Growth Of Solid Electrolyte Interface Research Articles

Characterization and Model-Based Investigation of Lithium-Ion Battery Cell Formation

The formation process of lithium-ion battery cells is one of the main contributors to the overall cell cost [1]. During this process step the solid electrolyte interface (SEI) is initially built. It is agreed upon that the composition and morphology of the SEI influences the safety and aging behaviour of the battery cell. However, the processes behind the initial build-up are not fully understood due to the manifold influencing factors, such as electrolyte composition, electrode structure, temperature, and formation protocol, which all impact the complex nanoscale processes at the interface [2,3,4]. High experimental effort is needed to investigate and optimize this process. Detailed multiscale models based on first principles can provide insights into the SEI formation but require many parameters that are difficult to determine and are also associated with uncertainty [5,6]. Therefore, those approaches are limited in terms of exploration and optimization of the formation process. Hence, there is a need for novel methods that enable to obtain information about the SEI growth and investigate the influential factors to effectively accelerate the development process. Here, we suggest a model-based approach that uses lumped growth models that can quickly be identified by using differential voltage data of formation experiments. Further, models are applied to simulate the impact of the formation protocol on the SEI formation with minimal experimental effort.Firstly, we present a parameter identification process for the applied growth model, which is based on a slow formation protocol and a reference measurement in a 3-electrode setup. The parameterization of the SEI model is conducted by combining coulometry and differential voltage analysis (DVA). Shifts of distinct peaks, i.e. feature, within the anode potential curve during the formation and the reference measurement are used to deduce the capacity loss due to the SEI growth. Using the SEI density and surface area of the electrode, the capacity loss is converted into an effective layer thickness, which is used to fit the parameters of the growth model. Within the SEI model different growth mechanisms can be implemented and compared. It is demonstrated that this approach can show differences in the SEI formation for a variation of electrolyte compositions, i.e. the electrolyte additives as can be seen in Figure 1. Afterwards, a simulation study for different formation protocols is presented. The identified growth model has been coupled with a pseudo-2-dimensional (P2D) model. Simulations are also compared to experimental data for various formation protocols and are shown to be in good agreement. Simulations reveal that the final effective film thickness is only slightly affected by current magnitude and mainly depends on the formation time as well as the anode potential. Furthermore, it can be seen that higher formation currents affect the homogeneity of the SEI across the electrode negatively.To sum up, the DVA analysis of the formation yields information about the SEI growth. This can be used to parametrize SEI growth models and investigate film growth mechanisms for different electrolyte compositions. Coupling growth models with P2D models enables to explore the influence of the formation protocols. To conclude, this novel approach enables to significantly accelerate the optimization of battery cell formation. In future work the approach could be refined to enable application to full cell voltage data, which would allow its use within battery production lines and could support end of line testing.[1] A. Kwade, W. Haselrieder, R. Leithoff, A. Modlinger, F. Dietrich, K. Dröder, Current status and challenges for automotive battery production technologies, Nature Energy, 2018, 3, DOI: 10.1038/s41560-018-0130-3[2] G. E. Blomgren, Liquid electrolytes for lithium and lithium-ion batteries, Journal of Power Sources, 2003, 119, DOI: 10.1016/S0378-7753(03)00147-2[3] S. J. An, J. Li, C. Daniel, D. Mohanty, S. Nagpure, D. L. Wood, The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling, Carbon, 2016, 105, DOI: 10.1016/j.carbon.2016.04.008.[4] K. Xu, Electrolytes and interphases in Li-ion batteries and beyond, Chemical reviews, 2014, 114(23), DOI: 10.1021/cr500003w.[5] A. Wang, S. Kadam, H. Li, S. Shi, Y. Qi, Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries, npj Computational Materials, 2018, 4, DOI: 10.1038/s41524-018-0064-0[6] F. Röder, R. D. Braatz, U. Krewer, Multi-scale simulation of heterogeneous surface film growth mechanisms in lithium-ion batteries, Journal of the Electrochemical Society, 2017, 164 (11), DOI: 10.1149/2.0241711jes. Figure 1

Ageing Rate of Li-Ion Battery Cells As a Function of Temperature, C-Rate, and State of Health

