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<p>Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. </p> <p><br></p> <p>The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. </p> <p><br></p> <p>In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. </p> <p><br></p> <p>The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages. </p>

Energy storage is crucial for intermittent renewable energy sources, electric vehicles, and portable devices. The continuously increasing energy consumption in these industries necessitates the enhancement of commercial lithium-ion batteries (LIB), especially regarding their safety and energy density. Historically, aqueous electrolytes were the norm in the battery industry. Prior to the development of lithium batteries, most commercially significant batteries used water as the solvent. In the past decade, "highly concentrated" electrolytes resurrected the notion of an aqueous lithium-ion battery (ALIB). Significant efforts have been made since then to comprehend the interfacial stability of these high-concentration electrolytes, and make them suitable for use in batteries especially high voltage ones. Another candidate for future batteries is All-Solid-State Batteries (ASSB) as they have the potential to double, or even triple, the energy density figures we currently achieve in LIBs mainly due to their ability to utilize lithium metal anode which has the highest specific capacity among anodes (3860 mAh g⁻¹), lowest reduction potential (-3.04 V vs SHE), and low density (0.53 g cm⁻³). This thesis first proposes a phenomenological model to describe the microstructure of aqueous electrolyte and the relation between their phase diagrams with ionic conductivity; highlighting a common correlation between the eutectic composition and peak ionic conductivity in conductivity isotherms. we then propose an empirical model correlating ionic conductivity with both molar concentration and temperature. The aim of this portion of the thesis is to provide an in depth understanding of aqueous electrolytes' physical properties in a way that can help researchers optimize the energy density and the cost of ALIBs. Moving further, the thesis presents two novel composite solid electrolytes (CSE) that were developed and fully characterized. Both of which were composed of the following four components; polyethylene oxide (PEO), lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt, lithium lanthanum titanate (LLTO) perovskite inorganic ceramic and the polymer plasticizer succinonitrile (SN). The careful formulation of these CSEs was based on the trade-off between film forming ability and ionic conductivity. The optimized polymer rich CSE proved to have better characteristics when compared to its ceramic rich alternative. ASSBs employing both CSEs were successfully charged and discharged when coupled with lithium metal anode and in-lab prepared composite cathode. The developed thin and flexible CSEs could be utilized in small applications (Wh-KWh) such as in consumer electronics and flexible biomedical devices (e.g., pacemakers) or larger applications (kWh-MWh) such as in EVs and large format storage for the electrical grid.

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.

Since their introduction in the late 20th century, lithium-ion batteries have become the leading battery technology for portable devices and electric vehicles due to their high energy density and rechargeability. However, the increasing demand for a longer battery life span is hindered by the fading of the battery’s charge capacity over prolonged use. This reduction in charge capacity stems from electrochemical and mechanical degradation of the battery cells. The main research focus in the literature has been on the chemical degradation of battery cells. However, the mechanical degradation also substantially contributes to the battery’s capacity degradation. Therefore, it is crucial to understand the mechanical properties of the battery cells to be able to mitigate mechanical degradation. The battery’s mechanical degradation stems from the electrode layers’ constituents. This thesis aims to model the positive electrode’s mechanical properties by recreating its granular microstructure using the discrete element method. In Papers 1 and 2, a discrete element method modelling framework is developed, which can reconstruct a positive electrode layer of a lithium-ion battery, simulate manufacturing processing steps, and determine the mechanical properties of the electrode layer. The framework uses two contact models, representing the positive electrode material in the form of particles and a binder agent, which gives the electrode layer its structural integrity. The framework is used to link the mechanical behaviour of the electrode particles and the binder agent to the mechanical behaviour of the entire electrode layer. The framework is able to capture the layer’s pressure sensitivity and relaxation behaviour, properties which have been displayed in the literature through experimental testing. / Sedan de introducerades i slutet av 1900-talet har litiumjonbatterier blivit den ledande batteriteknologin för portabla enheter samt elfordon på grund av deras höga energidensitet och återladdningförmåga. Den ökade efterfrågan på utökade batterilivslängder är dock hämmad av reduceringen av uppladdningskapacitet över längre användningstider. Denna reducering av laddningskapacitet kommer från elektrokemisk och mekanisk degradering av battericellerna. Det största forskningsintresset i litteraturen har varit på den kemiska degraderingen av battericellerna. Dock ger den mekaniska degraderingen ett betydande bidrag till batteriets kapacitetsdegradering. Därför är det viktigt att förstå battericellens mekaniska egenskaper för att kunna förhindra mekaniskdegradering. Batteriets mekaniska degradering beror på elektrodlagrets beståndsdelar. Denna avhandlings målsättning är att modellera den positiva elektrodens mekaniska egenskaper genom att återskapa dess granulära mikrostruktur med hjälp av diskret elementmetodik. I Artikel 1 och 2 utvecklades ett ramverk för modellering med användning av diskreta elementmetoden, vilket kan återskapa det aktiva lagret för en positiv elektrod, simulera tillverkningsprocesser, samt fastställa elektrodlagrets mekaniska egenskaper. Ramverket använder två kontaktmodeller som representerar det positiva elektrodmaterialet i form av partiklar samt ett bindemedel, som ger elektrodlagret dess strukturella integritet. Ramverket används för att undersöka hur de mekaniska egenskaperna för det hela elektrodlagret beror på egenskaperna för de aktiva partiklarna samt bindemedlet. Ramverket kan fånga lagrets tryckkänslighet samt dess relaxering, egenskaper som har påvisats i litteraturen genom experimentell provning. / <p>Qc240322</p>

