The development of a numeric simulation for predicting the performance of an Ocean Current Energy Conversion System is presented in this thesis along with a control system development using a PID controller for the achievement of specified rotational velocity set-points. In the beginning, this numeric model is implemented in MATLAB/Simulink® and it is used to predict the performance of a three phase squirrel single-cage type induction motor/generator in two different cases. The first case is a small 3 meter rotor diameter, 20 kW ocean current turbine with fixed pitch blades, and the second case a 20 meter, 720 kW ocean current turbine with variable pitch blades. Furthermore, the second case is also used for the development of a Voltage Source Variable Frequency Drive for the induction motor/generator. Comparison among the Variable Frequency Drive and a simplified model is applied. Finally, the simulation is also used to estimate the average electric power generation from the 720 kW Ocean Current Energy Conversion System which consists of an induction generator and an ocean current turbine connected with a shaft which modeled as a mechanical vibration system.

A continuous increase in demands from the utility grid and traction applications have steered public attention toward the integration of energy storage (ES) and hybrid ES (HESS) solutions. Modern technologies are no longer limited to batteries, but can include supercapacitors (SC) and flywheel electromechanical ES well. However, insufficient control and algorithms to monitor these devices can result in a wide range of operational issues. A modern day control platform must have a deep understanding of the source. In this dissertation, specialized modular Energy Storage Management Controllers (ESMC) were developed to interface with a variety of ES devices. The EMSC provides the capability to individually monitor and control a wide range of different ES, enabling the extraction of an ES module within a series array to charge or conduct maintenance, while remaining storage can still function to serve a demand. Enhancements and testing of the ESMC are explored in not only interfacing of multiple ES and HESS, but also as a platform to improve management algorithms. There is an imperative need to provide a bridge between the depth of the electrochemical physics of the battery and the power engineering sector, a feat which was accomplished over the course of this work. First, the ESMC was tested on a lead acid battery array to verify its capabilities. Next, physics-based models of lead acid and lithium ion batteries lead to the improvement of both online battery management and established multiple metrics to assess their lifetime, or state of health. Three unique HESS were then tested and evaluated for different applications and purposes. First, a hybrid battery and SC HESS was designed and tested for shipboard power systems. Next, a lithium ion battery and SC HESS was utilized for an electric vehicle application, with the goal to reduce cycling on the battery. Finally, a lead acid battery and flywheel ES HESS was analyzed for how the inclusion of a battery can provide a dramatic improvement in the power quality versus flywheel ES alone.

Ultra low power wireless sensors and sensor systems are of increasing interest in a variety of applications ranging from structural health monitoring to industrial process control. Electrochemical batteries have thus far remained the primary energy sources for such systems despite the finite associated lifetimes imposed due to limitations associated with energy density. However, certain applications (such as implantable biomedical electronic devices and tire pressure sensors) require the operation of sensors and sensor systems over significant periods of time, where battery usage may be impractical and add cost due to the requirement for periodic re-charging and/or replacement. In order to address this challenge and extend the operational lifetime of wireless sensors, there has been an emerging research interest on harvesting ambient vibration energy. Vibration energy harvesting is a technology that generates electrical energy from ambient kinetic energy. Despite numerous research publications in this field over the past decade, low power density and variable ambient conditions remain as the key limitations of vibration energy harvesting. In terms of the piezoelectric transducers, the open-circuit voltage is usually low, which limits its power while extracted by a full-bridge rectifier. In terms of the interface circuits, most reported circuits are limited by the power efficiency, suitability to real-world vibration conditions and system volume due to large off-chip components required. The research reported in this thesis is focused on increasing power output of piezoelectric transducers and power extraction efficiency of interface circuits. There are five main chapters describing two new design topologies of piezoelectric transducers and three novel active interface circuits implemented with CMOS technology. In order to improve the power output of a piezoelectric transducer, a series connection configuration scheme is proposed, which splits the electrode of a harvester into multiple equal regions connected in series to inherently increase the open-circuit voltage generated by the harvester. This topology passively increases the rectified power while using a full-bridge rectifier. While most of piezoelectric transducers are designed with piezoelectric layers fully covered by electrodes, this thesis proposes a new electrode design topology, which maximizes the raw AC output power of a piezoelectric harvester by finding an optimal electrode coverage. In order to extract power from a piezoelectric harvester, three active interface circuits are proposed in this thesis. The first one improves the conventional SSHI (synchronized switch harvesting on inductor) by employing a startup circuitry to enable the system to start operating under much lower vibration excitation levels. The second one dynamically configures the connection of the two regions of a piezoelectric transducer to increase the operational range and output power under a variety of excitation levels. The third one is a novel SSH architecture which employs capacitors instead of inductors to perform synchronous voltage flip. This new architecture is named as SSHC (synchronized switch harvesting on capacitors) to distinguish from SSHI rectifiers and indicate its inductorless architecture.

