The normalization of the United States-Libya relations, 2003-2006

The purpose of this study is to examine and analyze the factors leading to the normalized relations between the United States (U.S.) and Libya in 2003–2006. The theoretical framework of this study was the rational actor model of foreign policy decision making, which held that the foreign policy decisions were made in such a way as to maximize benefits while minimizing costs. The study was divided into three parts. The first part looked at the political factors leading to the normalized relations between the two countries. These factors include the diplomatic, leadership and media. The second part looked at the economic factors such as the oil and economic sanctions. The last part looked at the security factors including terrorism and weapons of mass destruction, and the Libya‘s attitudes towards Israel. The data for this study were collected mainly from both primary and secondary sources. The primary sources included documents, agreements, and treaties that signed by Libya with the U.S. In addition, the researcher analyzed the outputs of the U.S. and Libyan policy- makers and institutions relating to the research topic, such as speeches, official correspondences, decrees, and decisions of both governments relating to each other. Interviews with knowledgeable people were also conducted. The secondary sources included books, journals, magazines and newspapers. Key findings over the period studied indicated the importance of the political, economic, and security factors in forwarding the U.S. policy options towards the normalization of the U.S. - Libya relations in 2006. Furthermore, this study also concluded that the success of the normalized relations was a product of intertwining of these factors together through their influences on policy- makers of both countries to take flexible attitudes to resolve outstanding issues between them

Real-time system identification and self-tuning control of DC-DC power converter using Kalman Filter approach

Ph. D. ThesisSwitch-mode power converters (SMPCs) are employed in many industrial and consumer devices. Due to the continuous reduction in cost of microprocessors, and improvements in the processing power, digital control solutions for SMPCs have become a viable alternative to traditional analogue controllers. However, in order to achieve high-performance control of modern DC-DC converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. This model can be acquired by means of prior knowledge about the system parameters or using system identification methods. For the best performance of the designed controller, the system identification methods are preferred to handle the model uncertainties such as component variations and load changes. This process is called indirect adaptive control, where the model is estimated from input and output data using a recursive algorithm and the controller parameters are tuned and adjusted accordingly. In the parameter estimation step, Recursive Least Squares (RLS) method and its modifications exhibit very good identification metrics (fast convergence rate, accurate estimate, and small prediction error) during steady-state operation. However, in real-time implementation, the accuracy of the estimated model using the RLS algorithm is affected by measurement noise. Moreover, there is a need to continuously inject an excitation signal to avoid estimator wind-up. In addition, the computational complexity of RLS algorithm is high which demands significant hardware resources and hence increase the overall cost of the digital system. For these reasons, this thesis presents a robust parametric identification method, which has the ability to provide accurate estimation and computationally efficient self-tuning controller suitable for real-time implementation in SMPCs systems. This thesis presents two complete real-time solutions for parametric system identification and explicit self-tuning control for SMPCs. The first is a new parametric estimation method, based on a state of the art Kalman Filter (KF) algorithm to estimate the discrete model of a synchronous DC-DC buck converter. The proposed method can accurately identify the discrete coefficients of the DC-DC converter. This estimator possesses the advantage of providing an independent strategy for adaptation of each individual parameter; thus offering a robust and reliable solution for real-time parameter estimation. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. This thesis also validates the performance of the identification algorithm with time-varying parameters; such as an abrupt load change. Furthermore, the proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Additionally, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical Exponentially weighted Recursive Least Square (ERLS) method. To design a computationally efficient self-tuning controller for DC-DC SMPCs, the second part of the thesis develops a complete package for real-time explicit self-tuning control. The novel partial update KF (PUKF) is introduced for real-time parameter estimation. In this approach, a significant complexity reduction is attained as the number of arithmetic operations are reduced, more specifically the computation of adaptation gains and covariance updates. The explicit self-tuning control scheme is constructed via integrating the developed PUKF with low complexity control algorithm such as Bányász/Keviczky PID controller. Experimental and simulation results clearly show an enhancement in the overall dynamic performance of the closed loop control system compared to the conventional PID controller designed based on a pre-calculated average model. Importantly, in this thesis, unlike a significant proportion of existing literature, the entire system identification, and closed loop control process is seamlessly implemented in real-time hardware, without any remote intermediate post processing analysis.Ministry of Higher Education, General Electricity Company of Liby

