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In response to the present and projected growth of the EV industry, the development of a large-scale, reliable and efficient lithium-ion battery recycling sector is vital to ensure circularity of the embedded valuable metals and ensure overall sustainability of the technology. One of the main recycling procedures under development is based on hydrometallurgy. As a pretreatment step before lithium-ion batteries can undergo this process, they have to be deactivated to prevent uncontrolled release of the contained electrical energy. This deactivation step is often performed by deep discharging batteries to 0.0 V, instead of the usual lower cut-off around 3.0 V. Usually, deep discharging is performed by connection to resistors or through submersion in a salt solution. However, due to the discharge current derating proportionally to the terminal voltage, this procedure can be quite slow, especially if considerable rebound voltages are to be prevented. This work explores the feasibility of a faster discharge procedure in terms of discharge speed, effectiveness, and safety. The proposed procedure entails deep discharging at constant current using a controllable load, followed by applying an external short-circuit immediately. The C-rate during constant current discharging is varied to study its effects. The short-circuit is applied at a terminal voltage of 0.0 V or 1.0 V. The safety of both process steps is assessed experimentally. The main safety risks that are reviewed are the temperature rise and subsequent risk of thermal runaway, as well as the risk of electrolyte leakage due to pressure increase and swelling. In the experimental work, two types of large format prismatic NMC811 cells are deep discharged starting from an SoC of 0%. The experiments are limited to single cells. It is found that an additional 4% of additional capacity is available in the deep discharging region for a stationary cell at 0% SoC. The risk of thermal runaway is assessed as low based on the temperature measurements and a literature review. To investigate the rise in pressure, the thickness of all cells are measured, and the in situ pressure is measured for three samples. The risk for electrolyte leakage is assessed as low. The rebound voltage and cell thickness are followed up to one week after the discharge procedure. After a short-circuit of 30 minutes, the rebound voltage of all cells is near 2.0 V, but a slightly longer short circuit duration would be necessary to reliably achieve this threshold. The total procedure time is much shorter than those of alternative discharge procedures, while still remaining safe. / Som svar på den nuvarande och förväntade tillväxten inom elbilsindustrin är utvecklingen av en storskalig, tillförlitlig och effektiv återvinningssektor för litiumjonbatterier avgörande för att säkerställa cirkularitet för de inbäddade värdefulla metallerna och säkerställa teknikens övergripande hållbarhet. En av de viktigaste återvinningsmetoderna som är under utveckling baseras på hydrometallurgi. Som ett förbehandlingssteg innan litiumjonbatterier kan genomgå denna process måste de avaktiveras för att förhindra okontrollerad frisättning av den elektriska energi som de innehåller. Detta deaktiveringssteg utförs ofta genom djupurladdning av batterierna till 0.0 V, istället för den vanliga lägre gränsen runt 3.0 V. Vanligtvis utförs djupurladdning genom anslutning till resistorer eller genom nedsänkning i en saltlösning. Eftersom urladdningsströmmen avtar proportionellt mot terminalspänningen kan denna procedur dock vara ganska långsam, särskilt om man vill förhindra stora återkopplingsspänningar. I detta arbete undersöks genomförbarheten av en snabbare urladdningsprocedur när det gäller urladdningshastighet, effektivitet och säkerhet. Det föreslagna förfarandet innebär djupurladdning vid konstant ström med en kontrollerbar belastning, följt av omedelbar applicering av en extern kortslutning. C-hastigheten under urladdning med konstant ström varieras för att studera dess effekter. Kortslutningen appliceras vid en terminalspänning på 0.0 V eller 1.0 V. Säkerheten för båda processtegen bedöms experimentellt. De huvudsakliga säkerhetsriskerna som granskas är temperaturökningen och den efterföljande risken för termisk rusning, samt risken för elektrolytläckage på grund av tryckökning och svullnad. I det experimentella arbetet djupurladdas två typer av prismatiska NMC811-celler i storformat från en SoC på 0%. Experimenten är begränsade till enstaka celler. Det visade sig att ytterligare 4% kapacitet finns tillgänglig i djupurladdningsområdet för en stationär cell vid 0% SoC. Risken för termisk urladdning bedöms som låg baserat på temperaturmätningarna och en litteraturgenomgång. För att undersöka tryckökningen mäts tjockleken på alla celler och in situ-trycket mäts för tre prover. Risken för elektrolytläckage bedöms som låg. Återkopplingsspänningen och cellernas tjocklek följs upp upp till en vecka efter urladdningsproceduren. Efter en kortslutning på 30 minuter är returspänningen för alla celler nära 2.0 V, men en något längre kortslutningstid skulle vara nödvändig för att tillförlitligt uppnå detta tröskelvärde. Den totala tiden för proceduren är mycket kortare än för alternativa urladdningsprocedurer, samtidigt som den fortfarande är säker.

