Målet med projektet var att undersöka samt implementera en fysikbaserad DFN modell för att simulera kalender samt cyklingåldrande av litiumbatterier som används i elbilar. Den fysikbaserade modellen var konstruerad baserad på ett Python biblioteket vid namn PyBaMM, vilket till skillnad från datadrivna modeller ger essentiell information om de kemiska processerna inuti batteriet. Den första delen av projektet täcker konceptet av kalenderåldring, vilket inkluderar en jämförelse mellan tre olika tre olika hastighetsbegränsande SEI modeller. Parametrar som påverkar det erhållna resultatet från modellen är identifierade, estimerade, och till slut validerade för att säkerhetsställa att modellen och parametrarna är identifierbara gentemot experimentella data. Resultatet av jämförelsen gav att SEI tillväxt begränsad av litium interstitiell diffusion är den mest optimala modellen att applicera när kalenderåldring för litiumbatterier ska modelleras. Resultaten visade också att endast en parameter, inre SEI litium interstitiell diffusivitet ska justeras för att erhålla optimal anpassning mot experimentella data. Andra delen av projektet använde resultatet från den första delen och litium plätering implementerades som en andraåldringsmekanism som undersöktes under tre olika laddningsprotokoll. Modellen var optimerad och anpassad gentemot experimentella data, där parametervärdet för kinetisk hasighetskonstanten för plätering var estimerad. Den optimerade modellen användes därefter för att erhålla mer information om elektrokemiska variabler för att kunna analysera samt beskriva åldringsprocessen utan att behöva genomföra praktiska laborationer. Resultaten visade att mängden pläterat litium på den negativa elektroden ökade för celler som var exponerade till högre ström under laddningsprocessen, samt när cellerna var laddade vid höga SoC nivåer. Sammanfattningsvis, visade modellen hög potential att representera och evaluera experimentella data, samt tillhandahålla en inblick i elektrokemiska processer och kapacitetsförluster länkade till SEI tillväxt och litium plätering. Däremot, för att erhålla en högre grad noggrannhet av elektrokemiska åldringsmekanismer i litiumbatterier, fler ytterligare mekanismer måste implementeras såsom mekanisk stress av både negativ och positiv elektrod. / The aim of this study was to investigate and apply a physics-based DFN model to simulate the calendar and cycling ageing of lithium-ion batteries manufactured for EV applications. The physics-based cell ageing model was constructed based on the open-source software Python library PyBaMM, which in comparison to data-driven models provides more essential information about the chemical process within the battery cell. The first part of the project covers the concept of calendar ageing which includes comparisons between three different rate-limiting SEI growth models. Parameters that affect the output from the physics-based model are isolated, estimated with numerical methods, and lastly validated to ensure that the model and the parameters rep- resent the physics behind the experimental data. It was found that the SEI growth limited by lithium interstitial diffusion is the most optimal model to apply for a physics-based model when modeling calendar ageing. It was also found that the only parameter that should be tuned against experimental data is the inner SEI lithium interstitial diffusivity. The second part of the project utilizes the results from the first part and introduces lithium plating as a second cell ageing mechanism under three different charging protocols. The model was optimized and fitted against experimental data by sweeping the lithium plating kinetic rate constant parameter. The optimized model was thereafter used to generate outputs that more thoroughly can explain the degradation effects of the cell without constructing real-world experiments. Where increased rate of plated lithium could be observed for the cell subjected to higher charging C-rate, and when the cells were charged at high SoC levels. To summarize, the model showed great potential in representing and evaluating the experimental data and providing the project with insight into the electrochemical processes and cell capacity losses of SEI growth and lithium plating. However, in order to achieve a higher accuracy of cell ageing model in relation to the lithium-ion cells used in customer vehicles, several additional cell degradation mechanisms have to be introduced, such as mechanical degradation of the two electrodes.

