A Nonlinear Model-Based Study for Dynamic-Steady-State Process Identification on Lithium-Ion Batteries

Determination of residual capacity, or ‘state of health’ (SOH), is important for the management of battery assets in service and when assessing batteries for re-use at the end of service. Nonlinear frequency response analysis (NFRA) has recently been demonstrated as a means to determine SOH, but the effects of temperature and state-of-charge (SOC) on battery NFRA measurements are not yet well understood. In this work, we investigate the effects of SOC on NFRA measurements performed on commercial 21700 cylindrical cells aged under different temperatures to various SOHs. Total harmonic distortion (THD%) is proposed as a capacity fade indicator. THD% in the range of 10 Hz–150 Hz are found to correlate well with SOH at all SOCs. For frequency < 1 Hz, the effect of aging on THD% is SOC dependent, with larger THDs for the new cell at low open-circuit voltages (OCVs) (2.75 V–3.2 V), and larger THDs for aged cells at higher OCVs (3.5 V–4.1 V). This work also demonstrates the feasibility of NFRA to distinguish between cells that have been aged differently based on temperature.

Electrochemical impedance spectroscopy (EIS) is one of the most important non-destructive diagnosis tools in lithium-ion battery (LIB) technology. EIS is routinely used for characterization of battery materials, optimization of manufacturing processes, and understanding of degradation mode. However, interpretation of EIS spectra is not a straightforward procedure. Typical EIS data of LIB have heavily overwrapped capacitive semicircles in Nyquist (complex impedance) plot because LIBs’ anodes and cathodes both involve several conduction processes. Widely used method for information separation is least-squares fitting based on discrete equivalent circuit model. However, this method requires a priori assumption on the number of capacitive processes. Destructive EIS measurement methods, such as insertion of reference electrode and symmetry cell construction, are powerful for separation of cathode/anode information. But these involve meticulous work, and battery structure or degradation state often make it difficult to obtain an accurate measurement result.Recently, distribution of relaxation times (DRT) analysis has increasingly used for EIS interpretation. This analysis treats an EIS spectrum as a continuous function of relaxation time, or time constant of infinitesimal RC equivalent circuit element. DRT analysis can distinguish several different capacitive processes without the need of prior model assumption. Application field of DRT analysis currently focuses on solid oxide fuel cell, but several LIB studies have been reported for LiFePO4 cathode system[1] and LiMn2O4-LiNixCoyMnzO2 cathode system[2]. Here we demonstrate that DRT analysis is a powerful tool for degradation analysis of LIBs with LiCoO2(LCO)-based cathode and graphite anode, and provide experimental verification of information obtained by DRT analysis.Commercial-size prototype LIBs with LCO-based cathode and graphite anode are used for this work. Charge-discharge cycling tests of the cells are performed at different temperature conditions. EIS measurements are conducted at fully charged state of every fifty cycles, with frequency range of 1 MHz to 10 mHz. For better separation of different processes, the EIS spectra are obtained at 23 deg C, 10 deg C and 0 deg C in a climate chamber. DRT is calculated by MATLAB with in-house code, which is based on Fourier transfer methods utilizing window function preprocessing[3]. To understand DRT results in physical viewpoints, several electrode analyses of disassembled cells are conducted at initial and the cycled state. Figure 1 shows Nyquist plots and the corresponding DRT plots of (a) low-temperature and (b) high-temperature cycled LIBs obtained at 10 deg C. In Nyquist plot, all spectra exhibit the shape of two depressed capacitive semicircles. At a first glance, both low-temperature cycling and high-temperature cycling appear to increase the size of low-frequency semicircles. But closer look shows that low-temperature cycling cause “merge” or heavier overwrap of two semicircles, whereas high-temperature cycling retains the separated feature. These characteristics are further clarified by DRT analysis. In each cell, we can identify four different peaks in DRT plots. The high-temperature cycled cell shows gradual increase in the lowest-frequency (1) peak, whereas the peak remains almost the same intensity in the low-temperature cycled cell and the middle-frequency (2) peak increases significantly. No change is observed for the (3) peak during the cycle test. Both cycling condition cause the increase in the highest-frequency (4) peak. Further teardown analyses verify that these DRT peaks can be attributed to different electrode degradation phenomena, which are related to cycling temperature. These results demonstrate the ability of DRT analysis as a degradation diagnostics tool for LIBs with various kinds of material systems. [1] J. P. Schmidt, T. Chrobak, M. Ender, J. Illig, D. Klotz, E. Ivers-Tiffée, J. Power Sources, 196 5342 (2011). [2] B. Stiaszny, J. Ziegler, E. Krauß, J. P. Schmidt, E. Ivers-Tiffée, J. Power Sources, 251 439 (2014).[3] H. Schichlein, A. C. Müller, M. Voigts, A. Krügel, E. Ivers-Tiffée, J. Appl. Electrochem., 32 875 (2002). Figure 1

