Incremental Capacity Analysis Research Articles

Online two-dimensional filter for anti-interference aging features extraction to accurately predict the battery health

Reliable and precise extraction of features plays a critical role in predicting the State of Health (SOH) of lithium batteries. Nevertheless, noise-tainted measurement signal can cause the distortion of differential curves, consequently compromising the accuracy of feature extraction. This paper develops an online two-dimensional filtering framework by fusing the Adaptive Taylor Kalman filter (ATKF) and the Improved Gaussian filter (IGF), applying it to both incremental capacity analysis and differential thermal voltammetry methods. Firstly, the Taylor series is harnessed to construct a state model for the noise-affected measurement signal. Then, an adaptive algorithm is introduced to diminish the dependence on human judgment when configuring variances. Finally, the IGF filters the curve from the two-dimensions of time and period. Experimental results demonstrate that the two-dimensional filtering method exhibits robust noise mitigation capabilities, enhancing the stability of feature extraction while maintaining system causality. Furthermore, this method has strong robustness to battery inconsistency and temperature uncertainty. When applying the proposed filter to extract aging features from battery data provided by CALCE and Oxford datasets, the correlation coefficient between the extracted aging features and SOH increases by 34.75% and 10.71%, respectively. Additionally, the RMSE of the SOH prediction results decreases by 60.57% and 59.46%, respectively.

A Lithium-Ion Battery Remaining Useful Life Prediction Method with A New Algorithm Based on Incremental Capacity Analysis

Safe use of lithium-ion batteries requires accurately assessing state of charge (SoC), state of health (SOH) and capacity estimation techniques. Due to numerous charge and discharge cycles, lithium ion batteries undergo a degradation process during their use leading to failures, accidents, and fire. Traditional ICA/DVA methods have been used to overcome these issues, but they are subject to changes in battery resistance and polarization processes during battery aging. Evaluation of the SoC as a function of incremental capacity is proposed in this work to overcome this problem. This article used a new algorithm to perform, through simulations carried out with Matlab® software, incremental capacity analysis for a preventive estimate of remaining useful life (RUL). In addition, the comparison between IC curves and the SoC here used fully represents the relationship between the IC values and the internal parameters of the battery. The validity of the proposed algorithm against the phenomenon of battery aging was evaluated based on experimental data from NASA's PCoE research center.

Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

Prediction of Health Level of Multiform Lithium Sulfur Batteries Based on Incremental Capacity Analysis and an Improved LSTM

Prediction of Health Level of Multiform Lithium Sulfur Batteries Based on Incremental Capacity Analysis and an Improved LSTM

Online state of health estimation for lithium-ion batteries based on gene expression programming

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness.

Electrophoretic deposition of graphene oxide as a stabilizing layer on the Ni-rich layered oxide electrodes for enhancing the cycling stability of Li-ion cathodes

One of the critical challenges for layered Ni-rich cathode material is the surface degradation caused by the parasitic reaction between the cathode material and the organic electrolytes. Herein, we develop an overall electrode modification strategy to improve the cathode surface's structural stability by integrating graphene oxide (GO) coating on the surface of the cathode electrode with the assistance of the electrophoretic deposition technique (EPD). The attenuated total reflectance (ATR) and scanning electron microscope (SEM) results confirm the formation of the uniform thin GO layer on the surface of the Ni-rich cathode. The electrochemical studies such as galvanostatic charge-discharge, galvanostatic intermittent titration Technique (GITT), and incremental capacity analysis (ICA) tests show that the existence of thin layers of GO not only does not hinder the intercalation of Li+ ions but also can facilitate the charge transfer and intercalation of Li+ ions by decreasing the charge transfer resistance of the electrode. The cycling stability of the cathode coated with GO in 30 s of EPD shows better performance over 150 charge-discharge cycles and lower Rct than the pristine electrode. Our work provides a design idea for stabilizing the surface/bulk structure of advanced Ni-rich cathode for high-performance Li-ion batteries.

Secondary Life of Electric Vehicle Batteries: Degradation, State of Health Estimation Using Incremental Capacity Analysis, Applications and Challenges

Secondary Life of Electric Vehicle Batteries: Degradation, State of Health Estimation Using Incremental Capacity Analysis, Applications and Challenges

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs.