The ageing rate and consequentially service life of mass-produced lithium-ion batteries depends on many external and internal factors. Some of the more important ones are the charge-discharge rate, ambient temperature, electrode thickness and state of health (SoH). This makes the estimation of the lifetime of battery cells very challenging and time-consuming. This motivates the development of models capable of determining the ageing behavior of a battery cell with relatively little experimental data.In this study1 we build on our previous work2,3 and use Arrhenius plots of the ageing rate to analyze the service life of battery cells in a relatively simple and straightforward way. We cycle aged two types of cells – commercial 5 Ah high-energy 21700 cells with Si/graphite anode (≈3 % Si) and LixNi0.90Co0.05Al0.05O2 + LiyNiO2 (NCA + LNO) cathode and lab-made 0.1 Ah pouch cells with LixNi0.33Mn0.33Co0.33O2 (NMC111) cathode and graphite anode. The ageing is ensured by systematically cycling the cells in a temperature range of -15 °C to 60 °C at various C-rates (0.2 C – 1 C).The capacity fade data is analyzed to obtain the ageing rate (% of SoH per cycle) as a function of temperature, which forms the V-shaped Arrhenius plots shown in Figure 1. Each set of data clearly shows two slopes that cross at intermediate temperatures. As shown previously experimentally2,3 and theoretically,4 the slopes correspond to two ageing mechanisms – lithium plating in the low temperature region and growth of solid-electrolyte interface (SEI) at high temperature region. The intersection of the two Arrhenius slopes denotes a transition between the dominating ageing mechanism and also denotes the optimum temperature at which the battery cell has the highest service life.By analyzing the data, we show that the crossover temperature shifts towards higher temperature values with increasing C-rate and increasing thickness of the anode, as both increase the electrode area-normalized current. The crossover temperature depends on the SoH of the battery. The precise trend depends on the cell chemistry, as we observe increase of the crossover temperature for one type of cells and decrease of crossover temperature for the other type of cells. The shift of the crossover temperature to lower temperatures observed in pouch cells could be explained by a more rapid loss of cyclable lithium, whereas the shift towards higher C-rates observed in commercial 21700 cells can be observed due to increasing practical C-rates as the electrodes degrade and loose some of their initial capacity. By using relatively little input data, we are able to predict the temperature- and C-rate-dependent end of life of a battery cell with good precision.Evaluating the Arrhenius-type dependences of the ageing rate of the battery cells provides valuable insights for use in battery management systems and extending the service life of battery cells. Acknowledgements Gints Kucinskis acknowledges Latvian Council of Science project “Cycle life prediction of lithium-ion battery electrodes and cells, utilizing current-voltage response measurements”, (no. LZP-2020/1-0425). The part of the research performed at ZSW was carried out in the framework of the industrial collective research program (IGF no. 20884 N/2). It was supported by the Federal Ministry for Economic Affairs and Energy (BMWi) through the AiF (German Federation of Industrial Research Associations eV) based on a decision taken by the German Bundestag. References G. Kucinskis, M. Bozorgchenani, M. Feinauer, M. Kasper, M. Wohlfahrt-Mehrens, T. Waldmann, submited to J. Power Sources.T. Waldmann, M. Wilka, M. Kasper, M. Fleischhammer, and M. Wohlfahrt-Mehrens, J. Power Sources, 262, 129–135 (2014) https://linkinghub.elsevier.com/retrieve/pii/S0378775314004352.M. Bozorgchenani, G. Kucinskis, M. Wohlfahrt-Mehrens, and T. Waldmann, J. Electrochem. Soc., 169, 030509 (2022) https://iopscience.iop.org/article/10.1149/1945-7111/ac580d.X.-G. Yang and C.-Y. Wang, J. Power Sources, 402, 489–498 (2018) https://linkinghub.elsevier.com/retrieve/pii/S0378775318310462. Figure 1. Arrhenius plots based on ageing rate for (a-e) commercial 21700 cells at 100 %, 95 %, 90 %, 85 % and 80 % of SoH and (f,g) lab-made pouch cells at 100 % and 95 % SoH. Figure 1

Determination of Leakage Currents Via Voltage Hold and Voltage Relaxation Method Using High Precision Coulometry - a Comparison and Optimization Study

