<p>An emerging technology in the field of transient thermal management is thermal energy storage, or TES, which enables temporary, on-demand heat rejection via storage as latent heat in a phase-change material.  Latent TES devices have enabled advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC systems and providing supplemental heat rejection in transient thermal management systems.  However, the design of a transient thermal management system with integrated storage comprises many challenges which are yet to be solved.  For example, design approaches and performance metrics for determining the optimal dimensions of the TES device have only recently been studied.  Another area of active research is estimation of the internal temperature state of the device, which can be difficult to directly measure given the transient nature of the thermal storage process.  Furthermore, in contrast to the three main functions of a thermal-fluid system--heat addition, thermal transport, and heat rejection--thermal storage introduces the need for active, real-time control and automated decision making for managing the operation of the thermal storage device. </p> <p>In this thesis, I present the design process for integrating thermal energy storage into a single-phase thermal management system for rejecting transient heat loads, including design of the TES device, state estimation and control algorithm design, and validation in both simulation and experimental environments. Leveraging a reduced-order finite volume simulation model of a plate-fin TES device, I develop a design approach which involves a transient simulation-based design optimization to determine the required geometric dimensions of the device to meet transient performance objectives while maximizing power density.  The optimized TES device is integrated into a single-phase thermal-fluid testbed for experimental testing.  Using the finite volume model and feedback from thermocouples embedded in the device, I design and experimentally validate a state estimator based on the state-dependent Riccati equation approach for determining the internal temperature distribution to a high degree of accuracy.  Real-time knowledge of the internal temperature state is critical for making control decisions; to manage the operation of the TES device in the context of a transient thermal management system, I design and test, both in simulation and experimentally, a logic-based control strategy that uses fluid temperature measurements and estimates of the TES state to make real-time control decisions to meet critical thermal management objectives. Together, these advances demonstrate the potential of thermal energy storage technology as a component of thermal management systems and the feasibility of logic-based control strategies for real-time control of thermal management objectives.</p>

<p>    </p> <p>This thesis studies networked Gaussian linear control systems with random delays. Networked control systems is a popular topic these years because of their versatile applications in daily life, such as smart grid and unmanned vehicles. With the development of these systems, researchers have explored this area in two directions. The first one is to derive the inherent rate-cost relationship in the systems, that is the minimal transmission rate needed to achieve an arbitrarily given stability requirement. The other one is to design achievability schemes, which aim at using as less as transmission rate to achieve an arbitrarily given stability requirement. In this thesis, we explore both directions. We assume the sensor-to-controller channels experience independently and identically distributed random delays of bounded support. Our work separates into two parts. In the first part, we consider networked systems with only one sensor. We focus on deriving a lower bound, R_{LB}(D), of the rate-cost tradeoff with the cost function to be E{| <strong>x^</strong>T<strong>x </strong>|} ≤ D, where <strong>x </strong>refers to the state to be controlled. We also propose an achievability scheme as an upper bound, R_{UB}(D), of the optimal rate-cost tradeoff. The scheme uses lattice quantization, entropy encoder, and certainty-equivalence controller. It achieves a good performance that roughly requires 2 bits per time slot more than R_{LB}(D) to achieve the same stability level. We also generalize the cost function to be of both the state and the control actions. For the joint state-and-control cost, we propose the minimal cost a system can achieve. The second part focuses on to the covariance-based fusion scheme design for systems with multiple > 1 sensors. We notice that in the multi-sensor scenario, the outdated arrivals at the controller, which many existing fusion schemes often discard, carry additional information. Therefore, we design an implementable fusion scheme (CQE) which is the MMSE estimator using both the freshest and outdated information at the controller. Our experiment demonstrates that CQE out-performances the MMSE estimator using the freshest information (LQE) exclusively by achieving a 15% smaller average L2 norm using the same transmission rate. As a benchmark, we also derive the minimal achievable L2 norm, Dmin, for the multi-sensor systems. The simulation shows that CQE approaches Dmin significantly better than LQE. </p>

