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Detta projekt syftar till att undersöka den termiska rusningsprocessen hos ett litiumjonbatteri med en fluorfri elektrolyt och jämföra den med en kommersiellt använd fluor-innehållande elektrolyt. Battericellerna innehöll silikon-grafit som anod och LiNi0.6Mn0.2Co0.2O2 (NMC622) som katod. Den fluorfria elektrolyten var baserad på litium bis(oxalato)borat (LiBOB) i organisk lösning med additivet vinylen karbonat(VC). Det jämfördes med en fluor-innehållande elektrolyt med LiPF6 i samma organiska lösning tillsammans med VC och fluoroetylene karbonat (FEC). De termiska stabilitetstesterna utfördes med Accelerating Rate Calorimetry (ARC) och Differentiell svepkalorimetri (DSC). Både knappceller och pouchceller har undersökts med hjälp av ARC. Trots flera försök med olika uppställning kunde den termiska rusningen inte bli detekterad för någon av celltyperna, med slutsatsen att en störremängd aktivt material behövs. Istället användes DSC för att undersöka de termiska reaktionerna hos batteri-komponenterna. Resultaten visade att anoden var mer termisk stabil med den fluorfria elektolyten, medan samma elektrolyt visade mindre termisk stabilitet på katoden. Vidare undersökningar behövs dock för bekräftelse av katoden. / This project aims to investigate the thermal runaway process of fluorine-free lithium ion battery cells and to compare this with a commercially used fluorinated electrolyte. The cells consisted of a silicon-graphite composite anode and a LiNi0.6Mn0.2Co0.2O2(NMC622) cathode. The non-fluorinated electrolyte used was based on lithiumbis(oxalato)borate (LiBOB) in organic solvents with the additive vinylene carbonate(VC). Moreover, the fluorinated electrolyte consisted of LiPF6 in the same organic solvents together with VC and fluoroethylene carbonate (FEC). The thermal stability measurements have included Accelerating Rate Calorimetry (ARC) and Differential Scanning Calorimetry (DSC). Moreover, both coin cells and pouch cells have been examined by ARC. However, thermal runaway could not be detected for either type of cells, concluding that a greater amount of active material was needed. In order to measure the thermal reactions of the battery components, DSC was used. These results concluded that the anode was more thermally stable with a non-fluorinated electrolyte. However, the thermal stability appeared to be lower for the cathode, therefore, further investigation is needed for confirmation of the cathode.

This thesis concerns the use of advanced solid-state NMR methods to investigate local structural features and ion dynamics in a series of paramagnetic cathode materials for lithium and sodium ion batteries. A variety of polyanionic phosphate and fluorophosphate derivatives were explored to identify characteristics that ultimately improve battery performance. Solid-state NMR is an excellent method to probe such materials, as it offers the unique ability to track the charge-carrying alkali ion (Li or Na) over the course of the electrochemical process, adding insight not obtainable by bulk characterization techniques. Selective inversion exchange experiments were used to elucidate ion diffusion pathways in low-mobility Li ion conductors Li2MnP2O7 and Li2SnO3. Contrasting experimental results highlight significant differences observed when the method is applied to paramagnetic versus diamagnetic systems, with the former being much more complicated to study with traditional exchange spectroscopy methods. Selective inversion was similarly applied to a new lithium iron vanadate framework, LiFeV2O7, where the changing ion dynamics as a function of electrochemical state of charge were quantified, allowing for the development of a model to explain the corresponding phase changes in the material. This represents the first example of an ex situ Li-Li exchange study for a cathode material, particularly where the conductivity changes are linked directly to a change of ion exchange rates. Additionally, 23Na NMR spectroscopy was additionally used to investigate Na2FePO4F as a potential Na ion battery cathode, where ex situ NMR measurements successfully determined the local Na ion distribution in the electrode as a function of electrochemical cycling. In combination with density functional theory (DFT) calculations, the NMR results lead to the construction of a biphasic desodiation model for Na2FePO4F cathodes. Finally, possible defect formation in sodium iron fluorophosphate was investigated with a variety of methods including 23Na NMR, DFT calculations, powder X-ray diffraction and Mössbauer spectroscopy. / Thesis / Doctor of Philosophy (PhD) / Lithium ion batteries are considered to be at the forefront of current energy storage development, offering high energy density in a small and lightweight package. This thesis delineates the investigation of materials for both lithium and sodium ion batteries via nuclear magnetic resonance methods. Slow Li ion dynamics were investigated and quantified in three lithium-conducting materials: Li2MnP2O7, Li2SnO3, and LiFeV2O7 via the use of selective inversion NMR experiments. In the case of the latter, the ion dynamics were probed ex situ during the course of battery cycling, where a maximum in Li mobility is observed approximately half way through the charge-discharge cycle. Additionally, a potential Na ion cathode material, Na2FePO4F, was found by ex situ methods to reveal a biphasic mechanism for the desodiation of the electrode during charging. This mechanism and the NMR data used to discover it were further supported by ab initio calculations.