Various impedance-based and nonlinear frequency response-based methods for determining the state-of-health (SOH) of commercial lithium-ion cells are evaluated. Frequency response-based measurements provide a spectral representation of dynamics of underlying physicochemical processes in the cell, giving evidence about its internal physical state. The investigated methods can be carried out more rapidly than controlled full discharge and thus constitute prospectively more efficient measurement procedures to determine the SOH of aged lithium-ion cells. We systematically investigate direct use of electrochemical impedance spectroscopy (EIS) data, equivalent circuit fits to EIS, distribution of relaxation times analysis on EIS, and nonlinear frequency response analysis. SOH prediction models are developed by correlating key parameters of each method with conventional capacity measurement (i.e., current integration). The practical feasibility, reliability and uncertainty of each of the established SOH models are considered: all models show average RMS error in the range 0.75%–1.5% SOH units, attributable principally to cell-to-cell variation. Methods based on processed data (equivalent circuit, distribution of relaxation times) are more experimentally and numerically demanding but show lower average uncertainties and may offer more flexibility for future application.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for the non-destructive diagnosis of lithium ion batteries (LIBs). Thanks to the measurement over a wide frequency range, electrochemical processes of different kinetics are reflected in EIS, which contains rich information on battery aging and degradation.Distribution of relaxation times (DRT) method has been increasingly utilised to interpret EIS data. Compared to conventional EIS presentations such as Nyquist plot and Bode plot, DRT isolates the processes with different time constants and gives an explicit showcase of timescales in the battery [1]. Another advantage of DRT is the possibility to separate contributions from cathode and anode without the meticulous work to make three-electrode cells, given that relaxation time peaks can be identified with half cells [2]. The information acquired through DRT can provide insight into battery degradation paths and help improve data-driven methods for battery diagnosis/prediction.This work looks into the DRT evolution of commercial NMC/graphite cylindrical cells (Sony US14500VR2 with a nominal capacity of 715mAh) under various cycling conditions. Before the experiment starts, the cells are in shipping status i.e. undergone pre-aging and kept at approximately 70% state of charge (SoC). All cells then go through one 0.1C/0.1C cycle and 99 continuous cycles under various C-rates and temperatures (see table 1). The lower C-rates under 25ºC and 35ºC are scaled against 45ºC by Arrhenius relation. All charges are constant-current constant-voltage and all discharges are constant-current, between 3V and 4.2V. Impedance spectra are measured over 10kHz~0.1Hz range after being fully charged and fully discharged in each cycle.The impedance spectra measured after every full charge of cells 1~5 are shown in Figure 1(a) ~ (e). The decrease of semicircle size as temperature increases can be seen. During continuous cycling, internal resistance and semicircle size grow monotonically as cycle number increases. While the semicircles in each EIS curve are overlapping, the impedance components with different relaxation times are clearly separated in the DRT plotted in Figure 2. In Figure 2(a) and (b), there are constantly four relaxation time peaks as cycle number increases in 25ºC. The feature time constant in lies tightly within 25ms~50ms range, corresponding to 20Hz~40Hz frequencies, regardless of C-rates. In Figure 2(c) and (d), relaxation time peaks differ with C-rates right after 35ºC cycling starts. With 0.5C/1C rate, the evolution trend of relaxation time is similar to 25ºC cases. With 1C/2C rate however, there are initially only three relaxation time peaks and the mid-frequency (between 0.1s and 1s) peak “splits” into two as cycle number approaches 100. This could be related to joule heating resulting from higher C-rate or kinetic factors, and is more similar to the 45ºC case as shown in Figure 2(e). In 45ºC with 1C/2C rate, the relaxation time peaks start with three and “split” into four around the 60th cycle.While the evolution of DRT needs further interpretation and more long-term experiments, we can see the effective of time scale separation for EIS under various cycling conditions. The time scale features can be incorporated into data-driven methods e.g. machine learning to aim at accurate battery diagnosis/prediction. Also, the fact that feature relaxation times (may be battery-type specific) are distributed in a relatively narrow range suggests the EIS measurement can be done with reduced frequency points to save on-board resources for future system-level application.This work is supported by Faraday Institution (EP/S003053/1) and North-East Centre of Energy Materials (EP/R021503/1) funded by EPSRC.[1] Ciucci, F., & Chen, C. (2015). Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A Bayesian and hierarchical Bayesian approach. Electrochimica Acta, 167, 439-454.[2] Sabet, P. S., Stahl, G., & Sauer, D. U. (2018). Non-invasive investigation of predominant processes in the impedance spectra of high energy lithium-ion batteries with Nickel-Cobalt-Aluminum cathodes. Journal of Power Sources, 406, 185-193. Figure 1