Cette thèse s'intéresse aux problèmes d'allocations des ressources et de puissance dans les réseaux cellulaires de quatrième génération (4G). Pour faire face à la demande continuellement croissante en débit des utilisateurs mobiles, les opérateurs n'ont d'autre choix que de densifier leurs infrastructures d'accès au réseau radio (RAN), afin de maximiser l'utilisation de la bande passante disponible dans l'espace. Un des défis de cette nouvelle architecture est la coexistence de nombreuses cellules voisines et la gestion des interférences co-canal qu'elles génèrent entre elles. De telles contraintes ont amené la communauté scientifique à s'intéresser aux réseaux auto-organisés et auto-optimisés (SON), qui permettent aux réseaux de s'optimiser localement via des décisions décentralisées (sans planification statique). L'intérêt principal de tels réseaux est le passage à l'échelle des algorithmes distribués et la possibilité de s'adapter dynamiquement à de nouveaux environnements. Dans cette optique, nous proposons l'étude de deux problèmes d'allocation de ressources. La première partie de cette thèse se concentre sur l'optimisation de l'usage des ressources, dans un contexte de transmission coordonnée par plusieurs stations de base (CoMP). Les performances de la coordination de stations de base sont évaluées, selon le critère de capacité uniforme, ainsi que le compromis entre l'efficacité spectrale et l'équité entre les utilisateurs. Nous proposons également une méthode généralisée et distribuée de sélection de l'ensemble de stations en coopération, afin d'optimiser le compromis efficacité-équité. Dans une seconde partie, nous nous intéressons à l'optimisation de l'allocation des ressources et de puissance, dans le but de minimiser la consommation électrique du réseau. Nous présentons deux algorithmes dont les décisions sont décentralisées. Le premier est basé sur une optimisation stochastique (via l'échantillonneur de Gibbs) et permet une optimisation globale du système. Le second quant à lui est basé sur l'adaptation de la théorie du contrôle et utilise des modèles prédictifs et la poursuite de cibles pour allouer les ressources et les puissances dans un contexte de canaux et d'interférences dynamiques. Dans de nombreux cas, plusieurs objectifs concurrents sont à considérer pour évaluer les performances d'un réseau (capacité totale, équité, consommation électrique, etc.). Dans le cadre de cette thèse, nous nous efforçons à présenter les résultats sous la forme de compromis multi-objectifs. / This thesis focuses on resources and power allocation problem in the fourth generation (4G) of cellular networks. To face the continuous growth of mobile users capacity requirements, operators need to densify their radio access network (RAN) infrastructure, to maximize the use of the available bandwidth in space. One of the major issues of this new architecture is the proximity of many base stations (BS) and the management of the interference they generate on each other's cell. Such constraints makes scientific community focus on Self-Optimized, Self-Organized Networks (SON) that allow network elements to optimize them-selves through decentralized decisions (no static network planning is required). A major interest of SON is their capability to scale to large and non-organized networks, as well as being able to adapt them-selves dynamically, by using distributed algorithms. In this context, this thesis proposes the study of two resource allocation problems. The first part of this thesis focuses on the optimisation of resource sharing, in the context of coordinated multi-points transmissions (CoMP). Performances of BS coordination are evaluated, using the uniform capacity criterion, as well as the trade-off between total capacity and fairness among users. We also propose a generalized and distributed method to select the set of coordination of BS, to optimize the capacity-fairness trade-off. In the second part of this thesis, we focus on optimizing the transmit power and resource allocation, in order to reduce electric consumption. We present two distributed algorithms: the first one is based on a stochastic optimisation (using Gibbs sampling), and tries to reach the global optimum state through decentralized decision. The second one is based on control theory, and uses target tracking as well as model predictive control to allocate resources and power in a dynamic channel scenario. In many cases, trade-offs are to be maid between opposite objectives when evaluating network performances (total throughput, fairness, energy consumption, etc.). In this thesis, we present most of the network performances using multi-objectives evaluations.