Ultra-fast charging of electric vehicles will soon be available to charge the batteries in less than 15 minutes to 80% state of charge. However, very few studies of batteries under these conditions exist. To design a battery pack with ultra-fast charging in mind, more information about batteries is needed, both electrically and thermally. In this thesis, the performance of three specific commercial lithium-ion batteries during ultra-fast charging is investigated and their thermal behaviour is simulated for use in the battery pack design process. The cells are charged at 1C to 6C current rates, or as high as 10C, and the surface temperature of each cell is measured. The loss calculated from the charging tests are used in a thermal analysis of the three batteries using finite element analysis. The batteries are modeled in a simple cooling apparatus to determine their thermal management requirements in a pack, i.e., how effectively must the heat be removed from the cells to obtain a specific temperature in a pack. Test results show that ultra-fast charging is possible with very little loss; but, it is dependent on the battery. The analysis illustrates important trade-offs between the battery type, charge rate, and the thermal management system. This thesis presents a holistic view to the study of the batteries for eventual use in the design of a battery pack. The thermal performance of the batteries is equally important as their electrical (charge) performance. It also attempts to justify the observed behaviour of the batteries by their underlying chemical behaviour. The work here can be used as a jumping-off point for further work on the ultra-fast charging of batteries or the design of a battery pack. / Thesis / Master of Applied Science (MASc) / Ultra-fast charging of electric vehicles, i.e., fully charging the vehicle in less than 15 minutes, will soon be more available. However, literature on the ultra-fast charging of the batteries used in these vehicles is limited. It is not widely known whether the batteries can effectively achieve ultra-fast charging or how the batteries behave under these conditions. Charging batteries this fast means that the battery cells will heat up. The temperature of the cell greatly impacts its longevity and safety. The thesis attempts to address these questions by studying three commercial lithium-ion batteries, selected for specific characteristics, that show potential for ultra-fast charging. The batteries are charged at different rates to ultra-fast charging levels and the charge performance at each rate is determined. The temperature of the batteries is simulated with different cooling systems to determine how effectively must heat be removed from the batteries to maintain the cells at a specific temperature.

This thesis suggests a new approach to extend the lifetime of Lithium-ion batteries for a Home Energy Storage System equipped with a renewable energy source. The new configuration improves the lifetime of the energy storage device by using the pulsed charge-discharge method. The batteries in this system can be charged either using solar panels when solar energy is available or by the grid power during off-peak hours when the electricity cost is at its lowest rate. In the new configuration, the battery bank is split into two equal sections to employ pulsed charge-discharge method. Interrupting the charge or discharge current provides a relaxation time for the lithium ions to diffuse gradually into the electrodes material of Lithium-ion batteries, this reduces the damage in the microstructure of the electrodes and thus it helps to prolong the battery lifetime. The spilt bank strategy improves the longevity of Lithium-ion batteries while maximizing the solar energy utilization. This strategy leads to reduce the reliance on the grid power which decreases the consumer’s total energy cost as well. To show the usefulness of the new approach, different modes of operation are discussed in details along with simulation results. An experimental setup is also developed to evaluate the effectiveness of the new approach in extending the Lifetime of Lithium-ion batteries. / Thesis / Master of Applied Science (MASc)