[ES] Esta tesis de doctorado está dedicada al problema de descomposición de señales no estacionarias en componentes modales, entendida como componentes oscilatorias independientes, con amplitud y fase dependientes del tiempo. Para este fin, se propone un enfoque metodológico basado en representaciones en espacio de estados diagonales en bloques. Una contribución teórica primaria de esta tesis consiste en demostrar que la respuesta de un sistema de espacio de estados diagonal en bloques puede ser representada en una forma modal con amplitudes y frecuencias dependientes del tiempo. Subsecuentemente, construyendo sobre este resultado, un marco de trabajo basado en filtros de Kalman se propone para la descomposición modal de señales no estacionarias. Como resultado, una familia de métodos paramétricos para la descomposición modal de señales no estacionarias univariadas y multivariadas basadas en representaciones de espacio de estados diagonales en bloques y filtros de Kalman ha sido postulada. La representación básica está construida en bloques de segundo orden, cada uno de los cuales representa los componentes en fase y en cuadratura de un único componente oscilatorio no estacionario. Así, la respuesta total es construida como la suma ponderada de cada uno de estos modos. La identificación de estos modelos requiere la estimación conjunta de las trayectorias y los parámetros modales dependientes del tiempo, así como los hiperparámetros del modelo, constituidos por la matriz de mezcla de modos, las matrices de covarianza del vector de estados, de parámetros y del ruido de medición, y las condiciones iniciales. Para este propósito, un algoritmo de Expectación-Maximización ha sido adaptado como parte de esta tesis. La metodología obtenida es entonces evaluada en la descomposición y eliminación de ruido de registros electrocardiográficos (ECG), los cuales consisten en componentes no-estacionarias pseudo-periódicas y son susceptibles a diferentes tipos de interferencias. La estructura de estas señales las hace susceptibles a las descomposiciones modales basadas propuestas en esta tesis. A diferencia de otros métodos populares de descomposición de señales, las descomposiciones obtenidas con la metodología propuesta proveen componentes oscilatorios con interpretabilidad física y que proveen resultados consistentes para señales multivariadas, como en el caso de registros de ECG con múltiples derivaciones. Otra estrategia que se desarrolló en este proyecto investigativo lo constituye la aplicación de la transformada delta u operador de Euler al filtro de Kalman, esto condujo a resultados de alta precisión en la extracción de componentes de banda angosta. La metodología propuesta constituye una herramienta confiable para la descomposición modal en línea de señales no estacionarias multicomponentes, con resultados excelentes / [CA] Esta tesi de doctorat està dedicada al problema de descomposició de senyals no-estacionaris en components modals, entesa com a components oscil·latòries independents amb amplitud i fase dependents del temps. Per a este fi, es proposa un enfocament metodològic basat en representacions en espai d'estats diagonals en blocs. Una contribució teòrica primària d'esta tesi consistix a demostrar que la resposta d'un sistema d'espai d'estats diagonal en blocs pot ser representada en una forma modal amb amplituds i freqüències dependents del temps. Subseqüentment, construint sobre este resultat, un marc de treball basat en filtres de Kalman es proposa per a la descomposició modal de senyals no estacionaris. Com a resultat, una família de mètodes paramètrics per a la descomposició modal de senyals no estacionaris univariadas i multivariades basades en representacions d'espai d'estats diagonals en blocs i filtres de Kalman ha sigut postulada. La representació bàsica està construïda en blocs de segon ordre, cadascun dels quals representa els components en fase i en quadratura d'un únic component oscil·latori no estacionari. Així, la resposta total és construïda com la suma ponderada de cadascun d'estos modes. La identificació d'estos models requerix l'estimació conjunta de les trajectòries i els paràmetres modals dependents del temps, així com els hiperparámetros del model, constituïts per la matriu de mescla de modes, les matrius de covariància del vector d'estats, de paràmetres i del soroll de mesurament, i les condicions inicials. Per a este propòsit, un algorisme d'Expectació-Maximització ha sigut adaptat com a part d'esta tesi. La metodologia obtinguda és llavors avaluada en la descomposició i eliminació de soroll de registres electrocardiogràfics (ECG), els quals consistixen en components no-estacionàries pseudo-periòdiques i són susceptibles a diferents tipus d'interferències. L'estructura d'estos senyals les fa susceptibles a les descomposicions modals basades propostes en esta tesi. A diferència d'altres mètodes populars de descomposició de senyals, les descomposicions obtingudes amb la metodologia proposada proveïxen components oscil·latoris amb interpretabilidad física i que proveïxen resultats consistents per a senyals multivariats, com en el cas de registres d'ECG amb múltiples derivacions. Una altra estratègia que es va desenvolupar en este projecte investigativo el constituïx l'aplicació de la transformada delta o operador d'Euler al filtre de Kalman, això va conduir a resultats d'alta precisió en l'extracció de components de banda estreta. La metodologia proposada constituïx una eina de confiança per a la descomposició modal en línia de senyals no estacionaris multicomponents, amb resultats excel·lents. / [EN] This PhD thesis is devoted to the problem of the decomposition of non-stationary signals in modal components, understood as independent oscillatory components with time-dependent amplitude and frequency. To this end, a methodological approach based on diagonal time-dependent state space models is postulated. A primary theoretical contribution of this work is to demonstrate that the response of a system in diagonal time-dependent state space form can be cast in a modal form characterized by time-dependent amplitudes and frequencies. Subsequently, building up on this result, a Kalman filter based framework for non-stationary modal decomposition is proposed. As a result, a family of parametric modal decomposition methods is postulated for univariate and multivariate non-stationary signals based on block-diagonal time-dependent state space representations and Kalman filtering/smoothing. The representation is built upon second order blocks, each representing the in-phase and quadrature components of a single non-stationary oscillatory component. The total response is then constructed as the weighted sum of each of these modes. Accordingly, the model identification involves the joint estimation of the modal trajectories and the time-dependent modal parameters, along with the model hyperparameters, constituted by the mode mixing matrix, the state, parameter and noise covariances, and initial conditions. A tailored Expectation-Maximization algorithm is designed for this purpose as part of this thesis. The obtained methodology is assessed in the decomposition and denoising of electrocardiographic (ECG) signals, which consist of pseudo-periodic non-stationary signals and are susceptible to significant interference. The ECG signal structure makes them amenable to the proposed non-stationary modal decompositions. In contrast to other popular non-stationary signal decomposition methods, the proposed method provides a physically meaningful decomposition of oscillatory components, with consistent results for multivariate signals, such as multi-lead ECG records. Another strategy that was developed in this research project is the application of the delta transform or Euler operator to the Kalman filter, which led to highly precise results in extracting narrowband components. The proposed methodology constitutes a reliable tool for on-line modal decomposition of multi-component non-stationary signals, with results comparable and even better than other state-of-the-art methods. / Avendaño, LE. (2024). Método de descomposición modal no estacionaria basado en representación de espacio de estados con aplicación al análisis de señales ECG [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/211185

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.