A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems

Transportation sector reported almost a quarter of global CO2 emissions. Thus, efforts to decarbonize this sector are essential for achieving net zero emission goals. Among the actions to mitigate the effects of climate change in favor to decarbonization, lithium-ion electric vehicle market has expanded over the past years because of both scientific advances and encouraging public policies. The analysis of ageing in lithium-ion batteries is essential to ensure optimal performance and determine the end of the useful life for that purpose. Lithium-ion batteries degradation is a complex multi-causal process. Ageing mechanisms could be grouped mainly into three degradation modes: Loss of Conductivity (CL), Loss of Active Material (LAM) and Loss of Lithium Inventory (LLI). Ageing battery process can be evaluated as a state of health (SoH) and tracked based on capacity and power. Although SoH quantifies the battery degradation through the decrease in capacity, its definition does not include an indication of the underlying deterioration mechanisms causing the degradation. Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively with the aim of finding a correlation between their evolutions with SoH. This paper proposes a method to identify and measure the ageing mechanisms in commercial 18650 NMC lithium-ion batteries over time using the EIS technique. The EIS spectra were fitted to a proper equivalent electrical circuit and the main mechanisms responsible for the degradation identified through the calculated parameters variations with time. Thus, the increase in Rohm was assigned to CL, Rsei and Rct with LLI and Rw with LAM. Correlation between degradation modes with SoH was also reported.

Understanding the root causes of Lithium-ion battery degradation is a challenging task due to the complexity of the different mechanisms involved. For simplicity, ageing mechanisms are often grouped into three degradation modes (DMs): conductivity loss, loss of active material and loss of lithium inventory. Battery Management Systems (BMSs) do not currently include an indication of the underlying DMs causing the degradation. Pseudo Open Circuit Voltage (pOCV), Incremental Capacity - Differential Voltage (IC-DV), Electrochemical Impedance Spectroscopy and Differential Thermal Voltammetry are the most common non-invasive diagnosis techniques studied in the literature to quantify DMs. This work presents a critical and systematic review of these techniques with the focus on the elaboration of their strengths and weaknesses for the implementation in automotive applications. Firstly, each technique is classified into different groups and their working principles are presented. Secondly, an evaluation criterion is introduced to review each technique following a systematic approach. The comparison of the techniques highlight that pOCV and IC-DV are the most advantageous because they fulfill most of the points included in the evaluation criteria. The further implementation of these techniques would support battery lifetime control strategies and battery designs.