The subject of this thesis is the application of magnetic resonance methods to the characterization and quantification of lithium-ion transport in a wide range of lithium-ion battery electrolyte materials relevant to the electromobility and energy storage sectors. In particular, field-gradient magnetic resonance techniques, in the form of PFG-NMR diffusivity measurements of both liquid- and solid-state electrolytes and in situ MRI of electrochemical cells, comprise the core means by which these characterizations were performed. PFG-NMR and ionic conductivity studies of a range of liquid-state electrolyte mixtures were performed, as a function of temperature, to assess how key mass and charge transport properties reflect differences in composition. In situ MRI was used to study the effect of temperature on steady-state concentration gradient formation in polarized liquid electrolytes, with the results quantitatively compared to model predictions. This approach was then extended, using a combination of MRI and spatially-resolved PFG-NMR, to study the interlinked effects of temperature and current density on concentration gradient formation, and to attempt a comprehensive characterization of the ion transport parameters with spatial resolution. Finally, PFG-NMR and MAS-NMR were applied in a solid-state electrolyte context to investigate compositional effects on ion transport in the argyrodite family of lithium-sulphide ion conductors, and the influence of macroscopic sample format (glass, crystalline powder, compressed crystalline pellet) on micro-scale ion transport in a thio-LISICON ion conductor. Taken together, the studies demonstrate the effectiveness of magnetic resonance methods for the robust elucidation of the means by which material properties impact ion transport in technologically-relevant lithium-ion electrolyte systems. / Dissertation / Doctor of Science (PhD) / Lithium-ion batteries are a critical component of the ongoing efforts to transition the global automobile fleet to electric vehicles and integrate renewable energy sources into the electricity grid. An important aspect of designing and optimizing lithium-ion batteries is a comprehensive understanding of the factors which impact the ability of the electrolyte in the battery to ferry the lithium ions from one electrode to the other, the process which enables them to release energy into the circuit to power a device. This thesis describes results obtained from measuring the diffusion of the ions within the electrolyte for both conventional liquid-state electrolytes, and emerging solid-state electrolyte materials. It also includes studies which make use of MRI to image the flow of ions within the liquid-state electrolyte of an operating battery mimic, and monitor the concentration changes of the ions across the electrolyte as a current is applied to it.

Recently, the production of lithium-ion batteries (LIB) has grown rapidly, highlighting the need for efficient and environmentally friendly recycling of LIB waste. In this work, the usage of so-called deep eutectic solvents (DESs) for the leaching of the LIB cathode material lithium cobaltoxide is investigated. The initial DESs investigated are mixtures of poly(ethylene glycol) (PEG200) and an organic acid: tartaric, ascorbic, citric, oxalic or succinic acid (PEG:TA (4:1), PEG:AA (8:1), PEG:CA (4:1), PEG:OA (2:1) and PEG:SA (6:1), the molar ratio in parenthesis). Thermogravimetric analysis shows that the solvents are stable up to 180-190 °C. DESs were analyzed with FTIR spectroscopy, pH was measured using a pH-meter and viscosity using a rolling-ball viscometer. The highest leaching efficiency was obtained using PEG:AA followed by PEG:OA, both having the ability to reduce Co(III). This ability was dominant over pH and viscosity influence. For the other three solvents, leaching efficiency increases in the order of decreasing pH (PEG:TA>PEG:CA>PEG:SA). More investigations of leaching as a function of time are needed to determine the impact of viscosity. PEG:CA and PEG:AA are used to study the impact of solid-to-liquid ratio. For PEG:AA the optimal S/L-ratio is 20 mg/g. For PEG:CA the optimal S/L-ratio is different for Li and Co. Three additional CA based DESs are made using ethylene glycol (EG) and choline chloride (ChCl): EG:CA, ChCl:EG:CA and ChCl:PEG:CA. Adding ChCl to EG:CA and PEG:CA increases the leaching efficiency from ca 5 and 10 to ca 30% and the color changes from pink to blue, indicating the formation of tetrachlorocobalt complexes. This reaction may produce chlorine gas, although none was detected using potassium iodide starch paper. Study of leaching as afunction of time of ChCl:EG:CA shows the reaction slows down significantly after 24 h, indicating that the reaction has reached or is near equilibrium at this point. Antisolvent crystallization of this solvent using ethanol was not succesful.