Parasitic side reactions on the surface of the anode and the cathode of lithium-ion batteries contribute significantly to calendar and cyclic aging [1, 2]. In order to investigate these parasitic side reactions, such as solid electrolyte interface growth, this study focuses on two methods broadly utilized to determine leakage currents: the voltage hold and the voltage relaxation method. Regarding the voltage relaxation method, the open circuit voltage (OCV) decay is observed over weeks without allowing active electrode de-/lithiation [3] and subsequently, a small pulse is performed to calculate the leakage current [4]. For the voltage hold method, a defined voltage is kept constant, which compensates the parasitic side effects and allows active electrode de-/lithiation to maintain the state of charge (SoC) [5, 6].To compare these methods, different results are found in literature. On the one hand, both methods were reported to deliver individual results [3, 7], while other research activities [8] found the variance only at 100% SoC.Therefore, voltage hold and voltage relaxation were compared in this study, utilizing high precision coulometry (HPC). The measurements were conducted on 16 commercial LGChem INR18650MJ1 cylindrical cells at 25 °C, 40 °C and 55 °C and different SoCs of 10%, 50%, 90%, and 100% SoC, respectively. The cells were preconditioned to each SoC and were subsequently stored for 30 days to minimize relaxation and anode overhang effects. Afterwards, voltage hold and voltage relaxation measurements were carried out for 21 days at each temperature and SoC. In addition to the discharge pulse, an incremental capacity analysis (ICA) was conducted for all three temperatures through the whole voltage range to compare and validate the results obtained.The measurements of this study delivered similar results for the voltage hold and the voltage relaxation method, especially at 10% SoC and 50% SoC. Consequently, the voltage hold did not contribute to additional parasitic side reactions from allowing active de-/lithiation of the electrode. Minor deviations were found for 90% SoC and 100% SoC, for which one possible explanation may be the flat shape of the OCV curve, among others. In addition, the results show a strong dependency on the pulse length and strength.This study was part of the project ExZellTUM III, funded by the German Federal Ministry of Education and Research (BMBF) under grant number 03XP0255, supervised by Project Management Jülich (PTJ).Literature [1] Smith, A.; Burns, J.; Dahn, J.: A high precision study of the coulombic efficiency of Li-ion batteries, In: Electrochemical and Solid-State Letters 13, p. A177, 2010 [2] Birkl, C. R.; Roberts, M. R.; McTurk, E.; Bruce, P. G.; Howey, D. A.: Degradation diagnostics for lithium ion cells, In: Journal of Power Sources 341, p. 373-386, 2017 [3] Zilberman, I.; Sturm, J.; Jossen, A.: Reversible self-discharge and calendar aging of 18650 nickel-rich, silicon-graphite lithium-ion cells, In: Journal of Power Sources 425 (9), p. 217-226, 2019 [4] Schmidt, J. P.; Weber, A.; Ivers-Tiffée, E.: A novel and fast method of characterizing the self-discharge behaviour of lithium-ion cells using a pulse-measurements technique, In: Journal of Power Sources 274, p.1231-1238, 2015 [5] Lewerenz, M.; Käbitz, S.; Knips, M.; Münnix, J.; Schmalstieg, J.; Warnecke, A.; Uwe Sauer, D.: New method evaluating currents keeping the voltage constant for fast and highly resolved measurement of Arrhenius relation and capacity fade, In: Journal of Power Sources 353, p.144-151, 2017 [6] Vadivel, N. R.; Ha, S.; He, M.; Dees, D.; Trask, S.; Polzin, B.; Gallagher, K. G.: On leakage current measured at high cell voltages in lithium-ion batteries, In: Journal of The Electrochemical Society, 164 (2), p. 508-A517, 2017 [7] Theiler, M.; Endisch, C.; Lewerenz, M.: Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells, In: Batteries 7 (2), p. 22–22, 2021 [8] Käbitz, S.; Gerschler, J. B.; Ecker, M., Yurdagel, Y.; Emmermacher, B.; André, D.; Mitsch, T.; Uwe Sauer, D.: Cycle and calendar life study of a graphite LiNi1/3Mn1/3Co1/3O2 Li-ion high energy system. Part A: Full cell characterization, In: Journal of Power Sources 239, p. 572-583, 2013

Model-Informed Si Electrode Design Considering Dynamic Pore-Closure and Stack Pressure Effects