Diagnosing battery degradation is critical for understanding the state of a cell in operation to predict remaining useful life. Degradation Mode Analysis (DMA) is increasing in popularity as a tool for diagnosing the state-of-health (SOH) of a cell in more detail than capacity-based methods. By attributing degradation to loss of lithium inventory (LLI) or loss of active material (LAM) on each electrode, the gap between the diagnosed state and the underlying degradation mechanism is reduced.DMA relies on a fitting procedure, which makes the potential sources of error numerous. As a thermodynamic method, DMA works best close to equilibrium, such that it is usually performed on experiments at low currents. But the resistance increase of the cell with degradation also increases the overpotential during electrochemical measurement at a fixed current in Amps. This effect can be exacerbated by the selection of reference performance test (RPT) procedures; best practices would suggest taking pseudo-open circuit voltage (pOCV) measurements at currents lower than C/25 (1) but this may be impractical, particularly in real-world applications. Inhomogeneous distribution of degradation inside the cell is also known to suppress features in the OCV profile that are required to make a DMA fit (2). The literature provides no singular agreed method for conducting DMA. Options include fitting to the unaltered pOCV data or to its derivatives and there are multiple potential approaches to correcting for the overpotential caused by cell resistance.Previous attempts to validate DMA methods have used synthetic datasets generated from known degradation mode combinations (3,4). In reality, however, there is no ground truth from which the degradation modes predicted by these methods can be validated without tearing down a cell (5). It is therefore impossible to quantify the error in a DMA fit in-situ.This work uses a physics-based battery degradation model in PyBaMM (6) to generate a synthetic dataset under different conditions, on which the error in DMA results is quantified by comparison with the degradation modes directly predicted by the model. This approach allows the performance of DMA methods to be quantified: at different cell states of health (SOH),reached as the result of different dominant degradation mechanisms that impact cell performance beyond LLI and LAM through mechanisms such as porosity change,using different RPT procedures, andwith the effect of degradation heterogeneity captured by a distributed model (7). We use PyProBE (8), an open-source Python package for battery data processing that includes a library of post-processing methods, including methods for DMA. Multiple approaches for performing the DMA fit can therefore be run within the same environment, allowing comparisons to be made.We demonstrate that careful selection of RPT procedure and fitting technique is critical for accurate prediction of degradation modes, with failure to do so leading to erroneous results. We show how the accuracy of DMA changes as the cell degrades, which must be considered when interpreting results. Finally, we provide users of DMA in research and industry an insight into the magnitude of error in their diagnostic tools so that they can attribute informed uncertainty to their results. Dubarry M, Anseán D. Best practices for incremental capacity analysis. Frontiers in Energy Research. 2022;10. https://www.frontiersin.org/articles/10.3389/fenrg.2022.1023555Lewerenz M, Fuchs G, Becker L, Sauer DU. Irreversible calendar aging and quantification of the reversible capacity loss caused by anode overhang. Journal of Energy Storage. 2018;18: 149–159. https://doi.org/10.1016/j.est.2018.04.029.Dubarry M, Beck D. Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis. Journal of Power Sources. 2020;479: 228806. https://doi.org/10.1016/j.jpowsour.2020.228806.Birkl CR, Roberts MR, McTurk E, Bruce PG, Howey DA. Degradation diagnostics for lithium ion cells. Journal of Power Sources. 2017;341: 373–386. https://doi.org/10.1016/j.jpowsour.2016.12.011.Chen J, Marlow MN, Jiang Q, Wu B. Peak-tracking method to quantify degradation modes in lithium-ion batteries via differential voltage and incremental capacity. Journal of Energy Storage. 2022;45: 103669. https://doi.org/10.1016/j.est.2021.103669.Sulzer V, Marquis SG, Timms R, Robinson M, Chapman SJ. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software. 2021;9(1). https://doi.org/10.5334/jors.309.Li S, Kirkaldy N, Zhang C, Gopalakrishnan K, Amietszajew T, Diaz LB, et al. Optimal cell tab design and cooling strategy for cylindrical lithium-ion batteries. Journal of Power Sources. 2021;492: 229594. https://doi.org/10.1016/J.JPOWSOUR.2021.229594.Holland T, Cummins D, Marinescu M. PyProBE: Python Processing for Battery Experiments. Submitted. https://github.com/ImperialCollegeLondon/PyProBE Figure 1

Accurate estimation of degradation in lithium-ion batteries is essential in predicting remaining useful life and understanding how to better operate batteries for extended lifetimes. A common way of estimating degradation modes in lithium-ion batteries is to analyse the cells voltage profile through techniques such as incremental capacity analysis, differential voltage analysis or direct fitting of half-cell potential profiles. To extract degradation modes such as loss of lithium inventory or loss of active material requires accurate knowledge of the individual electrode half-cell potential profiles, which is often not available for commercial cells without performing a cell teardown. This work investigates how the choice of half-cell potential profile influences the accuracy of a parametric voltage profile model to estimate electrode capacity and simulated degradation modes through a combination of half-cell and three electrode testing on a LiNi0.5Mn0.3Co0.2O2/Graphite cell. Results demonstrate that whilst half-cell potential data from the same electrode material batch gives the most accurate voltage profile fit, other data sources for the same electrode chemistry can also accurately estimate individual electrode capacity in a fresh cell. However, when degradation modes are induced into the voltage profile, only the model using half-cell profiles obtained from similar sources to the full-cell configurations are able to accurately distinguish between loss of lithium inventory and loss of active material of the positive electrode. It is also shown that the diagnostic accuracy of the parametric voltage profile model can be improved for all data sources through combined fitting of the voltage, incremental capacity and differential voltage analysis compared to voltage profile fitting alone.