Physics-based electrochemical models are useful tools for optimizing battery cell and material design, managing battery use, and understanding physical phenomena, all of which are key in enabling adoption of batteries to electrify transportation, grid storage, and other high carbon emission industries. Fitting these models to experiments can be a useful approach to determine missing parameters that may be difficult to identify experimentally. In this dissertation, two use cases of this approach — model parameterization and degradation tracking — are explored. An introduction to the need for batteries and an overview of challenges in the field is presented in Chapter 1. Of these challenges, those that can be addressed by battery modeling solutions are discussed in further detail. An overview of continuum level physics-based electrochemical models is provided, and the case is made for the utility of parameter estimation. In Chapter 2, an extension of a published model for lithium trivandate cathodes for lithiumion batteries is outlined. While the original model described (de)lithiation and phase change in the cathode, the new model describes simultaneous lithiation of the original phase, lithiation of the newly formed phase, and phase change. Parameters associated with the thermodynamics and kinetics of charge transfer and lithium transport in the second phase are estimated directly from experimental data. This study serves as an example of using the model fitting approach to determine model parameters that would be difficult to isolate and measure experimentally. Chapter 3 explores a similar concept of model parameterization, this time focusing on the electrode tortuosity. Tortuosity is a hard to quantify parameter that describes how tortuous of a path lithium ions must travel through an electrode or separator. Because there are several experimental measurement techniques suggested in the literature that do not always provide consistent results, an approach to fit the tortuosity to a standard rate capability experiment is introduced. The Bayesian approach returns uncertainties in tortuosity estimates, which can be used to predict a range of outcomes for high-rate performance. Covariance between parameters in the model and their impact on uncertainties in tortuosity is also discussed. Beyond model parameterization, parameter estimation can also be useful in the context of tracking degradation by fitting a physics-based model over the course of cycling and interpreting the evolution of the parameter estimates. In Chapter 4, this idea is explored by fitting the model developed in Chapter 2 to cycling of an LVO cell. Parameter estimates are interpreted in conjunction with traditional tear down and electrochemical analysis to identify root causes of degradation for this cell. Depending on the number of parameters being simultaneously estimated, it can become an onerous task to fit model parameters, especially if the physics-based model cannot easily be enclosed in an efficient optimization algorithm. To this end, machine learning (ML) can be useful. If a ML model is trained offline on synthetic data generated by a battery model to map the observable electrochemical data to parameters in the battery model, the ML model can be deployed to estimate parameters from experiment. These models can be referred to as inverse ML models, since they perform the inverse task of a "forward" physics based model. The procedure described above is implemented in Chapter 5. Interpretable ML models are trained on published synthetic data generated by equivalent circuit models. Pseudo-OCV (slow charge, C/25) full cell voltage curves are passed into the inverse ML models to estimate degradation modes in lithium ion batteries and classify which electrode limits cell capacity. These models are useful in diagnosing the state of the battery at any given time. Accuracies of the inverse ML models are evaluated on independent test sets also composed of synthetic data and are published to benchmark future diagnostic studies. The insights derived from the trained ML models in terms of which features in the full cell voltage curves are predictive of the degradation modes are compared to expert insights. In chapter 6, the robustness of the inverse ML approach towards model-experiment disagreement is probed. If the experiment does not directly map onto the protocol used to generate the synthetic training data for the ML model, or if the model itself is inherently a poor descriptor of experiment, the inverse ML model will inevitably return inaccurate estimates. In this chapter, a feed forward neural network (NN) is employed as the inverse ML model. In two case studies of model-experiment disagreement, the NN returns biased parameter estimates. A simple data augmentation procedure is introduced to mitigate these biases. Chapter 7 ties together the understanding developed in the previous chapters by applying more robust neural networks to estimate parameters for LVO cells cycled at different rates. This study demonstrates how to interpret parameter estimates in conjunction with cycling data to gain mechanistic insight into degradation. A complex map of coupled degradation hypotheses is reduced to a smaller subset of possible mechanisms for two exemplary LVO cells, and parameter estimates for a larger set of LVO cells are discussed. The framework presented in this study synergistically combines experiment, physics-based modeling, and machine learning to better understand degradation phenomena.