Silicon is novel Li-ion battery anode chemistry with exceptional theoretical energy densities. However, this alloying material has significant challenges with non-passivating solid-electrolyte interface (SEI) formation and significant chemo-mechanics issues. These issues have been studied extensively at the particle-level. However, extensive SEI formation and dynamic particle chemo-mechanics need to be accounted for when designing the overall electrode microstructure. For example, Si particle expansion can result in electrolyte pore-closure. During charging (Si lithiation), Si particles near the separator lithiate faster than Si near the current-collector. Thus, Si particles near the separator begin to expand faster and close-off electrolyte access to particles near the current collector. This heterogeneous through-plane lithiation then results in low overall electrode utilization, especially at high rates (i.e.,>1 C). The “choking off” effect is a result of a strong feedback between heterogeneous lithiation, Si expansion, and electrolyte pore closure.To aid in Si electrode design and account for dynamic particle expansion/contraction, the standard pseudo-2D battery model is reformulated to account for finite-strain chemo-mechanics. This model explores the trade-offs between initial electrode porosity, electrode thickness, rate-capability, and external pressure. Additionally, the model implements simplified SEI growth dynamics to explore how SEI growth effects should be accounted for in electrode design.The Figure illustrates polarization responses for a 1C charge for a Si/NMC532 cell. The different colors indicate different initial anode porosities. As shown in Figure a, the low initial porosity cases results in significant polarization, but additional gains after 50% initial porosity are significantly diminished. These results are shown for an external pressure of 15 psi. Figure b illustrates the cell thickness as a function of time. As illustrated, the lower porosity case starts at a shorter initial thickness. For all cases, the cell thickness increases as Si lithiates/expands. Figure c illustrates the anode porosity at the cut-off voltage as a function of anode thickness. As shown, the lower initial porosity case has significant pore-closure on the right-side at the separator interface. The higher initial porosity cases have increased anode lengths because the Si utilization is improved due to the reduced pore-closer effects. The graphic illustrates the main features of the reformulated pseudo-2D battery model. Figure 1

An Enhanced Battery Aging Model Based on a Detailed Diffusing Mechanism in the SEI Layer

The impetus for this study is the lack of a detailed knowledge on the formation mechanism of the solid electrolyte interface (SEI) layer and the diffusion mechanisms within this layer that impacts the predictive abilities of the current mathematical models. Specifically, most models continue to employ a constant value of diffusion coefficient along with several lumped fitting parameters, instead of a variable formulation that is dependent on the temperature and concentration of Li-ions, to characterize Li-ion batteries (LIBs). As a result, the current models fail in predicting the capacity fading accurately. In overcoming this gap, we have employed a previously proposed temperature and concentration-dependent diffusion equation to present a modified mathematical model that is capable of accurately predicting the capacity fading and SEI growth rate as a function of temperature, concentration, and time, with just two significantly simplified temperature dependent fitting parameters. Further, these parameters need to be adjusted only for new temperatures. Our enhanced model is validated with respect to the experimental data for different operational conditions, including open circuit condition with different initial state of charges (SOCs) as well as cycling with a constant current.

Modeling NCA/C6-Si battery ageing

The rapidly growing demand for energy storage has spurred the development of advanced Li-ion batteries. Physical-based modeling techniques have emerged as a powerful tool to assist the development of battery technologies because of the advantages of relating battery performances to internal physical and (electro)chemical processes. In the present paper, a pseudo-two-dimensional (P2D) based model has been developed for Li-ion batteries with NCA cathodes and C6/Si blended anodes. Coupled with a solid electrolyte interface (SEI) growth model and a loss of active materials (LAM) model, the P2D-based model has been applied to simulate the electrochemical behavior of pristine cells and the degradation of aged cells. For pristine cells, a competing reaction occurs between C6 and Si during (dis)charge, resulting in a sequential (de)lithiation and diverse stress behavior. For aged cells, the SEI growth mainly causes the battery capacity to fade under different cycling conditions. LAM also plays an important role in battery capacity loss. SEI growth is sensitive to the temperature, cycle time, cycle number, and cycle current, while LAM is significantly influenced by temperature and current. During storage, the main factor causing battery capacity losses is the SEI growth, which is affected by the storage time, storage state-of-charge, and temperature.

Heat generation and thermal runaway mechanisms induced by overcharging of aged lithium-ion battery

Overcharging occurs owing to the malfunction of charge control and inappropriate battery management system. Overcharging mechanisms, aging mechanisms, and the influence of aging on overcharging are studied in this work. The results indicate that normal charging, lithium plating, electrolyte oxidation and decomposition, excessive electrolyte decomposition, and solid electrolyte interface decomposition and regeneration occur with charging. Much heat is produced in the reactions between lithium plating and the electrolyte, which is the main reason for thermal runaway during overcharging. The primary aging mechanisms of a battery cycled at 40 ℃ and 10 ℃ are solid electrolyte interface growth and lithium plating, respectively, where the former increases and the latter decreases the safety of the lithium-ion battery during overcharging. The trigger for thermal runaway during overcharging changes from a local, micro-level internal short circuit to solid electrolyte interface decomposition and regeneration when the state of health is less than 70 % cycled at 40 ℃. If the electrolyte is depleted and the temperature is less than that promoting cathode decomposition, thermal runaway does not occur during overcharging.

A mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth

A mechanistic calendar aging model of lithium‐ion battery considering solid electrolyte interface growth

Modelling Solvent Consumption from SEI Layer Growth in Lithium-Ion Batteries

Predicting lithium-ion battery (LIB) lifetime is one of the most important challenges holding back the electrification of vehicles, aviation, and the grid. The continuous growth of the solid-electrolyte interface (SEI) is widely accepted as the dominant degradation mechanism for LIBs. SEI growth consumes cyclable lithium and leads to capacity fade and power fade via several pathways. However, SEI growth also consumes electrolyte solvent and may lead to electrolyte dry-out, which has only been modelled in a few papers. These papers showed that the electrolyte dry-out induced a positive feedback loop between loss of active material (LAM) and SEI growth due to the increased interfacial current density, which resulted in capacity drop. This work, however, shows a negative feedback loop between LAM and SEI growth due to the reduced solvent concentration (in our case, EC), which slows down SEI growth. We also show that adding extra electrolyte into LIBs at the beginning of life can greatly improve their service life. This study provides new insights into the degradation of LIBs and a tool for cell developers to design longer lasting batteries.

Investigation on lithium-ion battery degradation induced by combined effect of current rate and operating temperature during fast charging

Operating temperature and current rate are the main parameters that induce lithium-ion battery (LIB) degradation during the fast-charging process. In this study, fast-charging degradation was investigated using a commercial 18650 Nickel-Manganese-Cobalt battery at different charging current rates (C-rates) and operating temperatures. The degradation process was evaluated using electrochemical impedance spectroscopy and incremental capacity analysis. The electrode morphology change was investigated via a multiscale post-mortem analysis. The battery degradation mechanism at different C-rates and temperatures was characterized, and the coupled effects of the C-rate and operating temperature on the battery capacity fade were determined. With increasing operating temperatures, the most significant factor influencing the battery capacity fade changed gradually from lithium plating to the growth of the solid-electrolyte interface (SEI). An experimental analysis revealed that the battery capacity fade at the higher C-rates can be alleviated to some extent by employing higher operating temperatures. In comparison, the capacity fade at lower C-rates accelerated with increasing operating temperature. The changing and position shifting of the peaks in the incremental capacity curves were analyzed. It was found in the post-mortem analysis that the fast charging degradation was embodied in the macroscopic detachment of electrode material, the microscopic cracking of electrode particle, the SEI growth, the lithium plating, and the structural change of layered material crystal. The results of this study provide insight into the development of fast-charging strategies and the design of battery management systems.

In-situ analysis of solid-electrolyte interphase formation and cycle behavior of Sn battery anodes

Tin is in principle one of the most promising candidates for a lithium-ion battery anode due to its larger capacity than that of commercial graphite anode. But, researchers are still confused about the capacity fade especially caused by the formation of solid electrolyte interface (SEI). Here, cyclic voltammetry is conducted in combination with a quartz crystal microbalance to characterize the growth of SEI in-situ, since detected mass change per charge (MPE) enables identification of the ab/desorbed species. In advanced analysis, we combine three different characteristics including mass spectra, real-time MPE and average MPE, for the overall and segmented study on the long-term SEI formation. Beside the formation of the organic and inorganic SEI layer in the first cycle, the SEI is continuously affected by the formation of Li2O during lithiation and the oxidation of Sn during de-lithiation, which was further proved by XRD, XPS, SEM and TEM results. Beside Li, a heavier species (probably Li2O) is reversibly cycled with a relative contribution of 23% compared to the cycled Li. We believe that the analysis strategy used here for the SEI on Sn provides a significant example for general in-situ SEI growth studies.

Cost and Carbon Footprint Reduction of Electric Vehicle Lithium-Ion Batteries through Efficient Thermal Management