We present a battery lifetime model that predicts Irreversible battery thickness change under constant pressure operation as the battery ages, along with capacity loss and resistance growth. These metrics are very important for predicting lithium-ion battery state of health (SOH) and remaining useful life due to their interdependence with battery packaging and safety. To design and operate battery storage systems safely, we must be able to predict and detect changes in all three SOH metrics over life accurately and simultaneously. Traditionally, these three SOH metrics have been modeled separately. However, we developed a single model that simultaneously predicts battery capacity, resistance, and expansion over its lifetime.In this work, battery degradation is modeled using the single particle model (SPM) framework, including mechanical damage in the electrodes, which results in loss of active material (LAM), and the side reactions for SEI growth and Li plating, which result in loss of lithium inventory (LLI). We introduce an equivalent stress and concentration dependence of stress to the mechanical damage model. Commonly used fatigue models assume symmetric cycles (zero-mean stress) [1]. To account for cycling conditions with different currents on charge and discharge, an equivalent stress that adjusts for non-zero mean stress is needed. The concentration dependency of strain and the resulting stress allows us to capture different degradation rates for cells cycled at different depths of discharge (DOD). Lithium plating at the separator interface is often described as a spatially non-uniform process using the pseudo-2D (P2D) model. This phenomenon can be captured in the SPM framework using a current-dependent dynamic term to account for spatial-nonuniformity of electrolyte dynamics at higher currents.Experimental data from cells cycling with periodic reference performance tests were used to tune the degradation model. In addition to the standard I, V, and T measurements, continuous measurements of the thickness change of the battery pouch cells were recorded. From this data, we extracted the capacity, electrode state of health (eSOH), resistance, and reversible and irreversible expansion. The test conditions were designed to excite different dominant degradation mechanisms (e.g. mechanical damage) by cycling the cells at different conditions (C-rate, temperature, preload pressure, and DOD). The tuning, which uses accelerated aging simulations [2] (to speed up the tuning process), results in a single set of parameters that predicts capacity loss, resistance growth, and irreversible thickness change as the battery ages, even for conditions that were not used for parameter tuning.The tuning is performed in two sequential steps. First, the parameters related to the electrochemical and mechanical degradation rates, which result in LLI and LAM, are tuned to match the extracted eSOH variables at each Reference Performance Test (RPT). The first tuning gives us the amount of plated Li, SEI growth, and mechanical damage in the particles. We then relate these quantities to irreversible expansion using a linear relationship in the case of SEI and mechanical damage and quadratic in the case of Li plating [3]. The same relationship is used for all cells, and this represents the 2nd tuning step. Our data set and calibration are unique in that the dominant degradation mode is the loss of lithium inventory due to the loss of active anode material. Most prior work on lifetime predictive models relied on SEI growth as the dominant degradation mode. Figure 2 shows the tuned model prediction of the three performance metrics over the cycle life of cells for various cycling conditions. The markers represent the measurement at each reference performance test, and the lines correspond to the model.

