/.
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Yaegashi, Yuta. "Stochastic Optimal Control Models for Management of Plecoglossus altivelis under Predation Pressure from Phalacrocorax carbo." Kyoto University, 2020. http://hdl.handle.net/2433/253324.
Full textAbstract:
Kyoto University (京都大学)0048
新制・課程博士
博士(農学)
甲第22488号
農博第2392号
新制||農||1076(附属図書館)
学位論文||R2||N5268(農学部図書室)
京都大学大学院農学研究科地域環境科学専攻
(主査)教授 藤原 正幸, 教授 村上 章, 准教授 宇波 耕一
学位規則第4条第1項該当
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Güller, Mustafa [Verfasser], and Bernd [Akademischer Betreuer] Noche. "Optimal Inventory Control and Distribution Network Design of Multi-Echelon Supply Chains / Mustafa Güller. Betreuer: Bernd Noche." Duisburg, 2016. http://d-nb.info/1102896934/34.
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Park, Sang Gyun. "Optimal control based method for design and analysis of continuous descent arrivals." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53005.
Full textAbstract:
Continuous Descent Arrival (CDA) is a procedure where aircraft descend, at or near idle thrust, from their cruise altitude to their Final Approach Fix without leveling off. By eliminating inefficient leveling off at low altitude, CDA provides benefits such as fuel savings, flight time savings, and the significant noise reduction near airports, but the usage of CDAs has been limited in low traffic condition due to difficulty in the separation management. For the successful CDA without degradation of the runway throughput, air traffic controllers should know the performance bound of the CDA trajectory and control the time of arrival for each aircraft, which is interpreted as Required Time of Arrival (RTA) from the aircraft standpoint.
This thesis proposes a novel trajectory optimization methodology to meet RTA constraint. The CDA trajectory optimization problem in the flight management system is modeled as a path constrained optimal control problem of switched dynamical system. A sequential method that performs mode sequence estimation and parameter optimization, sequentially, is proposed to solve this problem. By analyzing the relaxed optimal solution with simplified dynamics, a computationally efficient algorithm to find the optimal switching structure is proposed and applied for the mode sequence estimation. This thesis also proposes a performance-bound analysis methodology using optimal control techniques to help controllers make a feasible schedule for CDA operations at a meter fix. The feasible time range analysis for a wide variety of aircraft is performed by using the proposed methodology. Based on the analysis result, a single flight time strategy is proposed for the application of CDA in high traffic conditions. The simulation with real traffic data has been shown that the single flight time strategy, combined with the proposed fixed RTA trajectory optimization, guarantees the conflict free CDA operation.
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Uebel, Stephan, and Bernard Bäker. "Optimal Velocity and Power Split Control of Hybrid Electric Vehicles." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-219665.
Full textAbstract:
An assessment study of a novel approach is presented that combines discrete state-space Dynamic Programming and Pontryagin’s Maximum Principle for online optimal control of hybrid electric vehicles (HEV). In addition to electric energy storage and gear, kinetic energy and travel time are considered states in this paper. After presenting the corresponding model using a parallel HEV as an example, a benchmark method with Dynamic Programming is introduced which is used to show the solution quality of the novel approach. It is illustrated that the proposed method yields a close-to-optimal solution by solving the
optimal control problem over one hundred thousand times faster than the benchmark method. Finally, a potential online usage is assessed by comparing solution quality and calculation time with regard to the quantization of the state space.
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Sahinci, Erin. "Optimal estimation of discrete fault probabilities using a stochastic state model." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/16888.
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Bright, John Charles. "Optimal control of irrigation systems : an analysis of water allocation rules." Lincoln College, University of Canterbury, 1986. http://hdl.handle.net/10182/2089.