The decarbonisation of the transport sector is an imperative step in our efforts to drastically reduce greenhouse gas emissions. In this context, electric vehicles (EVs) powered by lithium-ion batteries have emerged as one of the most promising options. However, in order to make the transition from internal combustion engine vehicles towards EVs economically and environmentally beneficial, it is crucial to reduce battery life cycle costs and carbon footprint. To do so, a key strategy is to increase the battery lifetime as an increased use phase can reduce the pressure on raw materials and counterbalance the cost and environmental impacts incurred during battery manufacturing. Battery lifetime is impacted by several factors such as depth of discharge and cycling rates [1]. Furthermore, temperature-induced stress can lead to accelerated capacity fade. Elevated temperatures can lead to capacity fade caused by chemical side reactions such as solid-electrolyte interface growth or electrolyte decomposition; too low temperatures can result in lithium plating. Therefore, efficient thermal management systems (TMS) are crucial as they ensure an optimal cell operating temperature during usage and thus slow down adverse temperature effects and battery degradation. In this study, the impact of the TMS on battery lifetime and in turn on life cycle cost and carbon footprint is evaluated [2]. To this end, the battery lifetime for various TMS including air cooling, surface cooling, tab cooling and immersion cooling is simulated. The cycle lifetime is then further implemented in carbon footprint and life cycle cost models to calculate economic and environmental impacts during the manufacturing and use phase. Indeed, it is demonstrated that by switching the battery TMS from air cooling to liquid cooling, the battery life cycle cost and carbon footprint can be reduced by 27 % and 25 %, respectively. Moreover, an optimised cell design is proposed, which increases the achievable heat extraction rate through the tabs enabling a more efficient tab cooling system. Tab cooling has the advantage towards surface cooling that cell temperature gradients, which accelerate battery degradation, are reduced thus leading to a prolonged battery cycle life [3]. Implementing tab cooling, the carbon footprint and life cycle cost can be reduced by 35 % and 40 %, respectively, compared with conventional cell designs combined with air cooling systems. This study reveals that engineering solutions such as battery management systems are a swift and impactful tool to significantly increase the economic and environmental benefits of EVs.[1] Birkl, C.R., Roberts, M.R., NcTurk, E., Bruce, P.G., Howey, D.A. J. Power Sources 341 (2017), pp. 373-386. [2] Lander, L., Kallitsis, E., Hales, A., Edge, J.S., Korre, A., G. Offer, G. Appl. Energy 289 (2021), p. 116737. [3] Hunt, I.A., Zhao, Y., Patel, Y., Offer, G. J. Electrochem. Soc. 163 (2016); pp. A1846–52. Figure 1

Effect of Electrochemical and Mechanical Properties of SEI on Dendritic Growth during Lithium Deposition on Lithium Metal Electrode

In the past three decades, lithium-ion batteries experienced a long series of successful improvements, which nowadays make them a widespread technology. Intercalation electrodes, like graphite in the negative electrode, drove large part of this success. On the other hand, they constitute a limit in terms of energy density, which makes them unlikely to match the increasing demands set by renewable energies and especially electric mobility. Thus, it becomes fundamental to investigate new materials that overcome these limits. Lithium metal represents the optimal candidate for the negative electrode, due to its high theoretical capacity (3860 mAh.g-1) and low potential (-3.04 V SHE). On the other hand, the major drawback of this technology is the formation of dendrites, which are lithium structures that form during its deposition. They can cause thermal runaway and internal short-circuits, potentially leading to explosion. Moreover, during lithium stripping, some parts of the lithium electrode tend to be isolated in the electrolyte. This phenomenon causes a reduction of the electroactive lithium quantity thus a limited lifetime of the cell. Therefore, a deeper comprehension of the interfacial phenomena involved during lithium deposition and stripping is necessary to make lithium metal a viable and competitive option as a negative electrode, fostering the transition towards the next generations of batteries.The proposed model starts from the stability analysis of the electrode interface proposed by Newman and Monroe [1] that considers the effect of the mechanical properties on the lithium deposition. Different from it, the presented model studies a time-dependent variation of the surface and it includes a pseudo-2D representation of the Solid Electrolyte Interface (SEI) component. The approach to model the SEI is based on the work of Liu and Lu [2]; and it includes both SEI creation, due to deposition side reactions, and SEI thickness deformation due to the change in geometry of the underlying electrode. In addition, the model considers the effect of the mechanical properties of the SEI on the surface evolution of the electrode. Therefore, starting from the initial surface geometry and the electrochemical and mechanical properties of the components, the model is able to predict the conditions that favors dendritic growth. The figure shows lithium electrodeposition, starting from the same surface defect, on the lithium metal electrode in a liquid electrolyte. After the same quantity of lithium is deposited, it is possible to distinguish different patterns of surface evolution, due to the difference in magnitude of the current density applied: a quasi-uniform growth at low current density (left), a mossy behavior at medium current density (center) and a branching pattern at high current density (right). The difference in dendritic regime is strictly connected to the local change in the SEI thickness, as shown in the figure, which confirms the importance of this component, often neglected in the literature of dendrites models.Since SEI has a major impact on dendritic growth, a consistent set of experiments has been designed to provide its parameters to the model. Electrochemical Impedance Spectroscopy (EIS) was applied to find the electrochemical parameters of the SEI (e.g. SEI diffusion coefficient, SEI resistivity), while Atomic Force Microscopy (AFM) was conducted to find its mechanical properties (e.g. SEI Young’s modulus). The proposed model has the goal of guiding the experiments in finding ways to avoid or curtail dendrites formation, being able to simulate the effects on dendrites growth of operative conditions, SEI and electrolyte compositions, electrode surface defects and coatings. Starting from the model, innovative ways to increase the lifetime of the lithium metal anodes will be analyzed during the presentation; including coatings, surface morphology modification and pulse charging.[1] C. Monroe, J. Newman, Journal of the Electrochemical Society 151(6), A880-A886 (2004)[2] G. Liu, W. Lu, Journal of the Electrochemical Society 164(9), A1826-A1833 (2017) Figure 1