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The master's thesis is devoted to establish optimal parameters produced composites of iron oxide and carbon (FeOx/C) through the physicochemical study of the samples obtained for further use as anode materials of lithium-ion batteries (LIA). This interdisciplinary work was made in the laboratory of chemistry of compounds of rare-earth elements of the The Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences as well as with the use of the equipment of the Ural Center for Shared Use «Modern nanotechnologies» of Ural Federal University named after the first President of Russia B. N. Yeltsin. / Магистерская диссертация посвящена установлению оптимальных параметров получения композитов из оксида железа и углерода (FeOx/C) посредством физико-химического исследования полученных образцов для дальнейшего использования в качестве анодных материалов литий-ионных аккумуляторов (ЛИА). Данная работа носит междисциплинарный характер и была выполнена в лаборатории химии соединений редкоземельных элементов ИХТТ УрО РАН, а также с использованием оборудования УЦКП «Современные нанотехнологии» УрФУ им. Б. Н. Ельцина.

<p dir="ltr">Lithium-ion batteries are being used increasingly more often to provide power for systems that range all the way from common cell-phones and laptops to advanced electric automotive and aircraft vehicles. However, as is the case for all battery types, lithium-ion batteries are prone to naturally occurring degradation phenomenon that limit their effective use in these systems to a finite amount of time. This degradation is caused by a plethora of variables and conditions including things like environmental conditions, physical stress/strain on the body of the battery cell, and charge/discharge parameters and cycling. Accurately and reliably being able to predict this degradation behavior in battery systems is crucial for any party looking to implement and use battery powered systems. However, due to the complicated non-linear multivariable processes that affect battery degradation, this can be difficult to achieve. Compared to traditional methods of battery degradation prediction and modeling like equivalent circuit models and physics-based electrochemical models, data-driven machine learning tools have been shown to be able to handle predicting and classifying the complex nature of battery degradation without requiring any prior knowledge of the physical systems they are describing.</p><p dir="ltr">One of the most critical steps in developing these data-driven neural network algorithms is data procurement and preprocessing. Without large amounts of high-quality data, no matter how advanced and accurate the architecture is designed, the neural network prediction tool will not be as effective as one trained on high quality, vast quantities of data. This work aims to gather battery degradation data from a wide variety of sources and studies, examine how the data was produced, test the effectiveness of the data in the Interfacial Multiphysics Laboratory’s autoencoder based neural network tool CD-Net, and analyze the results to determine factors that make battery degradation datasets perform better for use in machine learning/deep learning tools. This work also aims to relate this work to other data-driven models by comparing the CD-Net model’s performance with the publicly available BEEP’s (Battery Evaluation and Early Prediction) ElasticNet model. The reported accuracy and prediction models from the CD-Net and ElasticNet tools demonstrate that larger datasets with actively selected training/testing designations and less errors in the data produce much higher quality neural networks that are much more reliable in estimating the state-of-health of lithium-ion battery systems. The results also demonstrate that data-driven models are much less effective when trained using data from multiple different cell chemistries, form factors, and cycling conditions compared to more congruent datasets when attempting to create a generalized prediction model applicable to multiple forms of battery cells and applications.</p>

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.

Syftet med denna studie är att forska i hur olika rederier och besättningar förhåller sig till avsaknaden av regelverk gällande transport av fordon med litiumjonbatterier. Tidigare forskning visar att bränder i litiumjonbatterier skiljer sig från bränder i konventionella bilar då det finns risk för att termisk rusning initieras i cellerna vilket medför att återantändningsrisken är stor och branden svårsläckt med begränsade mängder vatten. Metoden som har använts är semistrukturerade intervjuer i syfte att få en dialog med välutvecklade svar mellan respondenten och forskarna genom en intervjuguide. En rederirepresentant och en befälhavare från tre olika ro-ro rederier som vartdera trafikerar tre olika fartområden intervjuades. Resultatet visar att det råder delade meningar från branschen om regelverk och risken med att transportera elbilar beroende på vilket fartområde och typ av ro-ro fartyg som transporten bedrivs på. Fortsatt forskning inom effektiva släckmetoder med fokus på släckning av elbilar ombord på ro-ro fartyg rekommenderas. / The purpose of this study is to research how different shipping companies and crews relate to the absence of regulations. Previous research shows that fires in lithium-ion batteries differ from fires in conventional cars because there is a risk of thermal runaway being initiated in the cells, which means that the risk of re-ignition is high, and the fire is difficult to extinguish with limited amounts of water. The method used is a semi-structured interview aimed at creating a dialogue with well-developed responses between the respondent and the researchers through an interview guide. A shipping company representative and a captain from three different ro-ro shipping companies with three different trafficked zones were interviewed. The results show that there are differing opinions within the industry regarding regulations and the risk of transporting electric cars depending on the speed range and type of ro-ro vessel used for transportation. More research on how to effectively extinguish fires in electric cars onboard ro-ro ships is recommended.