Development of future battery generations and quality control of current lithium-ion battery (LIB) systems require reliable techniques for the characterization of the complex dynamic processes in batteries. Electrochemical impedance spectroscopy (EIS) is an established tool for the electrochemical characterization of LIBs and the related ageing processes. Nevertheless, ensuring a reliable interpretation and validation of the impedance data remains challenging. Here we show how EIS complemented by microscopy data can be successfully used to follow battery ageing. We connect data from both methods in an electrochemical finite element model (FEM) to extract changes of intrinsic battery parameters, such as double layer capacitance, film resistance, exchange current density, and particle radii. Ageing mechanisms induced by cycling and temperature are investigated by EIS on commercial cells. Postmortem analysis of the anode and cathode electrodes is carried out to determine their initial micrometric and nanometric structure and to independently investigate morphological and electrical changes induced by ageing. While the obtained initial structural dimensions reduce drastically the number of adjustable parameters in the FEM model, which enables us to follow battery ageing processes noninvasively through the EIS spectra, the microscopy results of the aged cells prove independently the validity of the here presented approach.

Electrochemical impedance spectroscopy (EIS) is a powerful nondestructive tool that is widely used to identify physical parameters and health diagnostics in lithium-ion batteries.1 By applying a small perturbation of either current or voltage and subsequently analyzing the electrochemical responses over a range of frequencies, information can be revealed about the charge-transfer kinetics, transport phenomena, and thermodynamics of the cell.2 Small amplitude perturbations are used to ensure the battery operates in its linear-response regime. The frequency-dependent cell impedance Z(ω) is most commonly analyzed by fitting an equivalent circuit model and extracting parameters from these fits. However, equivalent circuit models limit the amount of physically insightful information that can be extracted from battery EIS data.3,4 Moreover, operating in the linear response regime also imposes a fundamental limitation on the total quantity of information contained in an impedance spectrum, since many of the physicochemical processes in a battery are inherently nonlinear in character. Nonlinear electrochemical impedance spectroscopy (NLEIS) is an adjunct to EIS where larger amplitude sinusoidal current or potential perturbations are applied, and the nonlinear response of the battery is measured by the presence of higher harmonics in the output. Through a fairly straightforward analysis, a purely frequency-dependent second harmonic “impedance” Z2(ω) can be defined. Higher harmonic, or nonlinear, electrochemical impedance spectra can provide deep insights into processes occurring in the electrochemical device under test, including insights into physicochemical processes that are invisible to standard (linearized) EIS approach.5 Here, Samsung 18650 LiNMC | C batteries (capacity = 1500 mAh) were cycled under various rates and conditions, with NLEIS data acquired throughout the duration of its cycle time, for different states of charge. For this particular battery capacity and chemistry, a typical “moderate” current perturbation was 300 to 500 mA. The experimentally measured nonlinear electrochemical impedance spectrum Z2(ω) can be compared to results derived from the psuedo-2D model of a Li-ion battery6. Our physics-based approach for computing the nonlinear electrochemical impedance is a natural extension of the linearized EIS analysis that has been done previously.4 We show that NLEIS experiments have all the information of EIS, plus additional information on the electrode thermodynamics (low frequencies) and charge transfer kinetics. We show that aggressive cycling increases the kinetic impedance of the positive electrode, with little impact on the negative electrode. NLEIS shows a correlated effect, but it is possible to use Z2(ω) to discern more about the nature of the interfacial degradation occurring on the positive electrode. In short, using nonlinear impedance in coordination with the linear impedance can provide further insight into more sensitive physicochemical phenomena that can be used to diagnose the state of health of Li-ion batteries during aging. [1] N. Lohmann, P. Weßkamp, P. Haußmann, J. Melbert, and T. Musch, J. Power Sources, 273, 613-623 (2015) [2] C. Chen and P.P. Mukherjee, Phys. Chem. Chem. Phys., 17, 9812-9827 (2015) [3] T. Osaka, D. Mukoyama, and H. Nara, J. Electrochem. Soc. 162, 2529-2537 (2015) [4] M. Doyle, J. P. Meyers, and J. Newman, J. Electrochem. Soc. 147, 99-110 (2000) [5] J.R. Wilson, D. T. Schwartz, and S. B. Adler, Electrochimica Acta, 51, 1389-1402 (2006) [6] T.F. Fuller, M. Doyle, and J. Newman, J. Electrochem. Soc. 141, 1-10 (1994)

Lithium-ion batteries (LIBs) degradation can be classified into capacity reduction and increase of the internal resistance. Accurate estimation of degradation is important to determine the economic value and the remaining useful life of the battery. This paper adopts dynamic electrochemical impedance spectroscopy (EIS) to evaluate degradation while charging the battery, without requiring additional time for the analysis. A new method is proposed to remove the artifacts arising from the time-variance of the system during the measurement. The validity of the method is confirmed by comparing it with the results obtained by static EIS measurements at equilibrium. The spectra are fitted to an equivalent circuit containing three resistances (R0, R1, R2). The method is applied to data obtained before and after aging cycles performed at 1.4C charge and 2.0C discharge rates, at 25 °C, 10 °C and −5 °C. The resistance of the films (R1), which is obtained from the semicircle at high frequencies in the EIS spectrum, has proved to be the key indicator of aging, and linearly depends on the SoC. The increase of R1 dominates among all the resistances, particularly at higher temperatures. Dynamic EIS is considered a powerful tool to diagnose LIBs in practical applications.