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and “break-in” mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

Dual‐Ligand Zn‐Based Metal–Organic Framework as Reversible and Stable Anode Material for Next Generation Lithium‐Ion Batteries

Metal–organic frameworks (MOFs) have received intensive scientific attention as electrode materials for lithium‐ion batteries because of their tailorable physical and chemical properties by incorporating different organic ligands. Herein, a zinc‐based MOF (Zn‐MOF) with a special dual‐ligand system, tris(4‐(1H‐1,2,4‐triazol‐1‐yl)phenyl)amine and dihydroxylterepthate, as anode material for lithium‐ion batteries is successfully fabricated. The activated Zn‐MOF based battery delivered a reversible and efficient lithium storage capacity of ≈200 mA h g−1 at 0.5 A g−1 with a 99% Coulombic efficiency over 1000 cycles. The sweep rate cyclic voltammetry and ex situ Fourier transform infrared spectroscopy on the electrode materials at different charging/discharging states reveal that lithium insertion in the organic moiety with a diffusion‐controlled process plays a critical role in the storage mechanism of the Zn‐MOF anode. Further spectroscopic analysis reveals the excellent material stability of dual‐ligand Zn‐MOFs with limited solid–electrolyte interface growth under long‐term charge–discharge operation, which is beneficial for next‐generation battery anodes.

Multiscale Investigation of the Diffusion Mechanism within the Solid-Electrolyte Interface Layer: Coupling Quantum Mechanics, Molecular Dynamics, and Macroscale Mathematical Modeling.

The solid-electrolyte interface (SEI) layer has a critical role in Li-ion batteries' (LIBs) life span. The SEI layer, even in modern commercial LIBs, is responsible for more than 50% of capacity loss. Due to the inherent complexity in studying the SEI layer, many aspects of its performance and characteristics, including diffusion mechanisms in this layer, are unknown. As a result, most mathematical models use a constant value of the diffusion coefficient, instead of a variable formulation, to predict LIBs' properties and performance such as capacity fading and the SEI growth rate. In this work, by employing a multiscale investigation using a combination of quantum mechanics, molecular dynamics, and macroscale mathematical modeling, some equations are presented to evaluate the energy barrier against diffusion and the diffusion coefficient in different crystal structures in the inner section of the SEI layer. The equations are evaluated as a function of temperature and concentration and can be used to study the diffusion mechanism in the SEI layer. They can also be integrated with other mathematical models of LIBs to increase the accuracy of the latter.

Composition‐Dependent Long‐Term Stability of Mosaic Solid‐Electrolyte Interface for Long‐Life Lithium‐Ion Battery

AbstractThe solid‐electrolyte interface (SEI) layer determines the fast charging capability and cycle life of a lithium‐ion battery. In‐depth investigation of the mosaic SEI layer‘s formation, growth, and stability are of utmost importance to obtain a stable electrochemical performance over cycles. We investigate the stability of the mosaic SEI layer during cycling at a high C‐rate (0.5 C). The composition of the SEI layer is governed by the current density‐dependent formation of inorganic and organic compounds. The inorganic compounds rich outer SEI layer and organic compounds rich inner SEI layer formed after the first lithiation helps in preventing SEI breakdown and growth during cycling. The SEI layer densifies during cycling as a result of the decomposition of the entrapped electrolyte. The composition‐dependent stability of the mosaic SEI layer stability opens up new possibilities for extending the cycle life of lithium‐ion batteries.

