The goal of this dissertation consists of improving the efficiency of energy harvesting using pyroelectric and piezoelectric materials in a system by the proper characterization of electrical parameters, widening frequency, and coupling of both effects with the appropriate parameters. A new simple stand-alone method of characterizing the impedance of a pyroelectric cell has been demonstrated. This method utilizes a Pyroelectric single pole low pass filter technique, PSLPF. Utilizing the properties of a PSLPF, where a known input voltage is applied and capacitance Cp and resistance Rp can be calculated at a frequency of 1 mHz to 1 Hz. This method demonstrates that for pyroelectric materials the impedance depends on two major factors: average working temperature, and the heating rate. Design and implementation of a hybrid approach using multiple piezoelectric cantilevers is presented. This is done to achieve mechanical and electrical tuning, along with bandwidth widening. In addition, a hybrid tuning technique with an improved adjusting capacitor method was applied. An toroid inductor of 700 mH is shunted in to the load resistance and shunt capacitance. Results show an extended frequency range up to 12 resonance frequencies (300% improvement) with improved power up to 197%. Finally, a hybrid piezoelectric and pyroelectric system is designed and tested. Using a voltage doubler, circuit for rectifying and collecting pyroelectric and piezoelectric voltages individually is proposed. The investigation showed that the hybrid energy is possible using the voltage doubler circuit from two independent sources for pyroelectrictity and piezoelectricity due to marked differences of optimal performance.

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate

Backup diesel generator (DG) systems continue to be a heavily polluting and costly solution for institutions with unreliable grid connections. These systems slow economic growth and accelerate climate change. Photovoltaic (PV), energy storage (ES), grid connected, DG – Hybrid Energy Systems (HESs) or, PV-HESs, can alleviate overwhelming costs and harmful emissions incurred from traditional back-up DG systems and improve the reliability of power supply. However, from project conception to end of lifetime, PV-HESs face significant barriers of uncertainty and variable operating conditions. The fit-and-forget solution previously applied to backup DG systems should not be adopted for PV-HESs. To maximize cost and emission reductions, PV-HESs must be adapted to their boundary conditions for example, irradiance, temperature, and demand. These conditions can be defined and monitored using measurement equipment. From this, an opportunity for performance optimization can be established. The method demonstrated in this study is a techno-economic and environmental approach to improving the performance of PV-HESs. The method has been applied to a case study of an existing PV-HES in Kenya. A combination of both analytical and numerical analyses has been conducted. The analytical analysis has been carried out in Microsoft Excel with the intent of being easily repeatable and practical in a business environment. Simulation analysis has been conducted in improved Hybrid Optimization by Genetic Algorithms (iHOGA), which is a commercially available software for simulating HESs. Using six months of measurement data, the method presented identifies performance inefficiencies and explores corrective interventions. The proposed interventions are evaluated, by simulation analyses, using a set of techno-economic and environment key performance indicators, namely: Net Present Cost (NPC), generator runtime, fuel consumption, total system emissions, and renewable fraction. Five corrective interventions are proposed, and predictions indicate that if these are implemented fuel consumption can be reduced by 70 % and battery lifetime can be extended by 28 %, net present cost can be reduced by 30 % and emissions fall by 42 %. This method has only been applied to a single PV-HES; however, the impact this method could have on sub-Saharan Africa as well as similar regions with unreliable grid connections is found to be significant. In the future, in sub-Saharan Africa alone, over $500 million dollars (USD) and 1.7 billion kgCO2 emissions could be saved annually if only 25 % of the fuel savings identified in this study were realized. The method proposed here could be improved with additional measurement data and refined simulation models. Furthermore, this method could potentially be fully automated, which could allow it to be implemented more frequently and at lower cost.