Lithium-ion (Li-Ion) batteries are widely used in various applications and are viable for automotive applications. The effective management of Li-Ion batteries in battery electric vehicles (BEV) plays a crucial role in performance and range. One can achieve good performance and range by using efficient battery models in battery management systems (BMS). Hence, these battery models play an essential part in the development process of battery electric vehicles. Physics-based battery models are used for design purposes, control, or to predict battery behaviour, and these require much information about materials and reaction and mass transport properties. Model parameterization, i.e., obtaining model parameters from different experimental sets (by fitting the model to experimental data sets), can be challenging depending on model complexity and the type and quality of experimental data. Based on the idea of parameter sensitivity, certain current/voltage data sets could be chosen that theoretically has a more considerable sensitivity for a given model parameter that is of interest to extract. In this thesis work, different methods for extracting model parameters for a Nickel-Manganese-Cobalt (NMC) battery composite electrode are experimentally tested and compared. Specifically, model parameterization using \emph{optimal experiments} based on performed parameter sensitivity analysis has been benchmarked against a 1C discharge test and low rate pulse tests. The different parameter sets obtained have then been validated on a drive cycle and 2C pulse tests. Comparing the methods show some promising results for the optimal experiment design (OED) method, but consideration regarding the state of charge (SOC) dependencies, the number of parameters has to be further evaluated. / Litiumjonbatterier (Li-jon) används i olika applikationer och är ett bra alternativ förfordonsapplikationer. Den effektiva hanteringen av litiumjonbatterier i elbilar har en viktigroll för fordonens prestanda och räckvidd. Man kan nå bra prestanda och räckviddgenom att använda bra batterimodeller i batteriets övervakningssystem (BMS). Därförspelar dessa batterimodeller en viktig roll i utvecklingen av elbilar. Fysikbaseradebatterimodeller används för design, reglering eller för att prediktera beteendet hos batteriet,vilket kräver mycket information om material samt dess reaktion och andra beskaffenheter.Modellparametrisering, dvs. att införskaffa modellparametrar från olika experiment (genom attanpassa modell till experimentella data) kan vara utmanande beroende på modellkomplexitetoch typen samt kvalitén på experimentell data. Baserat på idén om parametersensitivitet kan data om ström och spänning väljas så att de teoretiskt har mer sensitivitet för engiven modellparameter som är av intresse att extrahera. I detta examensarbete testas ochjämförs olika metoder för att extrahera modellparametrar för en Nickelmangankobolt (NMC)batterielektrod. Mer specifikt, modellparametrisering genom optimala experiment baseradepå genomförd parametersesitivitetsanalys jämförts med 1C urladdningstest och låg nivåpulstest. Jämförande av metoderna visar goda resultat för OED metoden men flera parametrarmåste fortsatt utvärderas gällande laddningstatusberoenden (SOC).

Na última década diversos algoritmos baseados em busca foram desenvolvidos para a geração de níveis em diferentes tipos de jogos. O espaço de busca para geração de níveis geralmente possui restrições, uma vez que a mecânica de um jogo define regras de factibilidade para os níveis. Em alguns métodos, a avaliação de factibilidade requer uma simulação com um agente inteligente que controla o jogo. Esse processo de avaliação geralmente possui ruído, causado por componentes aleatórios no simulador ou na estratégia do agente. Diversos trabalhos têm utilizado simulação como forma de avaliação de conteúdo, no entanto, nenhum deles discutiu profundamente a presença de ruído neste tipo de abordagem. Assim, esse trabalho apresenta um algoritmo genético capaz de gerar níveis factíveis que são avaliados por um agente inteligente em uma simulação ruidosa. O algoritmo foi aplicado a jogos de quebra-cabeças baseados em física com a mecânica do Angry Birds. Uma representação dos níveis em forma de indivíduos é introduzida, a qual permite que o algoritmo genético os evolua com características diferenciadas. O ruído na função de aptidão é tratado por uma nova abordagem, baseada em uma sistema de cache, que auxilia o algoritmo genético a encontrar boas soluções candidatas. Três conjuntos de experimentos foram realizados para avaliar o algoritmo. O primeiro compara o método de cache proposto com outros métodos de redução de ruído da literatura. O segundo mede a expressividade do algoritmo genético considerando as características estruturais dos níveis gerados. O último avalia os níveis gerados considerando aspectos de design (como dificuldade, imersão e diversão), os quais são medidos por meio de questionários respondidos por jogadores humanos via Internet. Os resultados mostraram que o algoritmo genético foi capaz de gerar níveis distintos que são tão imersíveis quanto níveis produzidos manualmente. Além disso, a abordagem de cache lidou apropriadamente com o ruído nos cálculos de aptidão, permitindo uma correta evolução elitista. / In the last decade several search-based algorithms have been developed for generating levels in different types of games. The search space for level generation is typically constrained once the game mechanics define feasibility rules for the levels. In some methods, evaluating level feasibility requires a simulation with an intelligent agent which plays the game. This evaluation process usually has noise, caused by random components in the simulator or in the agent strategy. Several works have used a simulation for content evaluation, however, none of them have deeply discussed the presence of noise in this kind of approach. Thus, this paper presents a genetic algorithm capable of generating feasible levels that are evaluated by an intelligent agent in a noisy simulation. The algorithm was applied to physics-based puzzle games with the Angry Birds mechanics. A level representation in the form of individuals is introduced, which allows the genetic algorithm to evolve them with distinct characteristics. The fitness function noise is handled by a new approach, based on a cache system, which helps the genetic algorithm finding good candidate solutions. Three sets of experiments were conducted to evaluate the algorithm. The first one compares the proposed cache approach with other noise reduction methods of the literature. The second one measures the expressivity of the genetic algorithm considering the structural characteristics of the levels. The last one evaluates design aspects (such as difficulty, immersion and fun) of the generated levels using questionnaires answered by human players via Internet. Results showed the genetic algorithm was capable of generating distinct levels that are as immersive as levels manually designed. Moreover, the cache approach handled properly the noise in the fitness calculations, allowing a correct elitist evolution.

