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.

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.

Qu’il s’agisse de s’éclairer, de se chauffer, de s’alimenter sainement, de se soigner, de se véhiculer, de s’informer ou encore de se distraire, l’énergie a toujours été au centre des préoccupations et sa conversion en électricité est désormais omniprésente. Le lourd constat environnemental à la suite de l’exploitation intensive de sources fossiles a mené à une indispensable transition vers les énergies renouvelables. Souvent intermittentes, il est nécessaire de les stocker, généralement grâce à des batteries. Parmi les différentes technologies, cette thèse traite des batteries lithium-ion pour le stockage de l’énergie solaire. En effet, cette thèse a pour but l’étude d’un dispositif « tout-en-un » capable de convertir l’énergie solaire et de la stocker. Pour se faire, un semi-conducteur organique photoactif de la famille des pérylènes diimides (PDI) a été emprunté au domaine des cellules solaires organiques et couplé à un matériau phare et durable des batteries lithium-ion : le LiFePO4 (LFP). Cette thèse se décompose en trois parties selon une méthodologie qui vise à la compréhension fondamentale de transferts électroniques photoinduits, en amont du développement d’un dispositif. Pour aboutir à une preuve de concept, une étude de l’extinction de fluorescence du PDI en présence de LFP a d’abord été menée, afin de vérifier l’injection d’électrons en provenance du matériau de batterie dans le semi-conducteur excité. Ce travail a été fait en solution puis à l’état solide, pour la mise au point d’une photoélectrode. Ces deux études ont permis de comprendre les pré-requis du matériau d’électrode positive de batterie pour qu’il soit photoxydé, puis des résultats de spectroscopie Raman ont démontré l’importance des interfaces dans la mise en contact du PDI et du LFP. Finalement, forts d’une première preuve expérimentale de photocharge au sein d’un dispositif « photobatterie », le PDI a été polymérisé et son implémentation dans une photoélectrode de batterie lithium-ion a pu être optimisée. Ses rôles multiples (photoactif, photooxydant, conducteur électronique et liant) ont permis de générer un photocourant sans que cela ne soit au détriment du fonctionnement de la batterie. / Converting energy to electricity is ubiquitous because it plays a vital role in daily life whether for lighting, heating, health, transport, information or entertainment. Societal energy demands are often met with fuel fossils that have had deleterious environmental effects. Transitioning to renewables can mitigate these adverse outcomes. Renewable energy is often intermittent, requiring it to be stored for use during periods when the energy is unavailable. Batteries have become viable means to this end. Among the different technologies, this manuscript examines lithium-ion batteries for solar energy storage. Indeed, this work puts forward an all-in-one device: a device capable of converting and storing solar energy. To this end, a well-known photoactive organic semi-conductor in solar cells (perylene diimide; PDI) was coupled to a conventional and durable electrode material (LiFePO4; LFP) for lithium-ion batteries. This manuscript is divided into three discrete parts following the methodology to demonstrate the fundamental underlying processes of the future all-in-one device before its development: light harvesting and electron transfer. Towards a proof of concept, the thesis systematically studied the light mediated processes in solution, in the solid state, and in an operating device. Initial studies examined the fluorescence quenching of PDI with LFP. This was to validate the injection of electrons from the battery material to the photoexcited semi-conductor indeed occurred. The same emission studies were applied in the solid state for developing a photoelectrode. The two studies generated knowledge about the compositional and architectural requirements of the positive electrode material for it be photoxidized by PDI. Raman spectroscopy further demonstrated the importance of interfaces between the battery material and the organic semiconductor. These enabled a photocharge when the photobattery was illuminated. The PDI was next polymerized and enabled a photocurrent in the battery, courtesy of its collective properties (light harvester, photo-oxidant, electronic conductor, and binder).

Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C. Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training. A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km. / Thesis / Master of Applied Science (MASc)

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