The increasing demand for renewable energy sources to meet climate targets and reduce carbon emissions poses challenges to the power grid due to their intermittent nature. One potential solution to maintain grid stability is by implementing Hybrid Energy Systems (HESs) that incorporate a Battery Energy Storage System (BESS). To achieve the most favorable outcome in terms of both technical feasibility and profitability of a BESS, it is essential to employ models for simulating and optimizing the control of system components. This thesis focuses on the analysis of energy and revenue streams in a HES consisting of a BESS, photovoltaics (PVs), and an energy load including a fast charging station for electric vehicles (EVs). The objective is to optimize the system based on revenue generation by comparing the control techniques of peak shaving, energy arbitrage, and the integration of ancillary services within the Swedish energy market. The research questions explore the optimal utilization of the BESS and assess the impact of the different control techniques. A model is created in Python with the package CasADi where data from an ongoing installation of a HES in southern Sweden is combined with data from literature research. The model includes an objective function that minimizes the total cost of power from the grid based on the day-ahead price, battery degradation, and monthly peak power.  To answer the research questions, four different scenarios are simulated. The first scenario is a base for comparison, the second one focuses on peak shaving and energy arbitrage, the third on participation in the ancillary service FCR-D upwards regulation, and the last one is a combination of peak shaving, energy arbitrage, and the ancillary service FCR-D. The results show that the remuneration from the ancillary service FCR-D is comparably much higher than the revenues generated from peak shaving and energy arbitrage, providing more than 500% of revenue compared to the same system but without a BESS. The scenario with peak shaving and energy arbitrage shows an increase in revenue of 29% but with more cycling of the battery which could cause losses in performance in the long term. To validate the results, sensitivity analyses are conducted by evaluating weighting in the objective function, implementing Model Predictive Control (MPC), and reviewing price variations.  In conclusion, efficient control techniques can enhance system performance, minimize losses, and ensure optimal utilization of different energy sources, leading to improved feasibility and profitability. The optimal usage of a BESS involves finding a balance between maximizing revenue generation and minimizing battery degradation. This can be achieved through control strategies that optimize the charging and discharging patterns of the BESS based on electricity price signals, demand patterns, and battery health considerations.

Technology advancements enable and encourage higher system electrifications in various applications. More electrified applications need more capable and higher performing sources of energy in terms of power delivery, power regeneration, and energy capacity. For example, in electric, hybrid electric, and plug-in hybrid electric vehicle applications (EVs, HEVs, and PHEVs), the power and energy ratings of the vehicle energy storage system (ESS) have a direct impact on the vehicle performance. Many researchers investigated and studied various aspects of hybrid energy storage systems (HESS) wherein multiple ESSs are combined together to share system loads, increase ESS capabilities, and cycle life. Various configurations and their application specific topologies were also proposed by other researchers; the potential of HESS has been proven to be very promising. In this research, the goal is to present the theory of a HESS configuration that has not been discovered thus far. This HESS configuration is called a series-parallel reconfigurable HESS (SPR-HESS) since it is capable of recombining multiple storage systems into different series, parallel, or series-parallel configurations, via power electronic converters, to accommodate different operation modes and load requirements. Simulations, as well as experimental verifications, are presented in this thesis. / Thesis / Doctor of Philosophy (PhD)

In this thesis, a physics-based single particle modeling is presented to analyze a proposed direct coupled hybrid energy storage modules using lithium-ion battery and ultracapacitor. Firstly, a state of the art for the energy storage system in the electrified vehicles are summarized. Several energy storage elements including lead-acid battery, nickel-metal hydride battery, lithium-ion battery, ultracapacitor, and lithium-ion capacitor are reviewed. Requirements of the energy storage systems in electric, hybrid electric, and plug-in hybrid electric vehicles are generalized. Typical hybrid energy storage system topologies are also reviewed. Moreover, these energy storage elements and hybrid energy storage system topologies are compared to the requirements of the energy storage systems in terms of specific power and specific energy. Secondly, the performance of different battery balancing topologies, including line shunting, ring shunting, synchronous flyback, multi-winding, and dissipative shunting are analyzed based on a linear programming methodology. As a traction battery in an electric or plug-in electric vehicle, high voltage lithium-ion packs are typically configured in a modular fashion, therefore, the analysis considers the balancing topologies at module level and cell level and focuses on minimum balancing time, minimum plug-in charge time, minimum energy loss, and component counts of every balancing topology for the entire battery pack. Thirdly, different modeling techniques for the lithium-ion battery and ultracapacitor are presented. One of the main contributions of this thesis is the development of a physics-based single particle modeling embedded with a solid-electrolyte interface growth model for a lithium-ion battery in battery management system. This development considers the numerical solution of diffusion equation, cell level quantities, parametrization method, effects of number of shells in a spherical particle, SOC-SOH estimation algorithms, and aging effects. The accuracy of the modeling is validated by experimental results of a Panasonic NCR18650A lithium-ion battery cell. Fourthly, the physics-based modeling is applied to analyze the performance of a proposed direct coupled hybrid energy storage module topology based on the Panasonic NCR18650A lithium-ion battery and Maxwell BCAP0350 ultracapacitor. There are many ways to directly connect battery cells and ultracapacitor cells in a module which would influence the performance of the module. The results show that a module has 9 cells in a battery string and 14 cells in an ultracapacitor string can obtain the highest power capability and utilize the most of the energy in an ultracapacitor. More ultracapacitor strings connected in parallel would increase the power density but reduce the energy density. Moreover, the simulation and experimental results indicate that the direct coupled hybrid modules can extend the operating range and slow the capacity fade of lithium-ion battery. An SOC-SOH estimation algorithm for the hybrid module is also developed based on the physics-based modeling. Finally, a pack design methodology is proposed to meet U.S. Advanced Battery Consortium LLC PHEV-40, power-assist, and 48V HEV performance targets for the battery packs or the proposed direct coupled topologies. In order to explore replacement tradeoffs between the battery and ultracapacitor, a case study of the direct coupled topologies is presented. From the case study, ultracapacitors enhance the power capability for short term pulse power and marginally reduce the cost of an entire energy storage system. Moreover, the hybrid module topologies can keep a relatively long all-electric range when the batteries degrade. / Dissertation / Doctor of Philosophy (PhD)

