The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.

Les estimations précises des états de charge (« State of Charge » - SoC) et de santé (« State of Health » - SoH) des batteries au lithium sont un point crucial lors d’une utilisation industrielle de celles-ci. Ces estimations permettent d’améliorer la fiabilité et la robustesse des équipements embarquant ces batteries. Cette thèse CIFRE est consacrée à la recherche d’algorithmes de détermination de l’état de santé de batteries lithium-ion, en particulier de chimie Lithium Fer Phosphate (LFP) et Lithium Manganèse Oxyde (LMO).Les recherches ont été orientées vers des solutions de détermination du SoH directement embarquables dans les calculateurs des véhicules électriques. Des contraintes fortes de coût et de robustesse constituent ainsi le fil directeur des travaux.Or, si la littérature actuelle propose différentes solutions de détermination du SoH, celles embarquées ou embarquables sont encore peu étudiées. Cette thèse présente donc une importante revue bibliographique des différentes méthodes d’estimation du SoH existantes, qu’elles soient embarquables ou non. Le fonctionnement détaillé ainsi que les mécanismes de vieillissement d’une batterie lithium-ion sont également explicités.Une partie majoritaire des travaux est consacrée à l’utilisation de l’analyse incrémentale de la capacité (« Incremental Capacity Analysis » - ICA) en conditions réelles, c’est-à-dire avec les niveaux de courant présents lors d’un profil de mission classique d’un véhicule électrique, avec les mesures disponibles sur un BMS (« Battery Management System ») industriel et avec les contraintes de robustesses associées, notamment une gamme étendue de température de fonctionnement. L’utilisation de l’ICA pour déterminer la capacité résiduelle de la batterie est mise en œuvre de façon totalement innovante et permet d’obtenir une grande robustesse aux variations des conditions d’utilisation de la batterie.Une seconde méthode est, elle, dédiée à la chimie LMO et exploite le fait que le potentiel aux bornes de la batterie soit représentatif de son état de charge. Un compteur coulométrique partiel est ainsi proposé, intégrant une gestion dynamique des bornes d’intégration en fonction de l’état de la batterie.A l’issue des travaux, une méthode complète et précise de détermination du SoH est disponible pour chacune des chimies LFP et LMO. La détermination de la capacité résiduelle de ces deux familles de batteries est ainsi possible à 4 % près. / Accurate lithium-ion battery State of Charge (SoC) and State of Health (SoH) estimations are nowadays a crucial point, especially when considering an industrial use. These estimations enable to improve robustness and reliability of hardware using such batteries. This thesis focuses on researching lithium-ion batteries state of health estimators, in particular considering Lithium Iron Phosphate (LFP) and Lithium Manganese Oxide (LMO) chemistries.Researches have been targeted towards SoH estimators straight embeddable into electric vehicles (EV) computers. Cost and reliability constraints are thus the main guideline for this work.Although existing literature offers various SoH estimators, those who are embedded or embeddable are still little studied. A complete literature review about SoH estimators, embedded or not, is therefore proposed. Lithium-ion batteries detailed operation and ageing mechanisms are also presented.The main part of this work is dedicated to Incremental Capacity Analysis (ICA) use with electric vehicle constraints, such as current levels available with a typical EV mission profile or existing measurements on the Battery Management System (BMS). Incremental Capacity Analysis is implemented in an innovative way and leads to a remaining capacity estimator with a high robustness to conditions of use variations, including an extended temperature range.A second method, dedicated to LMO chemistry, take advantage of the fact that the battery potential is representative of its state of charge. Partial Coulomb counting is thus performed, with a dynamic management of integration limits, depending on the battery state.Outcomes of this work are two complete and accurate SoH estimators, one for each chemistry, leading to a remaining capacity estimation accurate within 4 %.