<div>The manufacturing cost of solar cells along with their efficiency and reliability define the levelized cost of electricity (LCOE). One needs to reduce LCOE to make solar cells cost competitive compared to other sources of electricity. After a sustained decrease since 2001 the manufacturing cost of the dominant photovoltaic technology based on c-Si solar cells has recently reached a plateau. Further reduction in LCOE is only possible by increasing the efficiency and/or reliability of c-Si cells. Among alternate technologies, organic photovoltaics (OPV) has reduced manufacturing cost, but they do not offer any LCOE gain because their lifetime and efficiency are significantly lower than c-Si. Recently, perovskite solar cells have showed promising results in terms of both cost and efficiency, but their reliability/stability is still a concern and the physical origin of the efficiency gain is not fully understood.</div><div><br></div>In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.<br>

Im Beitrag wird zunächst die konventionelle auf Signalflüssen basierte Modellbildung mit modernen leistungsflussbasierten Methoden, die auf dem Prinzip von Aktion und Reaktion aufbauen, verglichen. BG (Bond Graph), POG (Power Oriented Graph) und EMR (Energetic Macroscopic representation) sind solche modernen Methoden die den Leistungsaustausch zwischen Teilsystemen als Grundlage für den Modellbildungsansatz nutzen. Diese Werkzeuge erhalten die physikalische Struktur, erlauben es in das dynamische System hineinzuschauen und unterstützen das Verständnis des Leistungsflusses. Unterschiede werden anhand verschiedener Eigenschaften in einer Tabelle angegeben. Nach Erläuterung der Grundlagen zu POG und BG erfolgt die Vorstellung einer Freeware Zusatzbibliothek zur Simulation von Bondgraphen. Spezielle Eigenschaften werden kurz umrissen. Diese Blockbibliothek läuft unter Simulink, besteht aus nur 9 mittels Menü konfigurierbaren Blöcken und realisiert bidirektionale Verbindungen. Die Beispiele Gleichstrommotor, Pulssteller und elastische Welle demonstrieren die Vorteile der leistungsflussorientierten Modellbildung. Zustandsregelung, Energieeffizienz und Simulink LTI Analysewerkzeuge führen in die Anwendung der vorgestellten Simulink Zusatzbibliothek für Bondgraphen ein.:Modellbildung dynamischer Systeme mittels Leistungsfluß 1. Leistungsfluß versus Signalfluß 2. Konjugierte Leistungsvariablen und Kausalität 3. Grundlagen Leistungsfluß orientierter Modellierung 3.1 Definitionen zum POG (Power Oriented Graph) 3.2 Definitionen zu Grundelementen für Bondgraphen (BG) 4. Freeware Bibliothek zur Simulation von Bondgraphen 4.1 Übersicht und Nutzerfreundlichkeit 4.2 Besonderheiten 5. Beispiele 5.1 Gleichstrommotor mit starrer Welle 5.2 Elastische Welle 5.3 Eingangsfilter und Pulssteller 5.4 Vereinfachter Antriebsstrang 6. Anwendung der Simulink Bondgraph Blockbibliothek 6.1 Beispiel elastische Welle 6.2 Zustandsregelung 6.3 Energieeffizienz 6.4 Simulink Analysewerkzeuge 7. Vorteile im Überblick / The paper starts with a comparison of the conventional modelling method based on signal flow and modern power flow oriented modelling methods based on the principle of action and reaction. BG (Bond Graph), POG (Power Oriented Graph) and EMR (Energetic Macroscopic representation) are such modern methods based on the power exchange between partial systems as a key element for the basic modelling approach. These tools preserve the physical structure, enable a view inside dynamical systems and support understanding the power flow. Relationships between these graphical representations will be given. After the explanation of basics for POG and BG an overview and special features of a freeware add-on library for simulation of BGs will be outlined. The block library runs under Simulink, consists of nine menu-driven customised blocks only and realises bidirectional connections. Examples DC motor, chopper and elastic shaft demonstrate the advantages of power flow oriented modelling. State space control, energy efficiency and Simulink LTI analysis tools exemplify the application of the presented Simulink add-on BG library.:Modellbildung dynamischer Systeme mittels Leistungsfluß 1. Leistungsfluß versus Signalfluß 2. Konjugierte Leistungsvariablen und Kausalität 3. Grundlagen Leistungsfluß orientierter Modellierung 3.1 Definitionen zum POG (Power Oriented Graph) 3.2 Definitionen zu Grundelementen für Bondgraphen (BG) 4. Freeware Bibliothek zur Simulation von Bondgraphen 4.1 Übersicht und Nutzerfreundlichkeit 4.2 Besonderheiten 5. Beispiele 5.1 Gleichstrommotor mit starrer Welle 5.2 Elastische Welle 5.3 Eingangsfilter und Pulssteller 5.4 Vereinfachter Antriebsstrang 6. Anwendung der Simulink Bondgraph Blockbibliothek 6.1 Beispiel elastische Welle 6.2 Zustandsregelung 6.3 Energieeffizienz 6.4 Simulink Analysewerkzeuge 7. Vorteile im Überblick