Solving the key issue of sustainability of battery-powered sensors continues to attract significant research attention. The prevailing theme of this research is to address this concern using energy-efficient protocols based on a form of simple cooperative transmission (CT) called the opportunistic large arrays (OLAs), and intelligent exploitation of energy harvesting and hybrid energy storage systems (HESSs). The two key contributions of this research, namely, OLA with transmission threshold (OLA-T) and alternating OLA-T (A-OLA-T), offer an signal-to-noise ratio (SNR) advantage (i.e., benefits of diversity and array (power) gains) in a multi-path fading environment, thereby reducing transmit powers or extending range. Because these protocols do not address nodes individually, the network overhead remains constant for high density networks or nodes with mobility. During broadcasting across energy-constrained networks, while OLA-T saves energy by limiting node participation within a single broadcast, A-OLA-T optimizes over multiple broadcasts and drains the the nodes in an equitable fashion. Another important contribution of this research is the design and analysis of a novel routing metric called communications using HESS (CHESS), which extends the rechargeable battery (RB)-life by relaying exclusively with supercapacitor (SC) energy, and is asymptotically optimal with respect to the number of nodes in the network.

Hybrid Energy Resource System (HERS) is studied and applied aroundworld in recent years. Control and monitor of them are quite important in realapplication. HERS also has the equirement to integral with power grid such asdistribution grid networks. Therefore, to design and implement the informationcommunication system following IEC 61850, which is most promising standard fordesign of substation communication and automation system, is necessary. This paperpresents the design of Information Communication Technology (ICT) architectureand Unified Modeling Language (UML) models and final implementation through LabVIEW programming for Smart Energy Container. Applying design following IEC61850 series standards allow the HERS can communicate and interoperate with other IEC61850 devices and SCADA systems. The implementation is applied to SmartEnergy Container which contains wind power, solar power, battery energy storagesystem, and hydrogen energy storage system. Verification and testing results shows thedesign is qualified to control and monitor Smart Energy Container. / Smart Energy Container

In diesem Beitrag wird eine Betriebsstrategie für einen hybriden Energiespeicher vorgestellt, die sich der Vorhersage zukünftiger Fahrzustände durch ein autonomes Fahrzeug bedient. Dies ermöglicht ein zusätzliches Verringern der Verluste im Vergleich zu herkömmlichen Strategien, die keine Vorhersagen verwenden. Um diese Funktionen umzusetzen, wurden drei Hierarchieebenen definiert. Die oberste enthält die Energiestrategie und bestimmt den langfristigen Ladestandverlauf des Kondensators mit Hilfe der Vorhersagen. Sie gibt der Leistungsstrategie in der mittleren Ebene einen Sollladestand und eine Zielzeit vor, zu der dieser Ladestand erreicht werden soll. Die Leistungsstrategie ist als modellprädiktive Regelung ausgeführt, die den Zielladegrad in einem Toleranzband führt und die Verluste des Energiespeichersystems minimiert. Die unterste Hierarchieebene enthält die Leistungsregelung des verwendeten DC/DC-Wandlers. Diese stellt die Kondensatorleistung nach der Vorgabe durch die Leistungsstrategie ein. Mit Hilfe dieses Ansatzes und einer Vorausschau von maximal 12 s konnten die Verluste im Vergleich zu einer regelbasierten Strategie ohne Vorausschau um 12 % verringert werden. Im Vergleich zu einer global optimierten Lösung, die mittels einer Dynamischen Programmierung erreicht wurde, erzeugt sie 8 % mehr Verluste. / This paper presents an operating strategy for a hybrid energy storage system using the prediction of future driving conditions by an autonomous vehicle. This allows to reduce the losses compared to conventional strategies that do not use predictions. To implement these functions, three hierarchy levels have been defined. The top level is the energy strategy and determines the long-term state of charge of the capacitor using the predictions. It gives the power strategy, the middle level, a target charge level and a target time at which this state of charge should be reached. The power strategy determines the current power distribution using a model predictive approach and stationary loss optimization. The lowest hierarchical level is the power control of the DC/DC converter used. This adjusts the capacitor power according to the specification of the power strategy. With the help of this approach and a forecast of maximum 12 s, the losses could be reduced by12 % compared to a rule-based strategy without a forecast. In comparison to a globally optimized solution achieved by dynamic programming, the new strategy generates 8 % more losses.