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.

Na última década diversos algoritmos baseados em busca foram desenvolvidos para a geração de níveis em diferentes tipos de jogos. O espaço de busca para geração de níveis geralmente possui restrições, uma vez que a mecânica de um jogo define regras de factibilidade para os níveis. Em alguns métodos, a avaliação de factibilidade requer uma simulação com um agente inteligente que controla o jogo. Esse processo de avaliação geralmente possui ruído, causado por componentes aleatórios no simulador ou na estratégia do agente. Diversos trabalhos têm utilizado simulação como forma de avaliação de conteúdo, no entanto, nenhum deles discutiu profundamente a presença de ruído neste tipo de abordagem. Assim, esse trabalho apresenta um algoritmo genético capaz de gerar níveis factíveis que são avaliados por um agente inteligente em uma simulação ruidosa. O algoritmo foi aplicado a jogos de quebra-cabeças baseados em física com a mecânica do Angry Birds. Uma representação dos níveis em forma de indivíduos é introduzida, a qual permite que o algoritmo genético os evolua com características diferenciadas. O ruído na função de aptidão é tratado por uma nova abordagem, baseada em uma sistema de cache, que auxilia o algoritmo genético a encontrar boas soluções candidatas. Três conjuntos de experimentos foram realizados para avaliar o algoritmo. O primeiro compara o método de cache proposto com outros métodos de redução de ruído da literatura. O segundo mede a expressividade do algoritmo genético considerando as características estruturais dos níveis gerados. O último avalia os níveis gerados considerando aspectos de design (como dificuldade, imersão e diversão), os quais são medidos por meio de questionários respondidos por jogadores humanos via Internet. Os resultados mostraram que o algoritmo genético foi capaz de gerar níveis distintos que são tão imersíveis quanto níveis produzidos manualmente. Além disso, a abordagem de cache lidou apropriadamente com o ruído nos cálculos de aptidão, permitindo uma correta evolução elitista. / In the last decade several search-based algorithms have been developed for generating levels in different types of games. The search space for level generation is typically constrained once the game mechanics define feasibility rules for the levels. In some methods, evaluating level feasibility requires a simulation with an intelligent agent which plays the game. This evaluation process usually has noise, caused by random components in the simulator or in the agent strategy. Several works have used a simulation for content evaluation, however, none of them have deeply discussed the presence of noise in this kind of approach. Thus, this paper presents a genetic algorithm capable of generating feasible levels that are evaluated by an intelligent agent in a noisy simulation. The algorithm was applied to physics-based puzzle games with the Angry Birds mechanics. A level representation in the form of individuals is introduced, which allows the genetic algorithm to evolve them with distinct characteristics. The fitness function noise is handled by a new approach, based on a cache system, which helps the genetic algorithm finding good candidate solutions. Three sets of experiments were conducted to evaluate the algorithm. The first one compares the proposed cache approach with other noise reduction methods of the literature. The second one measures the expressivity of the genetic algorithm considering the structural characteristics of the levels. The last one evaluates design aspects (such as difficulty, immersion and fun) of the generated levels using questionnaires answered by human players via Internet. Results showed the genetic algorithm was capable of generating distinct levels that are as immersive as levels manually designed. Moreover, the cache approach handled properly the noise in the fitness calculations, allowing a correct elitist evolution.

Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing advanced and immersive experience in the entertainment industry with countless media forms. Theses systems suffer a lack of correct direct and indirect illumination modeling where the virtual objects render with the same lighting condition as the real environment. Some systems are using baked GI, pre-recorded textures, and light probes that are mostly accomplished offline to compensate for precomputed real-time global illumination (GI). Thus, illumination information can be extracted from the physical scene for interactively rendering the virtual objects into the real world which produces a more realistic final scene in real-time. This work approaches the problem of visual coherence in AR by proposing a system that detects the real-world lighting conditions in dynamic scenes, then uses the extracted illumination information to render the objects added to the scene. The system covers several major components to achieve a more realistic augmented reality outcome. First, the detection of the incident light (direct illumination) from the physical scene with the use of computer vision techniques based on the topological structural analysis of 2D images using a live-feed 360^o camera instrumented on an AR device that captures the entire radiance map. Also, the physics-based light polarization eliminates or reduces false-positive lights such as white surfaces, reflections, or glare which negatively affect the light detection process. Second, the simulation of the reflected light (indirect illumination) that bounce between the real-world surfaces to be rendered into the virtual objects and reflect their existence in the virtual world. Third, defining the shading characteristic/properties of the virtual object to depict the correct lighting assets with a suitable shadow casting. Fourth, the geometric properties of real-scene including plane detection, 3D surface reconstruction, and simple meshing are incorporated with the virtual scene for more realistic depth interactions between the real and virtual objects. These components are developed methods which assumed to be working simultaneously in real-time for photo-realistic AR. The system is tested with several lighting conditions to evaluate the accuracy of the results based on the error incurred between the real/virtual objects casting shadow and interactions. For system efficiency, the rendering time is compared with previous works and research. Further evaluation of human perception is conducted through a user study. The overall performance of the system is investigated to reduce the cost to a minimum.

No description available.

No description available.

<p dir="ltr">Bridge safety has been a longstanding priority for civil engineers. Engineers and researchers devote significant effort toward establishing the ability to detect and monitor damage and to manage the performance of our bridges over their lifecycle. However, current practices, heavily reliant on manual involvement, still present challenges in efficiency and effectiveness. For instance, rapid bridge vulnerability assessment methods are being developed, but these methods normally require information about the bridge, e.g., substructure type, which is not readily available in most current bridge databases (e.g., National Bridge Inventory). Manually collecting the necessary information for each bridge is time-consuming and would influence the use of those rapid bridge vulnerability assessment methods. Similarly, routine bridge inspection, mandated every two years in the United States, requires organizing inspection photos, evaluating bridge condition, etc., which are also time-consuming tasks. A two-year inspection cycle may be overly cautious, especially in the early stages of its life when there is typically little degradation. Furthermore, when conducting an inspection there is no reference for when to use advanced inspection techniques, so visual inspection, the simplest method, is adopted for most bridges. Given these challenges in bridge asset management, the integration of machine learning to use the data from historical records of bridge performance can aid in and serve to expedite the above tasks.</p><p dir="ltr">The objective of this research is to develop machine learning-based methods that can assist humans in completing certain tasks associated with bridge asset management. Towards this objective, the following research tasks are carried out. In Task 1, a CNN-based bridge substructure identifier is developed to automatically recognize the bridge substructure type from an inspection image. A method is developed to set a rational budget for this work based on risk tolerance. In Task 2, the automated bridge inspection image organization tool (ABIRT) is developed to automate the process of organizing inspection images and generating an inspection report. In Task 3, a technique for machine-assisted bridge damage analysis using visual data is developed and validated. Additionally, a decision-making method is established to assist bridge inspectors in adopting this technique, with a focus on managing costs and minimizing risks. And in Task 4, reinforcement learning-based approach is developed to manage the bridge inspection process. Through this research, several key machine learning techniques are explored to assist bridge managers with the more tedious steps involved in the seismic vulnerability analysis and condition management tasks, allowing the engineer to dedicate more time to making decisions. This highly cross-disciplinary research is scalable and expandable to many other applications and will serve to improve the future safety and reliability of our infrastructure.</p>