Integrating more renewable energy sources into the grid has caused increased instability due to the intermittency of renewable energy sources. Hence, the need for grid balancing strategies such as frequency regulation has intensified. Areim, a Nordic real estate investment company, through this thesis, aims to have an assessment conducted to estimate the environmental benefits or consequences of using their specific battery system as a participant in the Swedish frequency regulation market, using the lifecycle assessment framework. The study only considered the cradle-to-gate lifecycle scope, excluding the product disposal stage, and the impact categories used align with the Environmental Footprint assessment methodology. The functional unit is in per kilo-watthour delivered, and the batteries are expected to deliver 933 kWh of electric energy over the estimated lifetime of 15 years. The normalized carbon emissions caused by delivering 1 kWh of energy for frequency regulation using the status quo prequalified technologies primarily comprised of hydropower, combined heat and power, and battery energy storage produce 4.75 kgCO2eq. Introducing Areim's specific battery system 200 kW bid into the prequalified technologies mix by substitution produces 0.075 kgCO2eq fewer carbon emissions per kWh delivered. The sensitivity analysis further supports that Areim will yield added carbon emission savings by increasing its available prequalified re-source capacity in the market. The findings of this thesis can be used to support Areim and other companies interested in grid support services such as frequency regulation to decide whether it is beneficial to use their specific battery systems for such services from an environmental effect perspective.

Indiana University-Purdue University Indianapolis (IUPUI) / Computational analysis of lithium ion batteries has been improved since Newman and et al. suggested the porous electrode theory. It assumed the electrode as a simple structure of homogeneous spherical particles. Bruggeman relationship which characterizes porous material by a simple equation was adopted in the homogeneous electrode model instead of the electrode morphology. To improve the prediction of a cell performance, the numerical analysis requires the realistic microstructure of the cell. Based on the experimentally determined microstructure of the positive and negative electrodes of a lithium ion battery (LIB) using x-ray micro/nano-CT technology, three dimensional (3D) simulations have been presented in this research. Tortuosity of the microstructures has been calculated by a linear diffusion equation to characterize the 3D morphology. The obtained tortuosity and porosity results pointed out that the Bruggeman relationship is not sufficiently estimate the tortuosity by the porosity of electrodes. We studied the diffusion-induced stress numerically based on realistic morphology of reconstructed particles during the lithium ion intercalation process. Diffusion-induced stresses were simulated at different C rates under galvonostatic conditions and compared with spherical particles. The simulation results showed that the intercalation stresses of particles depend on their geometric characteristics. The highest von Mises stress and tresca stress in a real particle are several times higher than the stresses in a spherical particle with the same volume. With the reconstructed positive electrode structure, local effects in the LIB cathode electrode during galvanostatic discharge process have been studied. The simulation results reported that large current density usually occurs at the joints between cathode active material particles and in the small channels in electrolyte, which will generate high electric joule power. By using the 3D real image of a LIB cathode electrode, numerical simulation results revealed that the spatial distribution of variable fields such as concentration, voltage, reaction rate, overpotential, and etc. in the cathode electrode are complicated and non-uniform, especially at high discharge rates.

