Series-fed printed slot antenna arrays excited by microstrip lines are low profile, easy to manufacture, low cost structures that found use in applications that doesn&rsquo / t require high power levels with having advantage of easy integration with microwave front-end circuitry. In this thesis, design and analysis of microstrip line fed slot antenna arrays are investigated. First an equivalent circuit model that ignores mutual coupling effects between slots is studied. A 6-element array is designed by using this equivalent circuit model. From the measurement and electromagnetic simulation results of this array, it is concluded that mutual coupling effects should be considered in order to achieve a successful design that meets the design specifications related to the main beam direction and sidelobe levels of the antenna. Next, an improved equivalent circuit model proposed for stripline fed slot antenna arrays is studied. It is observed that, the mutual coupling effects are incorporated into the equivalent model through the utilization of active impedance concept. Finally, the design equations proposed in the improved equivalent circuit model are derived for the microstrip line fed slot antenna array structure. To demonstrate the validity and the accuracy of the derived design equations, results obtained by the proposed analysis method are compared with simulation and measurement results. It is concluded that the proposed method successfully predicts the radiation pattern of the array by including the mutual coupling effects.

Design and analysis of polysilicon and single crystal silicon gyroscopes have been carried out. Variations in suspension design have been explored. Designs that utilize in-plane and out-of-plane sensing are studied. Damping plays an important role in determining the sense response. Reduction in damping directly affects sensor performance. The various damping mechanisms that are prevalent in gyroscopes are studied. Perforations on the proof mass are observed to significantly reduce the damping in the device when operated in air. The effects of perforation geometry and density have been analyzed. The analysis results show that there is a two orders of magnitude reduction in damping of thick gyroscope structures with optimized perforation design. Equivalent circuit lumped parameter models have been developed to analyze gyroscope performance. The simulation results of these models have been compared with results obtained from SABER, a MEMS specific system level design tool from Coventor-ware. The lumped parameter models are observed to produce faster simulation results with an accuracy comparable to that of Coventorware Three gyroscopes specific to the PolyMUMPS fabrication process have been designed and their performance analyzed. Two of the designs sense motion out-of-plane and the other senses motion in-plane. Results of the simulation show that for a given damping, the gyro design with in-plane modes gives a resolution of 4◦/s. The out-of-plane gyroscopes have two variations in suspension. The hammock suspension resolves a rate of 25◦/s in a 200 Hz bandwidth while the design with folded beam suspension resolves a rate of 2◦/s in a 12 Hz bandwidth. A single crystal silicon in-plane gyroscope has been designed with vertical electrodes to sense Coriolis motion. This design gives an order of magnitude higher capacitance change for a given rotation in comparison to conventional comb-finger design. The effects of process induced residual stress on the characteristic frequencies of the polysilicon gyroscopes are also studied. The in-plane gyroscope is found to be robust to stress variations. Analysis results indicate that the tuning fork gyroscope with the hammock suspension is the most susceptible to compressive residual stress, with a significant drop in sensitivity at high stress values.

No description available.

<p>Due to increasing regulation on emissions and shifting consumer preferences, the wide adoption of battery electric vehicles (BEV) hinges on research and development of technologies that can extend system range. This can be accomplished either by increasing the battery size or via more efficient operation of the electrical and thermal systems. This thesis endeavours to accomplish the latter through comparative investigation of BEV integrated thermal management system (ITMS) performance across a range of ambient conditions (-20 °C to 40 °C), cabin setpoints (18 °C to 24 °C), and six different ITMS architectures. A dynamic ITMS modelling framework for a long-range electric vehicle is established with comprehensive sub models for the operation of the drive train, power electronics, battery, vapor compression cycle components, and cabin conditioning. This modelling framework is used to construct a baseline thermal management system, as well as for adaptation to four common systems. Additionally, a novel low-temperature waste heat recovery (LT WHR) system is proposed and shown to have potential benefits at low ambient temperatures through the reduction of the necessary cabin ventilation loading. While this system shows performance improvements, the regular WHR system offers the greatest benefit for long-range BEV drive cycles in terms of system range and transient response. With an optimal thermal management system found for long range BEV’s this system is then used as a boundary condition for a study on cooling of the battery. Battery conditioning, health, and as a result their along cell and system lifetime remains an additional concern of consumers as well as thermal systems engineers seeking to ensure safety and ensure longevity of EV battery cells. Three typical coolant flow orientations are studied to compare them under different flow conditions and thermal interface material performance. The battery cooling model is then coupled to the previously established dynamic modelling environment to demonstrate the added modelling capability (and necessity) for incorporating module-level cooling performance in both battery cooling studies and transient ITMS environments. </p>