Full textAbstract:
A feasibility study of an irrigation development proposal should include an analysis of the effects of water supply conditions on the degree to which development objectives are expected to be realised. A method of making this analysis was developed based on procedures for solving two problems. These were; (a) optimally allocating a property's available supply of water among competing crops, and, (b) optimally controlling an open channel distribution system to meet temporally and spatially varying water demand. The procedure developed for solving (a) was applied. A stochastic dynamic programming procedure was developed to optimally schedule the irrigation of a single crop, subject to constraints on the timing of water availability and total application depth. A second procedure was developed, employing a constrained differential dynamic programming algorithm, for determining optimal irrigation schedules for use with variable application depth systems, and when several crops compete for an intra-seasonally limited supply of water. This procedure was called, as frequently as water supply conditions allowed, to provide short-term irrigation schedules in a computer simulation of the optimal irrigation of several crops. An application system model was included in these procedures to transform a crop water-use production function into the required irrigation water-use production function. This transformation was a function of the application device type and the mean application depth. From an analysis of the on-property effects of water supply conditions, it was concluded that in order to achieve high economic and irrigation efficiencies, water supply conditions must be sufficiently flexible to allow the application system operator to vary the mean application depth but not necessarily the time periods of water availability. Additionally, irrigation scheduling procedures which seek economically optimum strategies offer the potential to achieve a maximum level of net benefit at levels of water availability significantly lower than has previously been used for design purposes.
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Bovee, Katherine Marie. "Optimal Control of Electrified Powertrains with the Use of Drive Quality Criteria." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1448273973.
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Almgren, Johan, and Gustav Elingsbo. "Route Based Optimal Control Strategy for Plug-In Hybrid Electric Vehicles." Thesis, Linköpings universitet, Fordonssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138713.
Full textAbstract:
More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.
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Sampathnarayanan, Balaji. "Analysis and Design of Stable and Optimal Energy Management Strategies for Hybrid Electric Vehicles." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357079732.
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Vagg, Christopher. "Optimal control of hybrid electric vehicles for real-world driving patterns." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648946.
Full textAbstract:
Optimal control of energy flows in a Hybrid Electric Vehicle (HEV) is crucial to maximising the benefits of hybridisation. The problem is complex because the optimal solution depends on future power demands, which are often unknown. Stochastic Dynamic Programming (SDP) is among the most advanced control optimisation algorithms proposed and incorporates a stochastic representation of the future. The potential of a fully developed SDP controller has not yet been demonstrated on a real vehicle; this work presents what is believed to be the most concerted and complete attempt to do so. In characterising typical driving patterns of the target vehicles this work included the development and trial of an eco-driving driver assistance system; this aims to reduce fuel consumption by encouraging reduced rates of acceleration and efficient use of the gears via visual and audible feedback. Field trials were undertaken using 15 light commercial vehicles over four weeks covering a total of 39,300 km. Average fuel savings of 7.6% and up to 12% were demonstrated. Data from the trials were used to assess the degree to which various legislative test cycles represent the vehicles’ real-world use and the LA92 cycle was found to be the closest statistical match. Various practical considerations in SDP controller development are addressed such as the choice of discount factor and how charge sustaining characteristics of the policy can be examined and adjusted. These contributions are collated into a method for robust implementation of the SDP algorithm. Most reported HEV controllers neglect the significant complications resulting from extensive use of the electrical powertrain at high power, such as increased heat generation and battery stress. In this work a novel cost function incorporates the square of battery C-rate as an indicator of electric powertrain stress, with the aim of lessening the affliction of real-world concerns such as temperatures and battery health. Controllers were tested in simulation and then implemented on a test vehicle; the challenges encountered in doing so are discussed. Testing was performed on a chassis dynamometer using the LA92 test cycle and the novel cost function was found to enable the SDP algorithm to reduce electrical powertrain stress by 13% without sacrificing any fuel savings, which is likely to be beneficial to battery health.
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Peirano, Fabio. "Development of Optimal Energy Management Strategies for a Hybrid Boat." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Find full textAbstract:
The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track
time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and
time optimization targets, but the latter adds huge tuning and calculation complexity.
In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.
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YANG, DONGMEI. "A DYNAMIC PROGRAMMING APPROACH TO OPTIMAL CENTER DELAY ALLOCATION." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116120758.
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Escobar, Fernández de la Vega Marcos. "Systematic procedure to meet specific input/output constraints in the l₁-optimal control problem design." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37143.
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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995; and, (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1996.Includes bibliographical references (leaves 86-87).
by Marcos Escobar Fernández de la Vega.
M.S.