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[pt] Por conta dos rápidos avanços tecnológicos, percebe-se uma mudança nos hábitos e das necessidades das pessoas. Há uma dependência cada vez maior de aparelhos eletrônicos como smartphones, notebooks etc. Construir baterias com grande capacidade energética é um dos desafios atuais para aumentar a autonomia dos aparelhos eletrônicos. Entretanto, uma alternativa que pode ajudar a manter aparelhos eletrônicos por mais tempo longe das tomadas é o compartilhamento de baterias. Existem na literatura muitos estudos envolvendo o compartilhamento de baterias no contexto de veículos elétricos, porém não são encontradas aplicações em smartphones. Um parâmetro importante a ser monitorado neste contexto é o estado de saúde (SoH). Até o momento, não há um consenso na literatura acerca do melhor modelo para estimar o SoH de baterias devido à falta de métodos bem estabelecidos. Assim, o objetivo geral desta dissertação foi construir um modelo para estimar a curva de estado de saúde, por meio do estado de carga, com vistas a estimar a saúde de baterias de íon-lítio. O modelo proposto foi baseado em redes neurais recorrentes. Para treinar e validar o modelo, foi construído um sistema para a realização de ensaios destrutivos, sendo possível estudar o comportamento de baterias de íon-lítio ao longo de toda vida útil. O modelo proposto foi capaz de estimar o SoH das baterias estudadas com boa exatidão, sob diferentes parâmetros de carga/descarga. O diferencial do modelo são baixa complexidade computacional, mesmo envolvendo modelos de redes neurais, e serem adotados parâmetros de entrada de fácil medição. / [en] Because of the fast technological advances, there is a change in people s habits and needs. There is an increasing dependence on electronic devices such as smartphones, notebooks etc. Building batteries with great energy capacity is one of the current challenges to increase the autonomy of electronic devices. However, an alternative that can help keep electronic devices longer away from sockets is battery swap. There are many studies in the literature involving the sharing of batteries in the context of electric vehicles, but no applications are found in smartphones. An important parameter to be monitored in this context is state of health (SoH). To date, there is no consensus in the literature about the best model for estimating battery SoH due to the lack of well-established methods. Thus, the objective of this dissertation is to build a model to estimate the state of health curve, with a view to classifying the health of lithium-ion batteries, through state of charge curve, for applications involving battery swap aiming to use in smartphones. The proposed model was based on recurrent neural networks. To train and validate the model, a system was built to perform destructive tests, being possible to study the behavior of lithium-ion batteries throughout its useful life. The proposed model was able to estimate the SoH of the batteries studied with good precision, under different charge / discharge parameters. The distinction of the model is low computational complexity, even involving neural network models, and easy-to-measure input parameters are adopted.

Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. Using data from only the first 100 cycles, in a data set of 124 cells where lifetimes span between 150 and 2300 cycles, this work combines parametric linear models with non-parametric Gaussian process regression to achieve cycle lifetime predictions with an overall accuracy of 8.8% mean error. This work presents a relevant contribution to current research as this combination of methods is previously unseen when regressing battery lifetime on a high dimensional feature space. The study and the results presented further show that Gaussian process regression can serve as a valuable contributor in future data-driven implementations of battery health predictions. / Datadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.