<div>Energy-efficient communication has remained the primary bottleneck in achieving fully energy-autonomous IoT nodes. Several scenarios including In-Sensor-Analytics (ISA), Collaborative Intelligence (CI) and Context-Aware-Switching (CAS) of the cluster-head during CI have been explored to trade-off the energies required for communication and computation in a wireless sensor network deployed in a mesh for multi-sensor measurement. A real-time co-optimization algorithm was developed for minimizing the energy consumption in the network for maximizing the overall battery lifetime of individual nodes.</div><div><br></div><div>The difficulty of achieving the design goals of lifetime, information accuracy, transmission distance, and cost, using traditional battery powered devices has driven significant research in energy-harvested wireless sensor nodes. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetually-powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing event driven communication to minimize loss of information. We show results of continuous monitoring across 1Km without replacing the battery and maintaining an information accuracy of at least 95%.</div><div><br></div><div>Decades of continuous scaling in semiconductor technology has resulted in a drastic reduction in the cost and size of unit computing. This has enabled the design and development of small form factor wearable devices which communicate with each other to form a network around the body, commonly known as the Wireless Body Area Network (WBAN). These devices have found significant application for medical purposes such as reading surface bio-potential signals for monitoring, diagnosis, and therapy. One such device for the management of oropharyngeal swallowing disorders is described in this thesis. Radio wave transmission over air is the commonly used method of communication among these devices, but in recent years Human Body Communication has shown great promise to replace wireless communication for information exchange in a WBAN. However, there are very few studies in literature, that systematically study the channel loss of capacitive HBC for <i>wearable devices</i> over a wide frequency range with different terminations at the receiver, partly due to the need for <i>miniaturized wearable devices</i> for an accurate study. This thesis also measures and explores the channel loss of capacitive HBC from 100KHz to 1GHz for both high-impedance and 50Ohm terminations using wearable, battery powered devices; which is mandatory for accurate measurement of the HBC channel-loss, due to ground coupling effects. The measured results provide a consistent wearable, wide-frequency HBC channel loss data and could serve as a backbone for the emerging field of HBC by aiding in the selection of an appropriate operation frequency and termination.</div><div><br></div><div>Lastly, the power and security benefits of human body communication is demonstrated by extending it to animals (animal body communication). A sub-inch^3, custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation accuracy >99% when compared to traditional wireless communication modalities, with a 50x reduction in power consumption.</div>

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>