Les batteries électriques sont omniprésentes dans notre vie quotidienne : ordinateur, téléphone, etc. Elles jouent un rôle important dans le défi de la transition énergétique : anticiper la raréfaction des énergies fossiles et réduire la pollution, en développant le stockage des énergies renouvelables et les transports électriques. Cependant, l'estimation de l'état de charge (State of Charge – SoC) d'une batterie est difficile et les modèles de prédiction actuels sont peu robustes. En effet, une batterie est un système électrochimique complexe, dont la dynamique est influencée non seulement par ses caractéristiques internes, mais aussi par les conditions d'usages souvent non contrôlables : température, profil d’utilisation, etc. Or, une estimation précise du SoC permet de garantir une utilisation sûre de la batterie en évitant une surcharge ou surdécharge ; mais aussi d’estimer son autonomie. Dans cette étude, nous utilisons un modèle à espaces d'états gouverné par une chaîne de Markov cachée. Ce modèle est fondé sur des équations physiques et la chaîne de Markov cachée permet d’appréhender les différents «régimes de fonctionnement» de la batterie. Pour garantir l’unicité des paramètres du modèle, nous démontrons son identifiabilité à partir de contraintes simples et naturelles sur ses paramètres «physiques ». L’estimation du SoC dans un véhicule électrique doit être faîte en ligne et avec une puissance de calcul limitée. Nous estimons donc le SoC en utilisant une technique d’échantillonnage préférentiel séquentiel. D’autre part l’estimation des paramètres est faîte à partir d’une base d’apprentissage pour laquelle les états de la chaîne de Markov et le SoC ne sont pas observés. Nous développons et testons trois algorithmes adaptés à notre modèle à structure latente : un échantillonneur particulaire de Gibbs, un algorithme de Monte-Carlo EM pénalisé par des contraintes d’identifiabilité et un algorithme de Monte-Carlo EM pénalisé par une loi a priori. Par ailleurs les états cachés de la chaîne de Markov visent à modéliser les différents régimes du fonctionnement de la batterie. Nous identifions leur nombre par divers critères de sélection de modèles. Enfin, à partir de données issues de trois types de batteries (cellule, module et pack d’un véhicule électrique), notre modèle a permis d’appréhender les différentes sollicitations de la batterie et donne des estimations robustes et précises du SoC. / Electric batteries are omnipresent in our daily lives: computers, smartphones, etc. Batteries are important for anticipating the scarcity of fossil fuels and tackling their environmental impact. Therefore, estimating the State of Charge (SoC) of a battery is nowadays a challenging issue, as existing physical and statistical models are not yet robust. Indeed a battery is a complex electrochemical system. Its dynamic depends not only on its internal characteristics but also on uncontrolled usage conditions: temperature, usage profile, etc. However the SoC estimation helps to prevent overcharge and deep discharge, and to estimate the battery autonomy. In this study, the battery dynamics are described by a set of physical linear equations, switching randomly according to a Markov chain. This model is referred to as switching Markov state space model. To ensure the unicity of the model parameters, we prove its identifiability by applying straightforward and natural constraints on its “physical” parameters. Embedded applications, like electric vehicles, impose online estimated with hardware and time constraints. Therefore we estimate the SoC using a sequential importance sampling technique. Furthermore the model includes two latent variables: the SoC and the Markov chain state. Thus, to estimate the parameters, we develop and test three algorithms adapted to latent structure models: particle Gibbs sampler, Monte Carlo EM penalized with identifiability constraints, and Monte Carlo EM penalized with a prior distribution. The hidden Markov states aim to model the different “regimes” of the battery dynamics. We identify their number using different model selection criteria. Finally, when applied to various data from three battery types (cell, module and pack of an electric vehicle) our model allows us to analyze the battery dynamics and to obtain a robust and accurate SoC estimation under uncontrolled usage conditions.

The electrical motors parameters identification is solved in this master’s thesis using subspace based methods. Electrical motors are simulated in Matlab/Simulink interactive environment, specifically permanent magnet DC motor and permanent magnet synchronous motor. Identification is developed in Matlab interactive environment. Different types of subspace algorithms are used for the estimation of parameters. Results of subspace parameters estimation are compared with least squares parameters estimation. The thesis describes subspace method, types of subspace algorithms, used electrical motors, nonlinear approach of identification and comparation of parameters identification.