This thesis investigates simulation of synchronous machines using a novel Magnetic Equivalent Circuit (MEC) model. The proposed model offers sufficient detail richness for design calculations, while still keeping the simulation time acceptably short. Different modeling methods and circuit alternatives are considered. The selected approach is a combination of several previous methods added with some new features. A detailed description of the new model is given. The flux derivative is chosen as the magnetic flow variable which enables a description with standard circuit elements. The model is implemented in dq-coordinates to reduce complexity and simulation time. A new method to reflect winding harmonics is introduced. Extensive measurements have been made to estimate the traditional dq-model parameters. These in combination with analytical calculations are used to determine the parameters for the new MEC model. The model is implemented using the Dymola simulation program. The results are evaluated by comparison with measurements and FEM simulations. Three different operation cases are investigated; synchronous operation, asynchronous start and inverter fed operation. The agreement with measurements and FEM simulations varies, but it is believed that it can be improved by more work on the parameter determination. The overall conclusion is that the MEC method is a useful approach for detailed simulation of synchronous machines. It enables proper modeling of magnetic saturation, and promises sufficiently detailed results to enable accurate loss calculations. However, the experience is that the complexity of the circuits should be kept at a reasonable low level. It is believed that the practical problems with model structure, parameter determination and the simulation itself will otherwise be difficult to master.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application.  In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters. 2) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts.  3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal control problem with charging speed and information content as objectives. To also take battery degradation into account, constraints on polarization was included. The results showed that parameter information can be increased without significant increase in charge time nor aging related stress. / Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynamiska parametervarierande modeller formulerades och identifierades från kördata. Den uppmätta temperaturen, samt strömmens storlekoch den estimerade tomgångsspänningen (OCV) användes till parameterberoenden. För att hantera skillnader i tidsskala mellan spänningssvarets olikakomponenter användes systemidentifiering i kontinuerlig tid. Vi kunde draslutsatsen att de föreslagna modellerna var överlägsna motsvarande diskretaoch konstanta modeller. 3) Istället för att använda kördata för att parametrisera modeller undersökte vi också möjligheten att designa laddförloppet för att öka dess informationsinnheåll. Detta formulerades som ett optimeringsproblem med laddtidoch informationsinnehåll i kostnadsfunktionen. För att även ta batteriets åldrande i beaktning, ansattses bivillkor på polariseringsspänningen. / <p>QC 20220516</p>

ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY (EIS) has been widely used to study the electrical properties of biological material due to its non-invasive nature and experimental reliability. However, most of the precision impedance analyzers used in EIS only provide single- or two-channel measurements which are inadequate for larger-scale multiplexed measurements, such as those found in modern microfluidic cell culture experiments. The Biomedical Microsystems Laboratory has developed a 16-channel cell culture platform with integrated electrode arrays for monitoring cell growth and electrical properties (i.e., the so-called “electrical phenotype”). In this paper, a system consisting of a 16-channel solid-state analog multiplexer (MUX)paired with a low-cost, impedance analyzer is developed to replace high-cost physical relay MUX and impedance analyzer systems. System requirements and design constraints for monitoring biological systems are considered and a prototype device was fabricated. Initial testing was performed on a breadboard to verify the feasibility of the design idea. Results identified measurement errors due to parasitic elements in the system. Software compensation successfully corrected for parasitic capacitance in the analog MUX design. The accuracy of the measurement system was evaluated on a developed Printed Circuit Board Assembly (PCBA) by comparing theoretical values to MUX compensated data. Finally, an EIS experiment was carried out with tap water with the PCBA system, and measurement results were analyzed using an equivalent Circuit Model (ECM). These results successfully captured the dynamics of charge transport in the electrical double layer, consistent with a modified-Randlecell ECM.

Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C. Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training. A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km. / Thesis / Master of Applied Science (MASc)

Dans le cadre du développement des véhicules électriques et hybrides, la connaissance et la gestion de l'énergie du pack de batteries est une problématique majeure. Pour cela, les constructeurs automobiles ont besoin de modèles numériques pour représenter le comportement dynamique des batteries. L'objectif de cette thèse est de développer, d'une part une méthodologie de caractérisation du comportement dynamique des cellules de batterie et de leurs assemblages et d'autre part des modèles numériques associés qui soient simples, rapides, robustes, présentant le meilleur compromis précision/simplicité. La première partie du travail de la thèse a consisté à développer une nouvelle méthode de caractérisation expérimentale avec un modèle de circuit électrique équivalent, qui permet de s'appliquer facilement à différentes batteries et de calibrer la complexité du modèle (nombre de circuits utilisés) en fonction de la durée des mesures de la phase de repos après une sollicitation. Le modèle généré est capable de suivre les évolutions rapides et lentes de la tension de la batterie, ce qui peut améliorer l'estimation de la tension dans les applications BMS (Battery Management System). Des essais de validations sur différentes batteries ont montré que les modèles générés permettent une prédiction précise du comportement dynamique de la batterie. Ensuite, le manuscrit aborde les assemblages des cellules en série avec la méthode de caractérisation élaborée. Elle commence par une définition énergétique de l'assemblage. Puis, la modélisation de l'assemblage avec la méthode de caractérisation est discutée. Les essais de validation ont été menés sur différents assemblages et ont montré que le comportement dynamique de l'assemblage peut aussi être bien représenté avec les modèles identifiés / As part of the development of electric and hybrid vehicles, energy management in the battery pack is a major issue. Car manufacturers need a numerical model to represent the dynamic behavior of batteries. The objective of this work is to develop, on the one hand, a characterization method of the dynamic behavior of battery cells and their assemblies, and on the other hand the combined numerical models which are simple, fast, robust and with the best accuracy/simplicity compromise. The first part of the work is dedicated to develop a new experimental characterization method with an equivalent circuit model, which can be applied easily to different battery cells and allows calibrating the complexity of the model (number of the RC circuits) according to the measurement duration of the resting phase after a solicitation. Therefore, the generated model is able to follow the rapid and slow voltage change of the battery cell, which improves voltage and state of charge estimation for the BMS (Battery Management System) applications. The validation tests on different battery cells show that the generated model allows accurate prediction of the battery cell’s dynamic behavior. The second part of the work studies the cell assemblies with cells connected in series. It begins with an energy definition of the cell assembly. Then modelling of the assembly with the developed characterization method is discussed. The validation tests were carried out on different assemblies and show that the dynamic behavior of the assembly can be also well represented with the identified models

Práce se zabývá studiem superkondenzátorů (SC). Výstupem je detailní studie principů přenosu náboje ve struktuře SC, ukládání energie a nový náhradní model SC, který je založen na fyzikálních zákonitostech a principech SC. Dále byl vytvořen matematický model SC, který popisuje chování náboje v jeho aktivní vrstvě. SC byly testovány metodami umělého stárnutí. Závislosti poklesu parametrů SC vlivem různých metodik stárnutí jsou v práci shrnuty.