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Scharin, Henrik. "Management of eutrophicated coastal zones : the quest for an optimal policy under spatial heterogeneity /." Uppsala : Dept. of Economics, Swedish Univ. of Agricultural Sciences, 2004. http://epsilon.slu.se/a503.pdf.
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Ng, Kwok-kei Simon, and 吳國基. "Optimal planning and management of stochastic demand and renewable energy in smart power grid." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B50434299.
Full textAbstract:
To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid.
With the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side.
A bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure.
As high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model.
In summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand response, have been extensively studied and interactively modeled to provide more intelligent planning and management for the smart grid.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Chen, Qiang. "Active queue management methods in computer communication networks based on pole placement and H-infinity optimal control." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/29343.
Full textAbstract:
This thesis studies the control of TCP traffic in the Internet nowadays. Both classical and modern control theory are used to design the controllers for the TCP/AQM system.
For the classical control theoretical approaches, we revisit the simple pole placement technique, and exploit this technique to propose four kinds of controllers for active queue management (AQM) in IP routers: the Proportional controller based on Pole Placement (the P_PP controller), the Proportional-plus-integral controller based on Pole Placement (the PI_PP controller), the Self Tuning Proportional controller based on Pole Placement (the ST_P_PP controller), and the Self-Tuning Proportional-plus-Integral controller based on Pole Placement (the ST_PI_PP controller). The transient performance indices---the damping ratio xi and the undamped natural frequency on, are appropriately chosen such that: (a) the transient response of the system is satisfied; (b) all the poles lie in the left-half of the s-plane to guarantee the stability of the system. The self-tuning controllers (the ST_P_PP and the ST_PI_PP) can maintain on-line tuning of the controller parameters so that good system performance can be ensured even under significant load changes.
This thesis also investigates the application of a robust modern control technique--- Hinfinity optimal control to the AQM controller. Two Hinfinity loop-shaping techniques, the S/U Mixed Sensitivity Problem (MSP) and the S/T/U MSP are applied to the controller design. Different weight functions and their impacts on the system performance are discussed and evaluated.
Finally, by modeling the uncertainties in the TCP/AQM system in terms of the uncertainty weight, this thesis also designs a robust AQM controller based on a modern control technique---robust mu analysis. The controller is synthesized by approximating the mu-optimal control problem with the Hinfinity S/T/U MSP. The NP (Nominal Performance), the RS (Robust Stability) and the RP (Robust Performance) of the system under the synthesized controller are analyzed using the mu analysis technique.
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Li, Zhi. "Fuzzy logic based robust control of queue management and optimal treatment of traffic over TCP/IP networks." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001461/.
Full textAbstract:
Improving network performance in terms of efficiency, fairness in the bandwidth, and system stability has been a research issue for decades. Current Internet traffic control maintains sophistication in end TCPs but simplicity in routers. In each router, incoming packets queue up in a buffer for transmission until the buffer is full, and then the packets are dropped. This router queue management strategy is referred to as Drop Tail. End TCPs eventually detect packet losses and slow down their sending rates to ease congestion in the network. This way, the aggregate sending rate converges to the network capacity. In the past, Drop Tail has been adopted in most routers in the Internet due to its simplicity of implementation and practicability with light traffic loads. However Drop Tail, with heavy-loaded traffic, causes not only high loss rate and low network throughput, but also long packet delay and lengthy congestion conditions. To address these problems, active queue management (AQM) has been proposed with the idea of proactively and selectively dropping packets before an output buffer is full. The essence of AQM is to drop packets in such a way that the congestion avoidance strategy of TCP works most effectively. Significant efforts in developing AQM have been made since random early detection (RED), the first prominent AQM other than Drop Tail, was introduced in 1993. Although various AQMs also tend to improve fairness in bandwidth among flows, the vulnerability of short-lived flows persists due to the conservative nature of TCP. It has been revealed that short-lived flows take up traffic with a relatively small percentage of bytes but in a large number of flows. From the user’s point of view, there is an expectation of timely delivery of short-lived flows. Our approach is to apply artificial intelligence technologies, particularly fuzzy logic (FL), to address these two issues: an effective AQM scheme, and preferential treatment for short-lived flows. Inspired by the success of FL in the robust control of nonlinear complex systems, our hypothesis is that the Internet is one of the most complex systems and FL can be applied to it. First of all, state of the art AQM schemes