The aim of this diploma thesis is to evaluate the potential of available methods for simplification and reduction of complex models of technical systems and their integration with experimental models. Finding methods, which would allow us to create models and run simulations in shorter periods of time, is key in design process of modern technical systems. In the beginning of this thesis, a theory necessary for understanding and application of presented methods is given. These methods can be separated into two groups, first as experiment related, second as simulation related. The first group contains methods for experimental evaluation of response and its use for dynamic system identification. The second group contains methods of finite element model creation, with the usage of standard structural elements as well as Component Mode Synthesis substructures, and these models are in the following step reduced into state space models. In the next step, all presented methods are applied on simple experimental structure. In conclusion, the results of simulations are the subject for comparison not only from quantitative point of view, but also, for the purpose of practical application, in terms of time, feasibility, versatility and accuracy. The system identification method along with state space method proved to be very suitable. The results presented in this thesis might help, by selecting the appropriate method, in simpler evaluation of dynamic properties of technical structures.

Cette thèse présente trois contributions indépendantes. La première partie se concentre sur la modélisation de la moyenne conditionnelle des rendements du marché actions : le rendement espéré du marché. Ce dernier est souvent modélisé à l'aide d'un processus AR(1). Cependant, des études montrent que lors de mauvaises périodes économiques la prédictibilité des rendements est plus élevée. Etant donné que le modèle AR(1) exclut par construction cette propriété, nous proposons d'utiliser un modèle CIR. Les implications sont étudiées dans le cadre d'un modèle espace-état bayésien. La deuxième partie est dédiée à la modélisation de la volatilité des actions et des volumes de transaction. La relation entre ces deux quantités a été justifiée par l'hypothèse de mélange de distribution (MDH). Cependant, cette dernière ne capture pas la persistance de la variance, à la différence des spécifications GARCH. Nous proposons un modèle à deux facteurs combinant les deux approches, afin de dissocier les variations de volatilité court terme et long terme. Le modèle révèle plusieurs régularités importantes sur la relation volume-volatilité. La troisième partie s'intéresse à l'analyse des stratégies d'investissement optimales sous contrainte «drawdown ». Le problème étudié est celui de la maximisation d'utilité à horizon fini pour différentes fonctions d'utilité. Nous calculons les stratégies optimales en résolvant numériquement l'équation de Hamilton-Jacobi-Bellman, qui caractérise le principe de programmation dynamique correspondant. En se basant sur un large panel d'expérimentations numériques, nous analysons les divergences des allocations optimales / This PhD thesis presents three independent contributions. The first part is concentrated on the modeling of the conditional mean of stock market returns: the expected market return. The latter is often modeled as an AR(1) process. However, empirical studies have found that during bad times return predictability is higher. Given that the AR(1) model excludes by construction this property, we propose to use instead a CIR model. The implications of this specification are studied within a flexible Bayesian state-space model. The second part is dedicated to the modeling of stocks volatility and trading volume. The empirical relationship between these two quantities has been justified by the Mixture of Distribution Hypothesis (MDH). However, this framework notably fails to capture the obvious persistence in stock variance, unlike GARCH specifications. We propose a two-factor model of volatility combining both approaches, in order to disentangle short-run from long-run volatility variations. The model reveals several important regularities on the volume-volatility relationship. The third part of the thesis is concerned with the analysis of optimal investment strategies under the drawdown constraint. The finite horizon expectation maximization problem is studied for different types of utility functions. We compute the optimal investments strategies, by solving numerically the Hamilton–Jacobi–Bellman equation, that characterizes the dynamic programming principle related to the stochastic control problem. Based on a large panel of numerical experiments, we analyze the divergences of optimal allocation programs