Li-ion batteries degrade over time. Traditionally, capacity and resistance of the cell have been used as the state of health (SOH) indicators. However, these parameters cannot provide detail information about the degradation mechanisms. Thus, recently, there have been many efforts on developing diagnostics algorithm that can identify the degradation mechanisms. There exist many degradation mechanisms for Li-ion batteries such as SEI layer growth or lithium plating consuming lithium, structure disordering and metal ion dissolution making active material unavailable for insertion and extraction of lithium. Identifying these mechanisms is challenging due to complex inter-correlations between them. Instead, it is possible to categorize these mechanisms into two degradation modes: loss of lithium inventory (LLI) and loss of active material (LAM) at each electrode. On the electrode level, capacity and utilization window of the individual electrode are proposed as electrode-specific SOH parameters that can be related to the degradation modes. The differential voltage analysis (DVA) is one of the common approaches for estimating these electrode parameters. In this method, the phase transition of the electrode material is used as a fingerprint of each electrode in the cell’s differential voltage (dV/dQ) curve using a low C-rate constant current pseudo-OCV data [1,2]. Another interesting type of battery response is the mechanical response measured as a cell expansion. Batteries expand during charge and contract while discharge in repeatable patterns. Hence, similar to the voltage analysis, the expansion of a cell can be used for identifying the electrode parameters [3]. In this study, electrode-specific parameters (electrode capacity and utilization window) were estimated for a 5 Ah graphite/NMC pouch cell (University of Michigan Battery Lab) during accelerated aging cycle testing at the elevated temperature. A fixture was designed in order to measure the mechanical response of the cell as shown in Fig. 1(e) such that the top and bottom plates were fixed in place while the middle plate was free moving. The expansion was measured using a displacement sensor (Keyence) mounted on the top plate, and a battery cycler (Biologic BCS-815) was used for measuring the voltage. The cell was placed inside a climate chamber at 45˚C. The aging cycle consists of 2C constant current charge and discharge cycles. Diagnostic tests were conducted at intervals corresponding to an expected 5% loss in capacity for a cell at room temperature. The cell was cycled at C/20 to get the pseudo-OCV. Furthermore, in order to measure the potential of graphite and NMC electrodes, coin cells were built. The Li/NMC coin cell was cycled at C/50 between 2.8 and 4.35 V. The Li/graphite coin cell was cycled at C/50 between 0.005 and 1.0 V. The lattice expansion data was taken from literature for graphite and NMC. The terminal voltage, differential voltage, and expansion data of the cell is shown in Fig.1(a), (b), and (d). A model is developed based on [3] to estimate the electrode-specific SOH parameters as the cell ages and to quantify the degradation modes (i.e., LLI and LAM at each electrode). The cell OCV and expansion models share the same electrode SOH parameters and the estimation is done by finding the best fit for the voltage, differential voltage, and expansion curves. The results show a very good match between the data and model for all three different data curves. The estimated parameters then are provided to the equations describing the degradation modes for quantification (see Fig.1(c)). It is found that LAMNE and LLI are the main degradation modes for this chemistry of NMC/graphite cell under the accelerated aging cycling at the elevated temperature accounting for the cell capacity fade (SOH in Fig. 1(c)). Lastly, it was found that cell expansion measurement gives much higher confidence for estimating electrode SOH parameters [3]. Since the differential voltage is a derivative of the measured voltage, which is sensitive to the measurement noise and can lead to incorrect estimation. Furthermore, the addition of expansion makes this electrode SOH parameter estimation feasible when using a partial range of OCV data. This is important for automotive applications where battery packs rarely discharge fully. [1] Suhak Lee et al., "Comparison of Individual-Electrode State of Health Estimation Methods for Lithium Ion Battery," ASME 2018 Dynamic Systems and Control Conference, 2018. [2] Hannah M. Dahn et al., "User-friendly differential voltage analysis freeware for the analysis of degradation mechanisms in Li-ion batteries," Journal of The Electrochemical Society 159, 2012. [3] Peyman Mohtat et al., “Towards better estimability of electrode-specific state of health: Decoding the cell expansion,” Journal of Power Sources 427, 2019. Figure 1

Electrochemical 3-D morphology based degradation quantification framework for lithium-ion battery under low temperature

Investigation of inhomogeneous degradation in large-format lithium-ion batteries

Accurate state of health (SoH) estimation is crucial to optimize the lifetime of Li-ion cells while ensuring safety during operations. This work introduces a methodology to track Li-ion cells degradation and estimate SoH based on electrochemical impedance spectroscopy (EIS) measurements. Distribution of relaxation times (DRT) were exploited to derive indicators linked to the so-called degradation modes (DMs), which group the different aging mechanisms. The combination of these indicators was used to model the aging progression over the whole lifetime (both in the “pre-knee” and “after-knee” regions), enabling a physics-based SoH estimation. The methodology was applied to commercial cylindrical cells (NMC811|Graphite SiOx). The results showed that loss of lithium inventory (LLI) is the main driving factor for cell degradation, followed by loss of cathode active material (LAMC). SoH estimation was achievable with a mean absolute error lower than 0.75% for SoH values higher than 85% and lower than 3.70% SoH values between 85% and 80% (end of life). The analyses of the results will allow for guidelines to be defined to replicate the presented methodology, characterize new Li-ion cell types, and perform onboard SoH estimation in battery management system (BMS) solutions.

Diagnostic information of a battery allows for its maximum utilization while avoiding unfavorable or even dangerous operations. Model-based approaches have been proposed to identify the state of health (SOH) related parameters in lithium-ion (Li-ion) batteries; however, high computational cost for solving optimization-based parameter identification makes these approaches difficult to be implemented in onboard applications. To address this issue, this paper proposes a machine learning-based approach using a neural network (NN) model for identifying electrode-level degradation of Li-ion batteries. For the diagnosis of electrode-level degradation (i.e., loss of active material (LAM) for each electrode and loss of lithium inventory (LLI)), electrochemical features are extracted from both incremental capacity (IC) curve and differential voltage (DV) curve. The developed NN model trained with the proposed electrochemical features shows strong potential in identifying each degradation mode accurately: the RMSE of all degradation modes is less than 0.1.

Al2O3 protective coating on silicon thin film electrodes and its effect on the aging mechanisms of lithium metal and lithium ion cells

The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.