Le sujet de thèse aborde la quantification du flux de puissance parcourant les différents systèmes de conversion d'énergie statiques et dynamiques pour aboutir aux éléments de stockage de nature chimique / électrostatique / mécanique lors d'un freinage hybride récupératif brusque issu d’un véhicule électrique à traction avant. Le véhicule électrique est équipé de deux ensembles intégrés moteur-roues indépendants. Le côté commande des convertisseurs et des machines électriques sera aussi traité. La problématique concernera les cas de freinage régénératif brusque imposant des contraintes électriques et mécaniques élevées aux éléments de conversion d'énergie et de stockage. L'outil de simulation adopté est le logiciel Matlab/Simulink®. Un modèle assez fin du véhicule électrique utilisé sera développé afin de pouvoir simuler le comportement du véhicule conformément à la distribution des forces de freinage délivrée par le système de répartition et de quantification des forces de freinage. Une étude de la cinématique et de la dynamique du véhicule selon les différents états de route sera aussi examiné. Cette étude sera utilisée à posteriori dans la formulation des lois de distribution des forces de freinage. Les moteurs utilisés sont de type synchrones à aimants permanents intérieurs. L'objectif est d'assurer un couple électrique de freinage élevé à hautes vitesses de conduite du véhicule. A cette fin, la commande optimale de ces moteurs sera basée sur une nouvelle méthode de génération des courants de références assumant ainsi un couple régénératif élevé et donc une amélioration de l'énergie récupérée. Le système de stockage sera mixte et comportera une batterie Li-Ion et des cellules de supercondensateurs afin de réduire les contraintes sur la batterie et prolonger ainsi sa durée de vie. La structure de puissance de ce système sera analysée ainsi que le système de commande proposé du hacheur à 3 niveaux interfaçant l'ultracapacité avec le bus DC. Une résistance de freinage commandée par un régulateur pseudo-cascade sera aussi intégrée afin de réduire, si nécessaire, les contraintes sur la batterie. L'évaluation et la répartition des forces de freinage sur les quatre roues du véhicule en fonction de l'état de la route sont des éléments clés pour la stabilité du véhicule lors du freinage. La méthode de distribution et de quantification des forces de freinage proposée devra maintenir cette stabilité, répondre aux normes internationales et tirer profit de la présence des moteur-roues à l'avant du véhicule afin de maximiser l'énergie récupérée. Les travaux ont été étendus pour inclure une étude comparative avec un système de stockage contenant un élément de stockage à énergie cinétique comme source d'énergie secondaire pour un véhicule en opération de freinage et de traction. La thèse est le point de départ d'une collaboration de recherche entre l'IFSTTAR /Satie et le département de Génie Electrique du Cnam - Liban, centre associé au Conservatoire National des Arts et Métiers (Paris - France). / The thesis will address the quantification of power flow going through the different energy static and dynamic conversion systems to attain the chemical / electrostatic / mechanical storage elements during a hybrid regenerative brutal braking of a front-wheel driven electric vehicle. The electric vehicle is equipped by two integrated wheel-motors independent sets. The control of the converters and electrical machines is also treated. The problematic concerns the brutal regenerative braking case imposing high electrical and mechanical constraints on energy conversion and storage elements. The simulation tool adopted is Matlab/Simulink®. A detailed model of the used electric vehicle has been developed in order to be able to simulate the vehicle behavior with respect to the braking forces distribution delivered by the repartition and quantification of braking forces system. A study of the kinematics and dynamics of the vehicle according to different road types will be also considered. This study will be used retrospectively in the formulation of the braking forces distribution laws. The motors used are interior permanent magnet synchronous type. The objective is to ensure high electrical braking torque at high driving speeds of the vehicle. To this end, the optimal control of these motors will be based on a new current references generation method assuming then a high regenerative torque and therefore an improvement in the recovered energy. The hybrid storage system includes a Li-Ion battery and supercapacitors cells to reduce stress on the battery and to extend its life. The power structure of the system will be analyzed as well as the 3-level DC/DC converter interfacing the ultracapacitor with the DC bus proposed control system. A braking resistor controlled by a pseudo- cascaded controller will also be integrated to reduce, if necessary, the constraints on the battery. The evaluation and distribution of braking forces on the four wheels depending on road conditions are key elements for the stability of the vehicle during braking. The method of distribution and quantification of braking forces proposed should maintain this stability , meet international standards and take advantage of the presence of wheel motors in the front of the vehicle to maximize the energy recovered. The work has been extended to include a comparative study with a system containing a kinetic energy storage element as a secondary energy source for a braking and traction vehicle operation. The thesis is the starting point of a research collaboration between IFSTTAR / Satie and the Electrical Engineering Department of Cnam- Liban, associated center of the Conservatoire National des Arts et Métiers ( CNAM ), Paris, France.