As the energy storages in the renewable energy system, lithium ion battery (LIB) is one of the promising storage devices because of its high energy density and high power output. Diagnosis technology of LIBs is very important to use electric devices in safety as well as development of materials. Since early times, Professor Bruno Scrosati group and our group have collaborated to develop battery systems, consequently a rechargeable all-solid lithium/polypyrrole (PPy) polymer battery was demonstrated. [1] We introduce the Li batteries and its diagnosis system while appreciate his contribution in the field of batteries. To increase the energy density of LIB, it is important to keep the capacity balance of the cathode and anode. Even if the capacity of only one electrode increased, it is not possible to increase the energy density of LIB. Such a capacity balance of electrodes is calculated as the battery on the basis of a silicon-based anode and a sulfur-based cathode. It is important as the reactive silicon anode to overcome the problem that the large volume change during charging and discharging to encourage the cyclability, thus we developed a Si-O-C composite anode, which is prepared by the reduction of silicon source and organic solvent. The Si-O-C composite anode was composed of SiOx (0<x<2) and C from organic compounds, resulting in buffering the volume change of silicon. The Si-O-C composite anode delivered high discharge capacity of ~800 mAh/g of Si even after 7000 cycles. [2] The Si-O-C composite anode was evaluated as a full cell by combining with Li2S cathode developed by Professor Bruno Scrosati group. At that time, this battery is one of the rare examples of lithium-metal-free sulfur battery. [3] As for the sulfur-based cathode, it is the key issue to overcome the problem of lithium polysulfide dissolution, which result in capacity fading and lowering of the charge-discharge efficiency. We demonstrated one of the solutions as the coating sulfur electrode with a PPy film, which is prepared by oxidative electro-polymerization.[4] The simple structure prepared by directly coating the PPy film on the sulfer cathode, enabled the suppression of polysulfide dissolution into the electrolyte, thereby inhibiting the shuttle phenomenon. Therefore, it is one of the attractive methods to work sulfur cathode, since this allows the flexibility of choosing electrolytes according to the choice of the anode. Electrochemical impedance spectroscopy (EIS) is superior method for analysis of the battery health while in use. EIS has been utilized to characterize each factor of batteries, because it enables us to analyze dynamics of each elemental step sensitively and separately without destruction of the cell. In addition, un-destructive EIS is expected to utilize for premonitory diagnosis of batteries on board in electric vehicles. In our previous study, the equivalent circuit to express each elemental step in a commercial LIB by EIS has been carefully investigated. We proposed the designed equivalent circuit, and on the equivalent circuit, the impedance responses of LIB during capacity fading were analyzed with continuous charge-discharge cycling. EIS using conventional FRA – potentiostat systems is not easy to measure the impedance of the large-scale LIB because of its low internal resistance. Moreover, FRA – potentiostat system for conventional EIS measurement could not be mounted on the vehicle. Thus, impedance measurement system is needed without using FRA – potentiostat systems. In our study, application of square wave potential for input signals of EIS was investigated in simple electrochemical reaction to verify a new technique called “Square-potential/current electrochemical impedance spectroscopy (SP-EIS, SC-EIS)” which is a method for EIS without using the FRA systems. We applied SC-EIS to evaluate a state of a commercial LIB. Introduction of SC-EIS to diagnosis technology of laminated LIBs and the LIB module was discussed. [5]