Computationally Efficient Self-Tuning Controller for DC-DC Switch Mode Power Converters Based on Partial Update Kalman Filter

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a DC-DC switch-mode power converter (SMPC). The proposed estimation algorithm is based on a novel combination between the classical Kalman filter and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the Kalman Filter (KF), with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky PID controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a pre-calculated average model

Real-time parameter estimation of DC-DC converters using a self-tuned kalman filter

To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method

Towards robotizing the processes of testing lithium-ion batteries

To boost the circular economy of the electric vehicle battery industry, an accurate assessment of the state of health of retired batteries is essential to assign them an appropriate value in the post automotive market and material degradation before recycling. In practice, the advanced battery testing techniques are usually limited to laboratory benches at the battery cell level and hardly used in the industrial environment at the battery module or pack level. This necessitates developing battery recycling facilities that can handle the assessment and testing undertakings for many batteries with different form factors. Towards this goal, for the first time, this article proposes proof of concept to automate the process of collecting the impedance data from a retired 24kWh Nissan LEAF battery module. The procedure entails the development of robot end-of-arm tooling that was connected to a Potentiostat. In this study, the robot was guided towards a fixed battery module using visual servoing technique, and then impedance control system was applied to create compliance between the end-of-arm tooling and the battery terminals. Moreover, an alarm system was designed and mounted on the robot’s wrist to check the connectivity between a Potentiostat and the battery terminals. Subsequently, the electrochemical impedance spectroscopy test was run over a wide range of frequencies at a 5% state of charge. The electrochemical impedance spectroscopy data obtained from the automated test is validated by means of the three criteria (linearity, causality and stability) and compared with manually collected measurements under the same conditions. Results suggested the proposed automated configuration can accurately accomplish the electrochemical impedance spectroscopy test at the battery module level with no human intervention, which ensures safety and allows this advanced testing technique to be adopted in grading retired battery modules

Roadmap for a sustainable circular economy in lithium-ion and future battery technologies

The market dynamics, and their impact on a future circular economy for lithium-ion batteries (LIB), are presented in this roadmap, with safety as an integral consideration throughout the life cycle. At the point of end-of-life (EOL), there is a range of potential options—remanufacturing, reuse and recycling. Diagnostics play a significant role in evaluating the state-of-health and condition of batteries, and improvements to diagnostic techniques are evaluated. At present, manual disassembly dominates EOL disposal, however, given the volumes of future batteries that are to be anticipated, automated approaches to the dismantling of EOL battery packs will be key. The first stage in recycling after the removal of the cells is the initial cell-breaking or opening step. Approaches to this are reviewed, contrasting shredding and cell disassembly as two alternative approaches. Design for recycling is one approach that could assist in easier disassembly of cells, and new approaches to cell design that could enable the circular economy of LIBs are reviewed. After disassembly, subsequent separation of the black mass is performed before further concentration of components. There are a plethora of alternative approaches for recovering materials; this roadmap sets out the future directions for a range of approaches including pyrometallurgy, hydrometallurgy, short-loop, direct, and the biological recovery of LIB materials. Furthermore, anode, lithium, electrolyte, binder and plastics recovery are considered in order to maximise the proportion of materials recovered, minimise waste and point the way towards zero-waste recycling. The life-cycle implications of a circular economy are discussed considering the overall system of LIB recycling, and also directly investigating the different recycling methods. The legal and regulatory perspectives are also considered. Finally, with a view to the future, approaches for next-generation battery chemistries and recycling are evaluated, identifying gaps for research. This review takes the form of a series of short reviews, with each section written independently by a diverse international authorship of experts on the topic. Collectively, these reviews form a comprehensive picture of the current state of the art in LIB recycling, and how these technologies are expected to develop in the future