Die Arbeit befasst sich mit der effizienten Simulation des funktionellen Verhaltens von Piezoverbundmodulen als Aktor oder Sensor zur Schwingungsbeeinflussung mechanischer Strukturen. Ausgehend von einem FE-Modell werden über den Ansatz energetischer Äquivalenz die effektiven elektro-mechanischen Materialparameter ermittelt. Zur Berücksichtigung im Inneren der Einheitszelle liegender Elektroden werden die elektrischen Randbedingungen der Homogenisierungslastfälle angepasst. Die Homogenisierungslastfälle werden auch genutzt, um Phasenkonzentrationen für die Beanspruchungen der Verbundkomponenten zu ermitteln. Diese Phasenkonzentrationen werden eingesetzt, um aus dem effektiven Gesamtmodell die Beanspruchungen der Komponenten zu extrahieren. Zur dynamischen Modellbildung wird die Zustandsraumbeschreibung verwendet. Die Überführung einer piezo-mechanischen FE-Diskretisierung in ein Zustandsraummodell gelingt mit der Betrachtung der mechanischen Freiheitsgrade als Zustandsvariablen. Zur Abbildung der elektrischen Impedanz im Zustandsraum muss die elektrische Kapazitätsmatrix als Durchgangsmatrix einbezogen werden. Die Reduktion des Zustandsraums basiert auf der modalen Superposition. Die modale Transformationsbasis wird um Moden ergänzt, die die Verformung bei statischer elektrischer Erregung charakterisieren. Die Zustandsraumbeschreibung wird sowohl für eine Potential- als auch für eine Ladungserregung ausgeführt. Das Zustandsraummodell wird unter Verwendung von Filtermatrizen um Ausgangssignale für die mechanischen und elektrischen Beanspruchungsgrößen erweitert. Dies gestattet eine Kopplung der Zustandsraummodelle mit den Beanspruchungsanalysen. Die Anwendung der Berechnungsmethode wird am Beispiel der im SFB/TRR PT-PIESA entwickelten Piezo-Metall-Module demonstriert, die durch direkte Integration von piezokeramischen Basiselementen in Blechstrukturen gekennzeichnet sind.:1 Einleitung 2 Grundlagen 3 Stand der Forschung 4 Beanspruchungsermittlung für piezo-mechanische Verbunde 5 Zustandsraumbeschreibung piezo-mechanischer Systeme 6 Gesamtmodell 7 Zusammenfassung / This thesis deals with the efficient simulation of the functional behaviour of piezo composite modules for applications as actuators or sensors to influence vibrations of machine structures. Based on a FE-discretisation the effective electro-mechanical material parameters of the piezo composite modules are determined with an ansatz of energetic equivalence. To consider electrodes which are located inside the representative volume element the electrical boundary conditions of the load cases for homogenisation are adapted. The load cases for homogenisation are also used to determine the phase concentrations (or fluctuation fields) of stress/strain and electric field/electric displacement field in the composite constituents. These phase concentrations are required to extract stress and strain of the composite components based on the overall model with effective material parameters. For dynamical modelling a state space representation is used. The transformation of a FE-discretisation of the piezo-mechanical system into a state space model is possible by choosing the mechanical degree of freedom as state variables. For consideration of the electrical impedance in the state space model the electrical stiffness respectively capacitance matrix has to incorporate as feedthrough matrix. The dynamical model reduction of the state space model is based on modal superposition. For the correct reproduction of the electrical impedance the modal transformation basis has to be amended by deformation modes which represent the deformation behaviour due to static electrical excitation at the electrodes. The state space representation is built for potential and charge excitation. The state space model is enhanced by filter matrices to incorporate output signals for stress/strain and also for electric field/electric displacement field. This allows the coupling of the state space models with the stress analyses. The application of the simulation method is demonstrated using the example of the piezo-metal-modules developed in the CRC/TR PT-PIESA (German: SFB/TRR PT-PIESA). These piezo-metal-modules are characterised by direct integration of piezoceramic base elements in sheet metal structures.:1 Einleitung 2 Grundlagen 3 Stand der Forschung 4 Beanspruchungsermittlung für piezo-mechanische Verbunde 5 Zustandsraumbeschreibung piezo-mechanischer Systeme 6 Gesamtmodell 7 Zusammenfassung

In power electronics, the switched converter plays a fundamental role in the efficient conversion and dynamical control of electrical energy. Due to the switching operation of these systems, overlaid disturbances come into existence in addition to the desired behavior of the variables, causing deviations in the current and voltages. From a control perspective, these disturbances are of no interest since they cannot be compensated. They can even alter the measurements given to the control system, affecting its behavior. Furthermore, during the control design, averaged models are often used, by which the switching operation is somehow disregarded. They consider instead the average behavior of the system variables. Thus, it is essential that the measuring setup provides a measurement of the average value to the control system. To accomplish this goal, there are in practice different approaches. For example, the disturbances originated by the switching operation can be either suppressed using an analog or digital filter, or the sampling of the variables can be carried out in a suitable manner, synchronous to the carrier of the modulation method. Unfortunately, the use of filters adds an extra phase shift or delay to the control loop, reducing its dynamical performance. Moreover, the synchronous sampling method provides a good approximation of the average value only if certain conditions are met, otherwise a distortion due to aliasing takes place. A method is developed in this work to predict, in every switching cycle, the average value of the system variables in a switched power converter. In this context, the work presents an alternative method to carry out the measurement of the average value, avoiding the principal drawbacks of the standard measuring methods. To achieve this, a suitable model of the converter is used, incorporating the modulation method and the type of analog-to-digital converter, either a conventional sample-and-hold or a sigma-delta converter. The measurement given by the analog-to-digital converter is used to predict the time behavior of the system variables during the present switching period and then to evaluate its average value, before the period is completed. The method allows to obtain simultaneously the average value of currents and voltages, to get rid of the delay introduced by filtering, and to avoid the drawback of sampling in the measurement, i.e. aliasing. In this work, an overview of the standard measuring methods for switched power converters is first presented. The problematics that arise from the sampling process are also discussed. Next, the theoretical grounds of the method are developed and the tools needed to implement it are derived. To illustrate its applicability, the method is used first in DC-DC converters, where the case of the buck converter is analyzed in detail. Similarly, the method is applied to a three-phase two-level voltage source converter. In both cases, simulation results and experimental verification are presented for different operational modes. The usage of the method in open and closed loop is discussed, and its effect in the system behavior is shown. The performance of the prediction method is contrasted with other standard measuring methods.