Electrochemical impedance spectroscopy (EIS) is one of the most important tools to elucidate the charge transfer and transport processes in various electrochemical systems including dye-sensitized solar cells (DSSCs). Even though there are many books and reports on EIS, it is often very difficult to explain the EIS spectra of DSSCs. Understanding EIS through calculating EIS spectra on spreadsheet can be a powerful approach as the user, without having any programming knowledge, can go through each step of calculation on a spreadsheet and get instant feedback by visualizing the calculated results or plot on the same spreadsheet. Here, a brief account of the EIS of DSSCs is given with fundamental aspects and their spreadsheet calculation. The review should help one to develop a basic understanding about EIS of DSSCs through interacting with spreadsheet.

Corrosion is a spontaneous process that causes irreversible damage to nearly all metals, which has world-wide implications for architectural and artistic metalwork, such as bridges, buildings, airplanes and sculptures. Protective coatings such as wax, paint, or polymeric clear coatings are used to prolong the lifetime of metals such as steel and bronze. Unfortunately, these coatings fail over time due to oxidative damage by UV rays and by failure to exclude water that can carry salts and pollutants that cause corrosion of the underlying substrate. The current method of coating assessment is visual inspection but when coating failure is detected at this stage, irreversible damage has already occurred to the metal substrate. Diagnosing coating quality on artistic metalwork is a unique challenge as it requires a method that is non-destructive as to not alter the aesthetic of the piece. A non-destructive technique or device that can detect early signs of coating failure in the field (such as at a sculpture park) does not currently exist. The aim of this thesis is to develop a method that can be readily used in the field by a conservator to quickly diagnose the protective state of a coating on a sculpture in order to provide localized treatment. Electrochemical impedance spectroscopy (EIS) is a method currently used to study protective coatings in the lab. The technique itself is non-destructive but the most common electrochemical cell used with it must be used on a planar substrate and requires that a portion of coating be removed. Not only is the current method destructive, but the data produced by EIS is complicated and time consuming to analyze. These issues provide the foundation for this project. This thesis first proposes multiple methods to quickly analyze the complicated data traditionally collected through EIS. Three quick analysis methods, including the estimation of the derivative at one single frequency in EIS spectra, was successfully used to categorize coating quality of five different coating types. Using this quick analysis can aid conservators in assessing coating condition without the need for extensive training in EIS data interpretation. This thesis also proposes a method to measure early warning signs of coating degradation through a co-planar hydrogel electrochemical cell paired with EIS. The configuration of this co-planar hydrogel cell negates the need for the removal of the coating and can be used on multiple types of surfaces because of its flexibility, therefore overcoming the drawbacks of