Capacity analysis is a common preliminary step in the design of offshore foundations. Inaccuracies in traditional capacity analysis methods, and the advancement of numerical modelling capabilities, have increasingly led designers to optimise foundations using more complex methods. In this thesis, the ultimate limit state capacity of a range of foundation types is investigated using finite element limit analysis. Novel three-dimensional finite element limit analysis software is benchmarked against analytical solutions and conventional displacement finite element analysis. It is then used to find lower and upper bounds of foundation capacity, with adaptive mesh refinement used to reduce the bound gap over successive iterations of the solution. Rigid foundations subjected to short term loading on clay soil are analysed. The undrained soil is modelled as a rigid--plastic von Mises material, and attention is given to modelling any normal and/or shear stress limits at the foundation/soil interface. Shallow foundations, suction anchor foundations, and hybrid mudmat/pile foundations are considered. Realistic six degree-of-freedom load combinations are applied and results are reported in the form of normalised design charts, and tables, that are suitable for use in preliminary design. Relationships between loading combinations and failure mechanisms are also explored. A number of case studies based on authentic foundation designs are analysed. The results suggest that finite element limit analysis could provide an attractive alternative to displacement finite element analysis for preliminary foundation design calculations.

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Åre ski resort is the largest and most renowned ski resort in Sweden, offering excellent skiing opportunities, restaurants, and nightlife in a prime location. Meanwhile, it is often subject to heavy traffic during the peak season and has earned a bad reputation for struggling with long lift queues. To address the issue, this paper aimed at analyzing the current capacity of the ski resort with the purpose of identifying areas and cost-efficient measures for improvement. It was done by modeling the ski system as a Jackson Network based on queuing theory, with relevant parameters extracted from actual skier data provided by the operating company Skistar. Several models were constructed to capture varying skiing patterns throughout the day and under different weather conditions. The models suggested that the lift queues first start to form at the lifts VM 8:an, Sadelexpressen, and Bräckeliften when the number of skiers in the system ranges from 3,700 to 6,200. Recommendations were then proposed to Skistar on how to resolve the identified bottlenecks and increase the resort’s capacity to a range of 6,000 to 8,400 skiers. Lastly, the models estimated that the resort could / Åre är en av Sveriges mest populära skidorter, som med utmärkta kommunikationsmöjligheter erbjuder fantastisk skidåkning och ett brett utbud av restauranger och nöjesliv. Skidsystemet belastas samtidigt hårt under högsäsong och har ådragit sig ett rykte om att ofta vara drabbat av långa liftköer. Mot bakgrund av detta ämnade denna rapport att analysera skidsystemets kapacitet och identifiera förbättringsområden för att ta itu med problemet. Analysen grundade sig i en modellering av skidsystemet, utformat som ett Jacksonnätverk i enlighet med köteori. Skistar, som ansvarar för driften av skidsystemet, tillhandahöll data som låg till grund för framtagandet av nätverkets parametrar. Genom jämförelser mellan olika modeller kunde dagliga förändringar i åkmönster och variationer under olika väderförhållanden tas i beaktning i analysen. Modellerna indikerade att liftarna VM 8:an, Sadelexpressen och Bräckeliften först drabbas av köbildning, vilket sker då antalet skidåkare i systemet är mellan 3 700 och 6 200. De identifierade flaskhalsarna låg till grund för de rekommendationer som formulerades till Skistar och som innehöll kostnadseffektiva förslag på förändringar. Dessa skulle förbättra kapaciteten till en nivå mellan 6 000 och 8 400 skidåkare. Till sist estimerade modellerna systemets maximala kapacitet till 14 000 skidåkare, vilket endast uppnås vid ett optimalt nyttjande av samtliga liftar.

<p>In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. </p><p>The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins.</p><p>In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles. </p>

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.

The subject of this diploma thesis is variant design of adaptation of T- intersection to crossroad at Frýdek-Místek. At first, there was made traffic forecast of generated traffic from regional planning. After that, there were made traffic forecast from relocation of road II/477. Then for every traffic forecast there were made two more variants of designs. The first variant is designed by one lane roundabout and signal-controlled intersection with signal timing plan. The second and the third variant are designed by turbo roundabout and signal-controlled intersection with signal timing plan. All variants have their own capacity analysis.