Des progrès dans les batteries aux ions lithium sont en cours de développement afin de répondre, entre autres, à la demande croissante des hautes densités d'énergie et de puissance pour le réseau électrique et en particulier pour l'application dans les véhicules électriques. Ces derniers remplacent écologiquement les véhicules à moteur à combustion interne et leurs succès est principalement dû à leur efficacité énergétique supérieure, à leurs faibles coûts d'exploitation et à leur profil respectueux de l'environnement par rapport aux véhicules à essence. Parmi les différents matériaux de cathode, les composés d'intercalation LiNixMnyCo1-x-yO2 (NMC) sont les meilleurs candidats pour des applications dans les batteries aux ions lithium à hautes performances. Des efforts sont en cours pour mettre en oeuvre des matériaux cathodiques à base de NMC riches en nickel pour répondre aux besoins environnementaux et énergétiques. Aussi séduisants soient-ils, ces matériaux de cathode présentent certains inconvénients liés à une forte réactivité, notamment à l'interface avec l'électrolyte. Pour contourner ces problèmes, des modifications de surface sont étudiées comme des solutions accessibles pour protéger le matériau actif et améliorer ses performances. Bien que diverses chimies et stratégies de revêtement soient publiées dans la littérature, notre approche consistant à combiner la synthèse et la modification de surface du matériau actif en une étape est aussi simple qu'efficace. Le présent manuscrit porte sur l’étude de ce composé. Deux méthodes de revêtement de surface ont été étudiées et leur matériau revêtu résultant a été comparé au matériau non revêtu. Après une caractérisation détaillée de ces matériaux, des études électrochimiques ont été menées afin d’évaluer leurs performances. Enfin, notre NMC622 revêtu de LiAlO2 en une seule étape s'est avéré efficace pour contrer la dégradation de la capacité du NMC et pour améliorer la stabilité structurelle des particules, améliorant ainsi leur cycle de vie. / Advances in lithium-ion batteries are being developed in order to meet, among other things, the increasing demand for high energy and power densities for the electric power grid and especially for application in electric vehicles. The latter are a green replacement for internal combustion engine vehicles, and their success is mostly due to their higher energy efficiency, low operating costs and eco-friendliness compared to gasoline-powered vehicles. Among various cathode materials, LiNixMnyCo1-x-yO2 (NMC) intercalation compounds are the best candidates for applications in high performance lithium-ion batteries. Efforts are underway to implement nickel-rich NMC-based cathode materials to meet environmental and energy needs. As appealing as they are, these cathode materials present certain drawbacks associated with high reactivity, especially at the interface with the electrolyte. To circumvent these issues, surface modifications are investigated as accessible solutions to protect the active material and enhance its performance. Although various coating chemistries and strategies are published in the literature, our approach of combining synthesis and surface modification of the active material in a single pot is as simple as it is efficient. The following manuscript will be covering the study of this material. Two methods of surface coating were studied, and their resulting coated material was compared to the uncoated material. After a detailed characterization of these materials, electrochemical studies were carried out to evaluate their performance. Finally, our resulting one pot LiAlO2- coated NMC622 has shown to be effective in counteracting NMC capacity degradation and improving the structural stability of the particles, thereby improving their cycle- life.

<div> Porous lithium ion battery (LIB) electrodes are comprised of electrochemically active material particles that store lithium and a surrounding conductive binder, liquid electrolyte, carbon black mixture that facilitates ionic and electronic transport. Typically, lithium diffusivity is several orders of magnitude smaller in the active material as compared to the surrounding electrolyte, making the electrode microstructure a governing factor in determining the balance between its lithium storage capacity and transport rate. Here, the effects of microstructure on the performance of LIBs are systematically analyzed at three length scales - the single particle length scale, the spatially resolved multiple particle length scale, and the porous electrode layer (homogenized) length scale. At the single particle length scale, a thermodynamically consistent variational framework is presented to examine the effects of crystallographic anisotropy, crystallographic texture, grain size, and grain morphology on the LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) chemistry. The theory was extended to the spatially resolved multiple particle length scale and the porous electrode layer length scale to explain the microstructural origin of experimentally observed instances of apparent phase separation in NMC111. At the electrode length scale, a data driven framework is presented to evaluate the electrochemical performance of a wide range of particle morphologies and battery architectures. Specifically, microstructural characteristics of 53 356 microstructures are assessed, and strategies to optimize electrode design parameters such as active particle morphology, spatial orientation, electrode porosity, and cell thickness are presented.</div><p></p>

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).

Due to the large share of production costs, the lifespan of an electric vehicle’s (EV) lithium-ion traction battery should be as long as possible. The optimisation of the EV’s operating strategy with regard to battery life requires a regular evaluation of the battery’s state-of-health (SOH). Yet the SOH, the remaining battery capacity, cannot be measured directly through sensors but requires the elaborate conduction of special characterisation tests. Considering the limited number of test facilities as well as the rapidly growing number of EVs, time-efficient and scalable SOH estimation methods are urgently needed and are the object of investigation in this work. The developed virtual SOH experiment originates from the incremental capacity measurement and solely relies on the commonly logged battery management system (BMS) signals to train the digital battery twins. The first examined dataset with identical load profiles for new and aged battery state serves as proof of concept. The successful SOH estimation based on the second dataset that consists of varying load profiles with increased complexity constitutes a step towards the application on real driving cycles. Assuming that the load cycles contain pauses and start from the fully charged battery state, the SOH estimation succeeds either through a steady shift of the load sequences (variant one) with an average deviation of 0.36% or by random alignment of the dataset’s subsequences (variant two) with 1.04%. In contrast to continuous capacity tests, the presented framework does not impose restrictions to small currents. It is entirely independent of the prevailing and unknown ageing condition due to the application of battery models based on the novel encoder–decoder architecture and thus provides the cornerstone for a scalable and robust estimation of battery capacity on a pure data basis.