Random and systematic error as a function of sample area in electrochemical impedance spectroscopy data

Significant effort has been devoted to the research and development of safe lithium ion batteries in the last three decades. The commercial 26650 LiFePO4/Li4Ti5O12 (LFP/LTO) battery has been specifically found in many applications in which safety and cycle-life are of prime importance. The cell consists of olivine LiFePO4 as cathode, which is one of the most thermally stable, non-toxic, and long-cycle-life cathode materials, and spinel Li4Ti5O12 as anode, which features zero-strain Li intercalation, flat and relatively high operating voltage, and high thermal stability. In order to assess and improve the power capability and stability of these safe and long-life batteries, it is important to investigate and understand performance characteristics of individual cell components and interfaces, in order to elucidate the rate determining processes at each electrode. In this work, electrochemical impedance spectroscopy (EIS) analysis facilitated by distribution of relaxation times (DRT) is used to study the LFP/LTO cell at different states of charge (SOC) and SOC history, and an equivalent circuit model (ECM) is proposed in order to better understand the main processes at the anode and cathode and quantify their contribution to the full cell impedance. The modeling and interpretation of the full cell EIS is complicated because the LFP and LTO responses appear over similar frequency ranges. In order to unambiguously separate the anode and cathode contributions, EIS spectra from LTO/Li and LFP/Li half cells were also measured at different SOC and SOC history and the fit results were used to develop the full-cell ECM. The LFP/LTO cell impedance changes slightly only at low SOCs during charge, whereas it changes significantly during discharge. This SOC history dependence, i.e., the EIS response of a particular SOC depending on the previous SOC (Figure 1a), was mainly attributed to the change in the LFP electrode charge transfer process frequency and resistance (Figure 1b). This variation was found to be minimized at high SOCs and maximized at low SOCs (Figure 1), e.g., the LFP electrode charge transfer resistance in the full cell at 90% SOC was 11.3 Ωcm2 in charge vs 11.4 Ωcm2 in discharge (~1% difference) and at 10% SOC was 9.7 Ωcm2 in charge vs 13.3 Ωcm2 in discharge (~37% difference). As seen in the DRT results (Figure 1b), the frequency of the charge transfer process at the LFP electrode showed the same variation, i.e., it happens at 2.15 Hz at 90% SOC in charge vs 1.73 Hz in discharge (~20% difference) and at 1.31 Hz at 10% SOC in charge vs 0.55 Hz in discharge (~42% difference). The LTO electrode showed relatively little change in the charge transfer resistance and frequency with SOC and SOC history (Figure 1b).Figure 1. EIS response from the full 26650 cell at different SOC during charge (red lines) and discharge (back lines): a) shows the Nyquist plots and b) shows the DRT results. The spectra are shown after the truncation of the negative values of -Zimag and are normalized to the surface area of the electrodes. Figure 1

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

For the purpose of predicting the state of charge of Lithium-ion rechargeable battery accurately, we introduced the method of electrochemical impedance spectroscopy to solve the problem. The experiment data of impedance spectroscopy is comprised of an inductive arc in the high-frequency region and two capacitive arcs in the low-frequency region, and by which the reasonable equivalent circuit of battery was established. The component parameters obtained at several state of charge values of the battery had been analyzed by a non-linear least-squares fitting procedure and some electrochemical knowledge. Through researching the changing regulation of parameters with the different States of charge, the frequency of maximum of the semicircle (f max ), the phase angle o, the equivalent series capacitance (C s ) had been substantiated to be the suitable parameters for analyzing and predicting the state of charge values of the lithium-ion battery.