Abstract Background:  Electric Vehicles (EVs) have been identified as a sustainable alternative to reduce the world’s dependence on fossil fuels. EV sales are starting to reach significant numbers. Subsequently, the demand for Lithium-Ion Batteries (LIBs), a key component in EVs, has increased. Due to the higher demand, a greater volume of LIBs will enter the waste stream. The waste-management strategies commonly used for the disposal of LIBs create potential risks of soil and air pollution, affecting the sustainability of EVs. The underdeveloped waste-management strategies, and the environmental and social risks related to improper disposal of LIBs, makes the study of second-life strategies of LIBs relevant.  Circular Economy (CE) promotes circular instead of linear flows of materials to reduce environmental impacts and maximize resource efficiency. LIB recycling is gaining popularity since LIBs contain valuable metals such as cobalt and lithium. A major challenge for LIB recycling is developing economical ways to extract and process metals from spent LIBs. The reviewed literature points to aresearch gap formed by the lack of study on the standardization of LIBs aimed to improve LIB recycling. The research gap is relevant because the reviewed literature points to a connection between standardization, innovation, and sustainability. Innovation of LIBs is a driver of sustainable transportation solutions, and the study of LIB standardization is relevant for two reasons. Firstly, standardization may influence further innovations needed to enable sustainable transportation. Secondly, standardization is relevant to achieve better recycling of LIBs and reduce the negative environmental and health effects of improper LIB disposal. Objectives:  During the development of the theoretical framework, two paradoxes were observed. The first paradox is between the dimensions of innovation and sustainability. Innovation acts positively on sustainability by enabling LIB development necessary to include EVs in the transportation sector. On the other hand, improper disposal of LIBs results in pollution affecting sustainability negatively. The second paradox is between the dimensions of standardization and innovation. Academics perceive standardization either as an enabler or as a hinder to innovation. Standardization enables innovation by giving a path and conditions for further technological developments, but standardization could also constrain the freethinking needed in innovation. Considering that innovation of LIBs has been a driver in the development of EVs, often described as a sustainable transportation solution, the study of LIBs’ standardization is relevant in the context of further innovation and higher sustainability goals.  The purpose of this study is to help to fill the gap in existing research on LIB recycling by exploring what factors in the dimensions of standardization, innovation, and sustainability are perceived as relevant for LIBs’ standardization aimed for better recycling. Moreover, the purpose of this thesis is also to explore how these factors influence further innovation of LIBs. Consequently, this thesis seeks to answer the following research question: What factors are perceived to be relevant for standardization of LIBs in the dimensions of standardization, innovation and sustainability aimed to improve recycling, and why? Also, how could these factors influence innovation of LIBs? Methodology:  Research in standardization aimed for recycling of LIBs is in its infancy. Therefore, the research problem is perceived as unstructured and modestly understood. Consequently, an exploratory research design has been selected for this thesis. The chosen research strategy was to conduct a case study focused on automotive firms. The selection of the case was based on four criteria. The first criterion was to select a firm in Sweden. The second criterion was to choose a firm with a defined strategy towards electric vehicles and a track of being innovative. A third criterion behind the selection was the firm’s potential for growth in units sold. The fourth criterion was that the firm should have a publicly known ambition or strategy towards sustainability. Volvo Cars Corporation (VCC) fulfilled all four criteria and was selected as the study case for this thesis. Data was collected by conducting semi-structured interviews with key organizational members involved in work related to second-life strategies of LIBs, development of LIBs, sustainability analysis, andlegislations or standardization. The sampling then focused on informants in the business areas of R&D,which covers these organizational activities. The interview questions were based on factors found to berelevant during the literature review within the dimensions of standardization, innovation, and sustainability. Also, each factor was associated with corresponding attributes. The formulation of the interview questions aimed to explore the relevance of an attribute for the standardization of LIBs aimed for better recycling and to explore how the factor influences innovation. The explored factors in the dimension of standardization were the source of standard, working groups for standardization development, practices during standard development, and design of standards. Meanwhile, the explored factors in the dimension of innovation were network effects and barriers to entry. In the sustainability dimension, the explored factors were exploration-exploitation balance and network effects.  