outperform Drop Tail, but their performance is not consistent under different network scenarios. Research reveals that this inconsistency is due to the selection of congestion indicators. Most existing AQM schemes are reliant on queue length, input rate, and extreme events occurring in the routers, such as a full queue and an empty queue. This drawback might be overcome by introducing an indicator which takes account of not only input traffic but also queue occupancy for early congestion notification. The congestion indicator chosen in this research is traffic load factor. Traffic load factor is in fact dimensionless and thus independent of link capacity, and also it is easy to use in more complex networks where different traffic classes coexist. The traffic load indicator is a descriptive measure of the complex communication network, and is well suited for use in FL control theory. Based on the traffic load indicator, AQM using FL – or FLAQM – is explored and two FLAQM algorithms are proposed. Secondly, a mice and elephants (ME) strategy is proposed for addressing the problem of the vulnerability of short-lived flows. The idea behind ME is to treat short-lived flows preferably over bulk flows. ME’s operational location is chosen at user premise gateways, where surplus processing resources are available compared to other places. By giving absolute priority to short-lived flows, both short and long-lived flows can benefit. One problem with ME is starvation of elephants or long-lived flows. This issue is addressed by dynamically adjusting the threshold distinguishing between mice and elephants with the guarantee that minimum capacity is maintained for elephants. The method used to dynamically adjust the threshold is to apply FL. FLAQM is deployed to control the elephant queue with consideration of capacity usage of mice packets. In addition, flow states in a ME router are periodically updated to maintain the data storage. The application of the traffic load factor for early congestion notification and the ME strategy have been evaluated via extensive experimental simulations with a range of traffic load conditions. The results show that the proposed two FLAQM algorithms outperform some well-known AQM schemes in all the investigated network circumstances in terms of both user-centric measures and network-centric measures. The ME strategy, with the use of FLAQM to control long-lived flow queues, improves not only the performance of short-lived flows but also the overall performance of the network without disadvantaging long-lived flows.
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Abbaszadeh, Chekan Jafar. "A Data Driven Real Time Control Strategy for Power Management of Plug-in Hybrid Electric Vehicles." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/95822.
Full textAbstract:
During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis.
This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range.MS
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Viljoen, Johannes Henning. "Modelling and optimal control of the market of a telecommunications operator." Diss., Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-09152004-100823.
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Kagiri, Charles Muiruri. "Optimal approach to energy management and gas delivery of a compressed natural gas station." Thesis, University of Pretoria, 2019. http://hdl.handle.net/2263/77831.
Full textAbstract:
The global growth in demand for transportation has been phenomenal, owing to an exponential
increase in population, industrialization and urbanization. This has led to a corresponding increase in
the number of motor vehicles on the roads globally which has made the transport industry one of the
main contributors to environmental pollution and energy insecurity. The profile of alternative fuels
has been rising as an important component of the solutions to the challenge of energy sustainability.
Compressed natural gas is one of the most successful alternative fuels for motor vehicle applications
because of its compatibility with the internal combustion engine, reduced engine maintenance costs,
reduced criteria air pollutants, low cost, abundance and the existence of renewable sourced natural gas
from biomass. The infrastructure for the delivery of compressed natural gas forms part of the primary
energy supply network, which has a significant interdependence with the electricity supply network.
The compressed natural gas fuelling station is one of the vital nodes of the gas delivery network, that
is also reliant on the electricity supply due to the energy intensive compressors that are required to
achieve the right pressure conditions for gas transfer to vehicle tanks.
At the same time, the increase in human population, industrialization, urbanization and market volatility
have threatened the reliability and stability of electricity supply networks. Traditional reliance on
supply upgrading to meet rising demand has proven to be unsustainable due to prohibitively high costs
and associated environmental impact. As a result, demand side management solutions, where better use
of the existing capacity is emphasized have received increasing attention. Demand side management
requires that electricity consumers also play a role in the efficient operation of the electricity grid by
minimizing their electricity usage as well as shifting their flexible loads away from peak electricity
demand periods, so that grid stability is sustained.
In order to participate in demand side management initiatives, operators of compressed natural gas
stations need technically and economically sound strategies for the operation of station compressors
and system components so that energy costs are minimized and gas transfer performance is enhanced.