Lithium-ion batteries (LIBs) suffer severe performance degradation during their lifetime application because of the undesired chemical reactions, ageing, corrosion, structural integrity compromise, and the threat of thermal runaway. Among those degradation mechanisms, much attention has been focused on LIBs materials decomposition, new components formation and growth due to undesired side reactions. The electrode is covered by a passivation layer named as the solid electrolyte interface (SEI), which is one of dominant LIBs degradation mechanisms. The SEI layer affect the LIBs cyclability, life time, power and rate capability, and even their safety. Growth of the SEI reduces the lifetime of LIBs and increases cell internal resistance.Many researchers have investigated the SEI layer in term of structure, formation and compositions, thickness growth prediction and measurement. The SEI is believed to have multilayered structure, a thin layer of inorganic components close to the anode, and followed by a thick porous organic or polymeric layer close to the electrolyte phase. The composition of SEI layer can be distinguished to include Li2CO3, LiOH, LiF, Li2O, ROCO2Li and RCOLi (1). The SEI on the cross-section is dominated by Li and F and on the basal plane is dominated by organic materials. Heavy organic fragments were found mainly at the surface of the basal SEI. The thickness of the SEI on the basal plane was found to be three to five times smaller than that on the cross-section (2). Compare to the experiment, the simulation work will be more flexible. Yan et al phenomenologically modeled the formation and evolution of the SEI on the graphite electrode. Within the framework of classical nucleation theory (CNT), they qualitatively explained the origin of the two-layer structure of SEI films (3). Broussely et al. proposed their diffusion-limited SEI growth model, then tried to explain the mechanism of lithium loss for LIBs during storage and gave us the rate of lithium loss is proportional to the SEI electronic conductance(4). Kim et al. simulated the effect of electrolytes on the structure and evolution of the SEI (5).Although much work has already been done with the SEI study, the morphology of SEI layer is still not fully understood, especially in term of SEI structure prediction. In this work, we will predict the morphologies of the SEI using the phase field (6-8). The phase field model has been applied in many electrochemical studies to simulation the electrochemical reactions and to model the phenomena as roughness formation and electronically mediated reactions between interfaces in electronic contact. Also the phase field model is more accurate in regions with sharp composition gradients and can treat phase boundaries without the need to explicitly track interfaces. Phase field model can be used as a potentially powerful tool for modeling the structure of the SEI in LIBs.1. F. M. Wang, M. H. Yu, Y. J. Hsiao, Y. Tsai, B. J. Hwang, Y. Y. Wang and C. C. Wan, International Journal of Electrochemical Science, 6, 1014 (2011).2. E. Peled, D. Bar Tow, A. Merson, A. Gladkich, L. Burstein and D. Golodnitsky, Journal of Power Sources, 97–98, 52 (2001).3. J. Yan, B.-J. Xia, Y.-C. Su, X.-Z. Zhou, J. Zhang and X.-G. Zhang, Electrochimica Acta, 53, 7069 (2008).4. M. Broussely, S. Herreyre, P. Biensan, P. Kasztejna, K. Nechev and R. J. Staniewicz, Journal of Power Sources, 97–98, 13 (2001).5. S.-P. Kim, A. C. T. v. Duin and V. B. Shenoy, Journal of Power Sources, 196, 8590 (2011).6. B. C. Han, A. Van der Ven, D. Morgan and G. Ceder, Electrochimica Acta, 49, 4691 (2004).7. J. E. Guyer, W. J. Boettinger, J. A. Warren and G. B. McFadden, Physical Review E, 69, 12 (2004).8. J. E. Guyer, W. J. Boettinger, J. A. Warren and G. B. McFadden, Physical Review E, 69, 13 (2004).