Traffic light control (TLC) with transit signal priority (TSP) is an effective way to deal with urban congestion and travel delay. The growing amount of available connected vehicle data offers opportunities for signal control with transit priority, but the conventional control algorithms fall short in fully exploiting those datasets. This paper proposes a novel approach for dynamic TLC with TSP at an urban intersection. We propose a deep reinforcement learning based framework JenaRL to deal with the complex real-world intersections. The optimisation focuses on TSP while balancing the delay of all vehicles. A two-layer state space is defined to capture the real-time traffic information, i.e. vehicle position, type and incoming lane. The discrete action space includes the optimal phase and phase duration based on the real-time traffic situation. An intersection in the inner city of Jena is constructed in an open-source microscopic traffic simulator SUMO. A time-varying traffic demand of motorised individual traffic (MIT), the current TLC controller of the city, as well as the original timetables of the public transport (PT) are implemented in simulation to construct a realistic traffic environment. The results of the simulation with the proposed framework indicate a significant enhancement in the performance of traffic light controller by reducing the delay of all vehicles, and especially minimising the loss time of PT.

This thesis presents the design and implementation of control strategies for building energy management systems (BEMS). The controllers considered include the multi PI-loop controllers, state-space designs, constrained input and output MIMO adaptive controllers, fuzzy logic solutions and genetic algorithm techniques. The control performances of the designs developed using the various methods based on aspects such as regulation errors squared, energy consumptions and the settling periods are investigated for different designs. The aim of the control strategy is to regulate the room temperature and the humidity to required comfort levels. In this study the building system under study is a 3 input/ 2 output system subject to external disturbances/effects. The three inputs are heating, cooling and humidification, and the 2 outputs are room air temperature and relative humidity. The external disturbances consist of climatic effects and other stochastic influences. The study is carried out within a simulation environment using the mathematical model of the test room at Loughborough University and the designed control solutions are verified through experimental trials using the full-scale BMS facility at the University of Bradford.

In the thesis, a calibration method for positions of each accelerometer in an Inertial Sensor Array (IMU) sensor array is designed and implemented. In order to model the motion of the sensor array in the real world, we build up a state space model. Based on the model we use, the problem is to estimate the parameters within the state space model. In this thesis, this problem is solved using Maximum Likelihood (ML) framework and two methods are implemented and analyzed. One is based on Expectation Maximization (EM) and the other is to optimize the cost function directly using Gradient Descent (GD). In the EM algorithm, an ill-conditioned problem exists in the M step, which degrades the performance of the algorithm especially when the initial error is small, and the final Mean Square Error (MSE) curve will diverge in this case. The EM algorithm with enough data samples works well when the initial error is large. In the Gradient Descent method, a reformulation of the problem avoids the ill-conditioned problem. After the parameter estimation part, we analyze the MSE curve of these parameters through the Monte Carlo Simulation. The final MSE curves show that the Gradient Descent based method is more robust in handling the numerical issues of the parameter estimation. The Gradient Descent method is also robust to noise level based on the simulation result. / I denna rapport utvecklas och implementeras en kalibreringsmethod för att skatta positionen för en grupp av accelerometrar placerade i en så kallad IMU sensor array. För att beskriva rörelsen för hela sensorgruppen, härleds en dynamisk tillståndsmodell. Problemställningen är då att skatta parametrarna i tillståndsmodellen. Detta löses med hjälp av Maximum Likelihood-metoden (ML) där två stycken algoritmer implementeras och analyseras. En baseras på Expectation Maximization (EM) och i den andra optimeras kostnadsfunktionen direkt med gradientsökning. I EM-algoritmen uppstår ett illa konditionerat delproblem i M-steget, vilket försämrar algoritmens prestanda, speciellt när det initiala felet är litet. Den resulterande MSE-kurvan kommer att avvika i detta fall. Däremot fungerar EM-algoritmen väl när antalet datasampel är tillräckligt och det initiala felet är större. I gradientsökningsmetoden undviks konditioneringsproblemen med hjälp av en omformulering. Slutligen analyseras medelkvadratfelet (MSE) för parameterskattningarna med hjälp av Monte Carlo-simulering. De resulterande MSE-kurvorna visar att gradientsökningsmetoden är mer robust mot numeriska problem, speciellt när det initiala felet är litet. Simuleringarna visar även att gradientsökning är robust mot brus.

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