Early Detection of Lithium Plating in Lithium Ion Batteries: Using Multiple Physics-Based Electrochemical Signatures to Construct a Machine Learning Framework

A key challenge for Lithium-ion batteries (LiBs) is the capability to manage battery performance and predict lifetime, which is becoming critical as electric-vehicle (EV) adoption continues to increase. Early detection of battery-aging phenomena and the implications for the performance are crucial for maintaining warranty and avoiding safety-related liabilities. Among all of the aging phenomena in LiBs, loss of Li inventory (LLI) is a major reason behind capacity loss and shortened battery lifetime. LLI can be further separated into solid electrolyte interface (SEI) formation and Li plating. The growth of SEI layer is caused by continued usage of a LiB, in which the SEI layer thickens and leads to gradual capacity fade. Unlike SEI formation, Li plating causes drastic reduction of performance and safety issues in the LiB1, such as accelerated capacity fading and short circuiting. Therefore, Li plating should be identified and avoided early on.We established a machine learning (ML)-based algorithm framework for early identification of SEI and Li plating. This framework merges the strength of two mainstream approaches for estimating the state-of-health of LiB, namely the data-driven approach and physics-based modeling2. Meanwhile, we aim to identify the aging modes or mechanisms during battery cycling. In our approach, we analyze electrochemical signatures that are recorded along with battery aging cycles, then identify physically meaningful trends that are directly related to SEI growth or Li plating. Through this approach, multiple pieces of data can be analyzed in a data-driven approach, while the physical meanings are preserved as in the physics-based modeling.Here, we use graphite/NMC532 pouch cells with a variety of cell designs (anode loading, porosity) and charging protocols (CC-CV, 2-step, multi-step from 4C – 9C) to construct this classification framework. We discover that the evolving trend of capacity fade, end of charge voltage after rest (EOCV), and Coulombic efficiency (CE) can be combined into a logistic elastic net to build a ML classification model separating SEI growth and Li plating, as shown in Figure 1. The results also show that this framework differentiates the two cases as early as within the first 25 aging cycles. Additionally, we show that multiple electrochemical signatures have coherent response to aging phenomena and need to be integrated together to improve the accuracy in the classification. Because of the preservation of physical meanings, this approach has the potential to be extended to general cell designs and other types aging phenomena, such as degradation of electrodes. Ultimately, this early-decision framework will potentially shorten the battery design and test cycles, as well as provide insights into cell-design parameters and use guidelines to extend cell lifetime and delay the aging process. Waldmann, T., Hogg, B.I., and Wohlfahrt-Mehrens, M. (2018). Li plating as unwanted side reaction in commercial Li-ion cells – A review. J. Power Sources 384, 107–124.Hu, X., Xu, L., Lin, X., and Pecht, M. (2020). Battery Lifetime Prognostics. Joule 4, 310–346. Figure 1

Reversible oxide formation during cycling of Si anodes

Silicon is a hot candidate for battery anodes as its theoretical storage capacity is almost ten times larger than that of graphite. Volume expansion and kinetic limitations require the nanostructuring in particles, wires or thin films. One feature that still puzzles researchers is the solid electrolyte interface (SEI). Here we use an electrochemical quartz crystal microbalance (QCM) to inspect in-situ SEI formation on Si films. The measured mass uptake is split into a reversible part representing the battery function and an irreversible part attributed to continuous SEI formation. The evaluation of the latter quantifies the dependence of SEI growth on cycling window, rate and anode thickness. More striking is the reversible part. Advanced QCM mass spectrometry enables identification of the ab/desorbed species. Surprisingly, half of the battery storage is not due to lithiation but due to reversible adsorption of Li2O to the SEI layer. The dependence on rate and film thickness indicates this Li2O is formed by a self-limited, field-driven layer growth.

X-ray photoelectron spectroscopy as a probe for understanding the potential-dependent impact of fluoroethylene carbonate on the solid electrolyte interface formation in Na/Cu2Sb batteries

The solid electrolyte interface (SEI) forms from electrolyte decomposition during the initial discharge of half-cell batteries and is affected by the presence of electrolyte additives. Breaking down the initial discharge into stages, defined by voltage cut offs, can help discover the role of additives in SEI growth. In this study, X-Ray Photoelectron Spectroscopy (XPS) is used to analyze the SEI formed on electrodeposited, binder free Cu2Sb thin films in sodium ion half-cell batteries. The presence of fluoro-ethylene carbonate (FEC), an electrolyte additive known to enhance battery lifetime, has a significant effect on the carbon 1s XPS spectra. The concentration of oxygenated carbon environments are dramatically decreased when FEC is added to the system. These environments were present in samples without FEC before significant electrochemistry was observed, potentially displaying the reactivity of sodium metal with conventional carbonate electrolytes to form the initial components of the SEI before the battery is cycled. The differences observed when FEC is added are likely the chemical environments of the SEI that have the dramatic effect on battery performance. Interestingly, these results suggest that the critical aspects of SEI formation are determined before the active material is sodiated, with FEC playing an integral role.