Establishing a framework for understanding the degradation mechanisms of lithium-ion batteries (LIBs) subjected to abusive conditions has become increasingly important in recent years, as LIBs continue to capture new markets involving an array of abuse-prone applications such as electric vehicles, aircraft, and mobile energy storage systems. Characterization techniques used to identify the state-of-health (SoH) of LIBs can identify a range of degradation mechanisms, which can be broadly categorized as loss of lithium inventory (LLI), loss of active material (LAM), and conductivity loss (CL). These degradation mechanisms, which have been well characterized in literature, are established using a variety of in-situ and ex-situ techniques. The most common in-situ technique for determining cell SoH involves monitoring temperature and voltage as a function of state of charge (SOC) throughout cell cycling. For abusive conditions, however, this measurement is a particularly poor indicator of battery SoH and does not provide valuable insight into cell degradation until failure is imminent. It is particularly difficult to determine SoH for LIBs, as the voltage curve maintains a relatively uniform profile until moments before cell failure. A more versatile in-situ technique capable of monitoring battery degradation mechanisms is Electrochemical Impedance Spectroscopy (EIS), which quantifies cell SoH by measuring system response to a range of sinusoidal excitation signals at varying frequencies. Although extensive characterization of battery degradation has been conducted using traditional EIS hardware, no research has been conducted on impedance analysis of active load cells subjected to abusive conditions. This work attempts to identify failure markers by linking impedance measurements captured using the EIS toolbox to previously established degradation mechanisms for abused cells. In-situ EIS measurements were collected, followed by identification of failure markers using ex-situ material characterization of harvested electrodes. Failure markers were established by linking degradation behavior identified by material characterization methods to changes in impedance behavior observed by the toolbox. This data can be extrapolated for use with general LIB chemistries in a wide range of BMS’s. Although data extrapolation is outside the scope of this work, data obtained will help facilitate future extrapolation of failure markers to a wide range of LIB chemistries. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Electrochemical Impedance Spectroscopy (EIS) is a powerful diagnostic technique for probing internal electrochemical processes and assessing lithium-ion battery degradation, yet its practical application is often limited by the complexity and subjectivity of manual fitting procedures. This paper proposes a fully automated algorithm for extracting equivalent circuit model (ECM) parameters from EIS measurements to quantify the battery degradation modes, namely conductivity loss (CL), loss of active material (LAM), and loss of lithium inventory (LLI). The developed method integrates an automated partitioning of the measured impedance spectrum into three frequency regions using optimized percentile thresholds. This segmentation enables an initial estimation of circuit parameters describing lithium-ion cell behaviour, including ohmic resistance, charge-transfer resistances, non-ideal capacitances, and Warburg diffusion elements. These initial estimates are subsequently refined through an iterative, sequential optimization process based on a Trust Region Reflective least-squares algorithm, using a reference spectrum and propagating the optimized parameters across successive aging cycles. The algorithm was validated using two experimental datasets comprising various cell types and impedance magnitudes ranging from micro-ohms to a few ohms. The proposed approach minimizes operator intervention and provides a reliable and scalable tool for battery health monitoring, suitable for both real-life diagnostics and research activities involving large volumes of experimental data. • Automatized algorithm for multiple Electrochemical Impedance Spectroscopy fitting • Optimization of initial parameters set once, then automatically selected by code. • Practical applications are provided with validation on two datasets. • Equivalent Circuit Model theory is applied for aging mechanisms quantifications. • Industrial potential via improvements in battery management and aging predictions

In this paper, reversible capacity loss of lithium-ion batteries that cycled with different discharge profiles (0.5, 1, and 2 C) is investigated at low temperature (−10°C). The results show that the capacity and power degradation is more severe under the condition of low discharge rate, not the widely accepted high discharge rate. To shed some light on the aging phenomena, noninvasive electrochemical methods, ie, incremental capacity and differential voltage analysis, are applied to identify and quantify the effects of different degradation modes (DMs). Apart from the resistance increase, the DMs include the loss of lithium inventory (LLI) and the loss of active material (LAM). Both LLI and LAM decay to a greater extent for the cell cycled with lower discharge rate, and the growth of LAM is higher than that of LLI. Further, the analysis of state of charge (SOC) window shows that the earlier cutoff of the high discharge rate can lead to less mechanical and thermal stress on cathode materials, thus a lower degradation rate. Another cause is that the lithium plating on the anode materials can be mitigated by increasing the charging temperature which results from preceding high rate discharging.