Unraveling the Interplay between Degradation Mechanisms during Battery Cycling Conditions: Influence on SEI Growth

Although the solid electrolyte interphase (SEI) is integral for the operation and stability of Li-ion batteries, its continuous growth over the battery life leads to in loss of cyclable material and overall degradation which results in capacity face. Therefore, a proper understanding of the mechanisms and factors involved in the SEI evolution during battery operation is integral for both reliable ageing models, allowing proper prognosis of the state of health (SoH), and improvement of battery lifetime.Generally, studies of SEI growth have focused on calendar ageing experiments, in which the battery is left undisturbed for long periods of time and periodic check-up cycles are performed to estimate the capacity loss over time. Despite significant advances in the understanding of SEI formation with this kind of experiments, the specific limiting mechanism for SEI growth is still under discussion, with electron or Li-interstitial diffusion through the SEI being the main candidates in recent studies. Calendar ageing provides a suitable framework to study SEI growth in almost isolation from other degradation mechanisms but neglects the synergistic effects that can occur between them in real operation conditions, in which the SEI growth will be influenced by other degradation processes. For example, it has been reported that lithium plating can favor SEI formation and that new SEI will growth in exposed active material through cracks in the particles or in the existing SEI. Although initial efforts towards accounting for these synergies are already present in the literature, they have been applied to sparse sets of data and particularized for single chemistries.In this work we focus on the study SEI growth during cell operation and its interplay with other degradation mechanisms in the cell in different regimes to partially isolate degradation processes (e.g., varying cut-off voltages to favor lithium plating). To this end, we carry out electrochemical cycling tests with LNO/Gr and NMC811/Gr full coin cells and also assemble 3-electrode cells to differentiate between positive and negative electrode contributions. Moreover, the individual contributions from the different degradation mechanisms are assessed by combining EIS measurements, incremental capacity analysis and morphological characterization.The results of the characterization are then used to parametrize the implementation of degradation models (SEI, particle cracking, lithium plating, loss of active material...), with both semi-empirical and physics-based approaches, within a Doyle-Fuller-Newman pseudo-2D framework. The results of the model are then compared with the full coin cells experiments and used to evaluate the degradation synergies and provide best-practice approaches to balance battery performance and lifetime. Moreover, the flexibility of the model with degradation is tested using larger publicly available and literature datasets for different chemistries to assess its suitability in cases for which the complete model parameterization is not available (as it is often the case in commercial cells).

Calendar Aging of Lithium-Ion Batteries: Investigating the Influence of Electrolyte Additives in Open-Circuit and Floating State-of-Charge Conditions