The compressed natural gas fast-fill station, being the most used configuration for commercial fuelling
service is the focus of the work carried out in this thesis, with a description of solutions to minimize
energy consumption, minimize energy costs and improve gas transfer performance through reduction
of filling time.
For this purpose, firstly, an optimal control strategy that minimizes energy cost by shifting the compressor
load optimally away from the peak electricity pricing period under a time-of-use electricity
tariff, while meeting the gas demand is modelled and evaluated. The controller further minimizes the
switching frequency of the compressor thereby avoiding an increase in wear and tear which would
lead to higher maintenance costs. The results show the effectiveness of the optimal operation model to
achieve a huge reduction in electricity cost for the compressed natural gas station, when compressor-on
time is shifted to offpeak and standard electricity pricing times. Further strategies for the minimization
of switching frequency are compared and the superior approach identified.
Secondly, a hierarchical operation optimization model is designed and evaluated. The strategy achieves
minimized electricity cost and optimal vehicle filling time by optimally controlling the gas dispenser
and priority panel valve function under an optimised schedule of compressor operation. The results
show that the proposed approach is effective in achieving a minimum electricity costs in the upper
layer optimisation while meeting vehicle gas demand over the control horizon. Further, a reduction in
filling time is achieved through a lower layer model predictive control of the pressure-ratio-dependent
fuelling process.
Thirdly, an evaluation of compressor optimal sizing is carried out to minimize energy consumption
and cascade the benefits of optimal operation of the compressed natural gas compressor under the
time-of-use tariff. A comparison of the implication of using a variable speed drive or a fixed speed
drive which are optimally sized is carried out. Results show that indeed further reduction in electricity
costs for the compressed natural gas station is realized when optimally sized compressor drives are
used in combination with optimal operation strategies. Additionally, the four line priority panel is
evaluated for gas transfer performance and found to further increase the efficiency of vehicle fuelling
which is a performance indicator for consumer convenience.
The outcomes of this work demonstrate the effectiveness of the approaches proposed as necessary
to integrate compressed natural gas stations, which are vital nodes of the gas delivery network,
with the demand side management of the electricity grid while at the same time enhancing the gas
transfer performance. This increases the economic efficiency of the compressed natural gas as an
alternative fuel and also advances the goals of demand side management in electricity grid reliability
and stability.Thesis (PhD)--University of Pretoria, 2019.
Electrical, Electronic and Computer Engineering
PhD
Unrestricted
49
Zeng, Xiangrui. "Optimally-Personalized Hybrid Electric Vehicle Powertrain Control." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471342105.
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Macfie, Peter. "Large-scale security constrained optimal reactive power flow for operational loss management on the GB electricity transmission network." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5073.
Full textAbstract:
The transmission of power across the GB transmission system, as operated by National Grid, results in inevitable loss of electrical power. Operationally these power losses cannot be eliminated, but they can be reduced by adjustment of the system voltage profile. At present the minimisation of active power losses relies upon a lengthy manually based iterative adjustment process. Therefore the system operator requires the development of advanced optimisation tools to cope with the challenges faced over the next decade, such as achieving the stringent greenhouse gas emission targets laid down by the UK government, while continue to provide an economical, secure and efficient service. To meet these challenges the research presented in this thesis has developed optimisation techniques that can assist control centre engineers by automatically setting up voltage studies that are low loss and low cost. The proposed voltage optimisation techniques have been shown to produce solutions that are secured against 800 credible contingency cases. A prototype voltage optimisation tool has been deployed, which required the development of a series of novel approaches to extend the functionality of an existing optimisation program. This research has lead to the development of novel methods for handling multi-objectives, contradictory shunt switching configurations and selecting all credible contingencies. Studies indicate that a theoretical loss saving of 1.9% is achievable, equivalent to an annual emissions saving of approximately 64,000 tonnes of carbon dioxide. A novel security constrained mixed integer non-linear optimisation technique has also been developed. The proposed method has been shown to be superior to several conventional methods on a wide range of IEEE standard network models and also on a range of large-scale GB network models. The proposed method manages to further reduce active power losses and also satisfies all security constraints.