The data analysis for the interviewees was based on first-order categorization of the answers, followed by creating second-order themes. The first-order categories and second-order themes were used for analyzing and assessing the relevance of the explored factors for the standardization of LIBs. Afterward, the second-order and aggregate themes were considered for the analysis of the factors’ influence on innovation. Moreover, the second-order themes have been used to identify new relevant factors to be considered in the standardization of LIBs with an influence in innovation. In this work, those identified relevant factors are referred asidentified elements. Finally, the analysis for the influence of the identified elements on innovation was visualized by the elaboration of a thematic map.  Results & Analysis:  The explored factors of standardization sources, working groups for standardization development, practices during development of standards and design of standards within the dimension of standardization were perceived as relevant and influenced innovation in combination with other identified elements. The most relevant identified elements were maturity, rigid standards, harmonization, flexibility, tacit knowledge, and culture. Maturity and rigid standards were perceived as the dominant among the identified elements by being important to avoid a negative influence on innovation caused by technology lock-in and obsolescence of the developed standards. Regarding identified elements with a positive influence on innovation, harmonization, culture, tacit knowledge, and flexibility are perceived as relevant to innovation by enabling common solutions in harmonized alliances, clarity in scope for the standards, and allowing freedom in the choice of methods. In the case of explored factors in the dimensions of innovation and sustainability, the explored factors of networks effects, entry barriers, and exploration-exploitation were perceived as relevant for the standardization of LIBs. Moreover, the results of the identified elements showed technology lock-in and iiiinefficient products as the biggest influence negatively affecting innovation and sustainability,respectively. The most important identified elements are maturity and investments for the technology lock-in aggregate theme, whereas efficiency, pricing, and environmental impact are identified for the case of inefficient products. Regarding aggregate themes enabling innovation and sustainability, they were presented in the form of resource and process optimization, market dynamics, and holistic view. Among the dominant identified elements enabling innovation are efficiency and specialized facilities.In addition to positive and negative aggregate themes, the aggregate theme of technology path was created. This aggregate theme refers to a technology development path that could influence innovation and or sustainability in potentially different ways: positive, neutral, or negative. The dominant identified elements in this theme are infrastructure, social realm, and consumer preference and awareness. Conclusions:  This thesis explored the relevance of factors in the dimensions of standardization, innovation, and sustainability on LIB standardization and their influence on the innovation of LIBs. The exploration of the factors’ perceived relevance answered the research question and resulted in new identified elementsrelevant to be considered in the standardization of LIBs with an influence on innovation. Consequently, this thesis fulfilled its purpose by helping to fill the gap in the existing research on LIB recycling. The main conclusions for the exploration of factors within the dimension of standardization are two. Firstly, the explored factors are relevant to be considered in the standardization of LIBs. Secondly, the exploration led to the new identified elements with negative and positive influence on innovation. The identified elements of maturity and rigid standards showed a negative influence on innovation in the form of technology lock-in or obsolescence of standards. However, the identified elements of harmonization, flexibility, tacit knowledge, and culture, showed a positive influence on further innovation of LIBs. The main conclusions for the dimensions of sustainability and innovation were that the explored factors are relevant in LIB standardization. Also, high compatibility between LIB manufacturers and recyclers raises challenges towards innovation while supporting sustainability. Also, consumer preferences and their shifts play a central role with negative and positive influence on innovation and sustainability and as a driver to certain segments of standardization.

Lithium-ion battery responses to bipolar pulse perturbations of less than two minute duration and one C-rate amplitude are studied as general-purpose diagnostics signals that encode the cell impedance, remaining charge, and degradation level. It is shown that the information is derived from a combination of the linear and nonlinear system dynamics of the electrochemical overpotentials, open-circuit voltage change, and hysteresis of the cell. Experimental data is analyzed using an equivalent circuit composed of a conventional resistor-capacitor pair model, a square-root-order convolution-defined diffusion element, and a piece-wise-linear open-circuit voltage element. This bipolar pulse model disaggregates the battery voltage response into its constituent dynamics and allows the nonlinearities to be isolated. The nonlinearities are crucial features which allow the battery charge, health, and incremental capacity features to be regressed directly from the pulse voltage response using ridge regression and feedforward neural networks. Assessment of different pulse shapes suggests that the diagnostics power of the pulse may increase with higher amplitude and shorter duration. Real-world applications are then investigated, including the estimation of charge imbalance using the series-module pulse response, and state-space formulation of the convolution-defined diffusion element. Further refinement of the pulse techniques could simplify battery diagnostics by providing, from a single pulse diagnostic, the key states of charge, health, and power necessary to operate a reliable system.