Battery electrolytes play an important role in battery performance and safety. To improve the formation of a solid-electrolyte interface (SEI) and overall performance, additives such as vinylene carbonate (VC) and fluoroethylene carbonate (FEC) are added to the electrolyte. A thorough understanding of the aging process is critical to the safe operation of batteries. The reactions of the liquid electrolyte, its additives and the associated gas formation play a decisive role in cell aging and cell safety. Thus they are in the focus of the BatgasMod project (Modelling of battery gassing) funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the competence cluster Battery usage concepts (BattUse). The aim of the BatgasMod project is the modeling of the gassing behavior of batteries over their lifespan and the combination of electrolyte aging models with battery models for an early prediction of cell behavior in the usage phase.In order to support the modeling with aging data, three different cell variants were produced, differing in terms of their electrolyte composition. The cells were assembled at the MEET by combining commercial LiNi0.6Mn0.2Co0.2O2 (NMC622) cathode and graphite anode material together with a ceramic coated separator. The electrolyte for all three cell types is a mixture of the organic solvents EC and EMC in a ratio of 3:7 with 1M LiPF6. This composition without additives represents the reference cell. To investigate the influence of the additives, cells were prepared with either5 wt.-% vinylene carbonate (VC) or 5 wt.-% fluoroethylene carbonate (FEC) as an additive.First, both at IAM-AWP and ISEA cell formation was performed to study the entire life of the cells, including the critical step of SEI formation in the first cycles. After degassing, the cells were subjected to 10 additional cycles to obtain a stable capacity for the start of the aging tests. The cells were stored in temperature chambers at 25°C with SOCs of 100%, 80%, 30% and 5%. At IAM-AWP measurements were performed under open circuit conditions to allow for self-discharge. For comparison, at ISEA the cells were aged under floating SOC conditions, i.e. the SOCs were kept constant throughout the entire measurement. At predetermined time intervals, the cells were checked for remaining capacity and pulse resistance. In both measurements the influence of the SOC is clearly visible and the high SOC leads to an increased aging. The electrolyte additives showed different results for the different aging tests. The cells without additives outperform the cells with additives under open circuit conditions. This unusual behavior was further investigated and was attributed to the high self-discharge of the cells without additives. As a result, the voltage drop leads to a lower storage SOC, which was beneficial for the aging of these cells. The electrolyte additives showed deviating results for the different aging tests. The floating SOC measurements revealed a superior performance of the cells with additives. In addition, it was found that cells containing VC have lower capacity loss but higher resistance increase than cells containing FEC. Differential voltage analysis and incremental capacity analysis were performed to better understand the dominant aging mechanisms. These methods suggest that a combination of loss of lithium inventory and impedance rise causes cell degradation.These results provide guidance for improving calendar aging by selecting the most appropriate electrolyte additive. Future work will focus on comparing the influence of additives on the safety of fresh and calendar aged cells.

Exploring Operando Electrochemical Techniques to Track Degradation Mechanisms in Li-Ion Batteries

The battery field has recently received unprecedented attention and investments in order to handle the upcoming demands of energy storage notably from the electric mobility sector. The pursuit of higher performance batteries that store more energy, charge faster and have a longer life cycle has been converted into studies for new electrode materials, coatings, electrolytes, among others that often find the common key challenge of the performance decline. The precise identification of the degradation mechanisms and their tracking along aging is thus essential to pave the way for the next generation batteries. For that several methods are employed although most of them are not in operando, not following the practical cell operation. In that context, this paper introduces a set of operando electrochemical techniques that are still largely underexploited but that can be coupled and offer broader comprehension of the degradation mechanisms through deeper than usual analysis of electrochemical data. Taking insertion cells (like Li ion or Na ion batteries) as example it is first shown how differential techniques like incremental capacity analysis (ICA or dQ/dV vs V) and differential voltage analysis (DVA or dV/dQ vs Q) allow to identify main degradation mechanisms in the positive and in the negative electrodes separately by following their active material masses and their curves dynamics (relative displacement of the potential x capacity curves, the slippage) in a two electrodes configuration. For that the in house and free access developed software Electrochemical Visualization Application (EVA) is employed. In addition, using defined protocols, other techniques as Distribution of Relaxation Times (DRT) are coupled to those first analyses in order to complement the understanding of the degradation mechanisms in each electrode. This presentation intends to show an introduction of how to deeper analyze electrochemical techniques to follow loss of mass and lithium ion inventory from each electrode along aging, how to use the DRT with the ICA and DVA to identify the evolution of Impedance from each electrode along aging and how to build protocols that allow to couple those techniques minimizing any eventual degradation interference from those techniques, that all shown through experimental results and with the aid of a free and open software for the part of the ICA and DVA analyses. Figure 1

Degradation Diagnostics of Ni-Rich NMC in Lithium-Ion Batteries Aged in Different Degrading Conditions