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The energy crisis and environmental pollution have become increasingly prominent in recent years. Lithium batteries have attracted extensive attention due to their high energy density, safety, and low pollution. To further study how the battery works, it is necessary to establish an accurate model conforming to the battery characteristics. As the core function of a battery management system(BMS), accurate state of charge(SoC) estimation dramatically improves battery life and performance. This thesis selects a ternary lithium battery in the centre for advanced life cycle engineering(CALCE) dataset for a study of cell modeling and SoC estimation. The second-order Thevenin equivalent circuit model is selected as the cell model due to a trade-off between model complexity and accuracy. The parameters to identify include OCV, internal ohmic resistance, polarized internal resistance and capacitance. They were obtained with the MATLAB toolbox at various SoC state points under different temperatures. The ‘terminal voltage comparison’ method is utilized to verify the identification's accuracy. The simulation results turn out to be satisfactory. Then cell SoC can be estimated after cell modeling. First, the principles of the Coulomb counting method, OCV method and EKF method are analyzed. The state space equations required in SoC estimation are determined by discretizing the non-linear equivalent circuit model. The simulation results are compared with the experimental results in the HPPC discharge experiment. Furthermore, the robustness of the EKF algorithm is further investigated. The results prove that the EKF algorithm has high precision, fast convergence speed and strong anti-interference capability. Last but not least, the research on battery pack SoC estimation was continued. How to expand a single cell into a battery pack is analyzed, including aggregating cells into a pack and scaling a cell model to a pack. In addition, battery pack SoC is individually estimated by the 'Big cell' method and 'Short board effect' method. The result is not so good, indicating that further work can be done to improve the SoC estimation accuracy. / Energikrisen och miljöföroreningarna har blivit allt mer framträdande de senaste åren. Litiumbatteri har väckt stor uppmärksamhet på grund av sin höga energitäthet, säkerhet och låga föroreningar. För att ytterligare studera hur batteriet fungerar är det nödvändigt att etablera en exakt modell som överensstämmer med batteriets egenskaper. Som kärnfunktionen hos BMS förbättrar noggrann SoC-uppskattning dramatiskt batteriets livslängd och prestanda. Denna avhandling väljer ett ternärt litiumbatteri i CALCE-datauppsättningen för forskning. Dessutom slutförs cellmodellering och SoC-uppskattning baserat på det. Den andra ordningens Thevenins ekvivalenta kretsmodell väljs som cellmodell på grund av en avvägning mellan modellens komplexitet och noggrannhet. Parametrarna som måste identifieras inkluderar OCV, intern ohmsk resistans, polariserad intern resistans och kapacitans. De erhölls med MATLAB-verktygslådan vid olika SoC-tillståndspunkter under olika temperaturer. Metoden "terminalspänningsjämförelse" används för att verifiera identifieringens noggrannhet. Simuleringsresultaten visar sig vara tillfredsställande. Sedan kan cell SoC uppskattas efter cellmodellering. Först analyseras principerna för Coulomb-räknemetoden, OCV-metoden och EKF-metoden. Tillståndsrymdsekvationerna som krävs vid SoC-uppskattning bestäms genom att diskretisera den icke-linjära ekvivalenta kretsmodellen. Simuleringsresultaten jämförs med de experimentella resultaten i HPPC-utsläppsexperimentet. Dessutom, robustheten hos EKF-algoritmen undersöks ytterligare. Resultaten bevisar att EKF-algoritmen har hög precision, snabb konvergenshastighet och stark anti-interferensförmåga. Sist men inte minst fortsatte forskningen kring SoC-uppskattning av batteripaket. Hur man expanderar ett enskilt batteri till ett batteripaket analyseras, inklusive aggregering av celler till ett paket och skalning av en cellmodell till ett paket. Dessutom uppskattas batteripaketets SoC individuellt med "Big cell"-metoden och "Short board effect"-metoden. Resultatet är inte så bra, vilket indikerar att ytterligare arbete kan göras för att förbättra SoC-uppskattningens noggrannhet.

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Kolfiber CF kan användas som positiv elektrod i strukturella batterier om de beläggs med ett aktivt material, såsom litiumjärnfosfat LFP. Fördelen med att använda kolfibrer som elektroder är att de samtidigt kan bära mekanisk belastning och lagra elektrisk energi. Det finns flera tekniker för att belägga kolfibrerna. I denna rapport kommer en jämförelse att göras av fibrer som belagts med elektroforetisk deponering, sprutbeläggning och pulverimpregnering. Elektrokemisk karakterisering kommer att avgöra och utvärdera prestandan hos dessa tre tekniker. Cellerna som monterades med sprutbeläggda och pulverimpregnerade prover visade de högsta kapaciteterna, 141 mAh/g vid C/10 respektive 139 mAh/g vid C/14. Vidare testning utfördes på de pulverimpregnerade proverna för att studera elektriska egenskaper och beteende, såsom elektrokemisk impedansspektroskopi EIS, cyklisk voltammetri CV och långtids-cykling. Svepelektronmikroskop SEM analys genomfördes för att observera ytmorfologin och förstå hur de elektrokemiska testerna kan påverka fibrernas yta. / Carbon Fibres (CF) can be used as the positive electrode in structural batteries if they are coated with an active material such as Lithium Iron Phosphate Oxide (LFP). The advantage of using carbon fibres as electrodes is that they simultaneously can carry the mechanical load and store electrical energy. There are several techniques to coat the carbon fibres. In this report, a comparison will be made on fibres coated using electrophoretic deposition, spray coating and powder impregnation. Electrochemical characterisation will determine and evaluate the performance of these three techniques. Cells assembled with spray-coated and powder-impregnated samples delivered the highest capacities, 141 mAh/g at C/10 and 139 mAh/g at C/14, respectively. Further testing was conducted on the powder-impregnated samples to study the electrical properties and behaviour, such as Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and long-term cycling. Scanning Electron Microscopy (SEM) analysis was performed to see the surface morphology and understand how electrochemical testing can affect the surface of the fibres.