Ni-rich Nickel Manganese Cobalt Oxides (NMC) such as LiNi0.6Mn0.2Co0.2O2 (NMC622) and LiNi0.8Mn0.1Co0.1O2 (NMC811) have been widely used as the cathode active materials in lithium-ion batteries (LIB) of electric vehicles due to their high capacity. Thus, we can foresee millions of tons of end-of-life LIB will be accumulated by the next decades. This means that the efficient recycling method of batteries material, especially NMC which has the highest value, should be developed to reduce the cost and increase the efficiency of recycling. Direct recycling which is a novel method to recover cathode active materials in lithium-ion batteries by directly regenerating the chemical and physical structure of cathode materials, skipping the usage of harsh chemical and high energy consumption such as those of other recycling methods i.e. pyrometallurgy and hydrometallurgy. Such an attempt to directly reprocess the spent NMC back into the as good as new cathode requires and understanding what has happened with the materials through the battery life. This implies that the understanding of degradation mechanisms of Ni-rich NMC in different usage conditions is required to up-scale the direct recycling of Ni-rich NMC from degraded lithium-ion batteries.In this work, we investigated failure mode in controlled aged samples of LIB cells to understand the degradation mechanism of NMC622 and NMC811 which are to be connected with the method for direct regeneration. Lithium-ion batteries composed of NMC622 or NMC811 cathode active materials were aged through 1C-rate charge/discharge cycling at 25°C, 45°C or 55°C until 20% or 40% capacity fading. The degraded cells were analyzed via incremental capacity analysis (ICA), volume change measurement, electrochemical impedance spectroscopy (EIS). Ex-situ X-ray powder diffraction (XRD), Scanning electron microscopy analysis (SEM), X-ray photoelectron spectroscopy (XPS) and Inductively Coupled Plasma Optical Emission spectroscopy (ICP-OES) were used to characterize the chemical and physical structure of degraded NMC from each different degrading conditions.Inspection of degraded cells shows that NMC811 cells exhibit larger cell expansion compared to NMC622 cells going through the same degrading condition. EIS results show that the solution resistance (R S) does not vary much between the different cells, while solid-electrolyte interphase resistances (R SEI) were found to increase with temperature at approximately similar level for both NMC622 and NMC811. Fig. 1a and Fig. 1b show the contour plot on the variation of charge transfer resistance (R CT) ratio after and before degradation of NMC622 and NMC811 cells. It can be seen that R CT in NMC811 cells were found to increase for the more highly cycled NMC811 coupled with high temperature. However, temperature does not seem to have much role in the increment of R CT in NMC622. Investigating the change in structure of the cycled NMCs at different temperatures and to level of different degradation, it was found that the results show that at the same level of capacity remaining, NMC622 has a much larger change in c/a ratio after vs. before compared to NMC811.The above impedance analysis coupled with the XRD results imply that the main cause of capacity fade of NMC811 cells is likely due to electrolyte decomposition which generates gas, resulting in high degree of cell expansion, causing loss of electrical contact. The electrolyte decomposition in NMC811 cell is further facilitated by the increment of temperature due to the lower onset potential limit for gas evolution in NMC811, causing more capacity fading in NMC811 cell at high temperature [1]. On the other hand, the capacity fading in NMC622 cells is mainly from the chemical/structural change in the cycled NMC622 cathode.These insight information on the degradation mechanism of LIB cells and structural variation of Ni-rich NMC after ageing in each degrading condition is then further inspected to elucidate the method to effectively directly regenerate both NMC622 and NMC811 cathode in the spent Li-ion batteries.

Diagnostics of Safety Hazardous Ageing Conditions for Commercial Lithium-Ion Pouch Cells

It is well-known that Lithium-ion cells can have safety issues. A large number of battery fire incidents have been reported the last years within e.g. battery energy storage systems [1], electric and hybrid-electric ships [2], electric vehicles and mobile phones [3]. The causes for these incidents are numerous and can include internal shortage of the Li-ion cell, external shortage of the battery system, over-heating or over-charging. All these causes result in overheating which can lead to an eventual thermal runaway which again could lead to a severe fire and explosion. We report on changes in thermal stability due to different ageing mechanisms in abstract Lian (#176439) and in Lian [4]. A decrease in thermal stability due to battery ageing could be hazardous and might be an end-of-life criteria. It is thus of vital importance to develop diagnostic tools capable of detecting a potentially hazardous drop in thermal stability for aged lithium-ion cells.This study is based on results from a battery life study performed on a large renowned Li-ion pouch cell with an initial capacity of 64 Ah. More than 100 cells were tested following a selected test matrix including different temperatures, currents, and State-of-Charge windows. All cells were tested until they reached a remaining capacity of between 60 to 80 % State-of-Health. A large dataset with diagnostic data was recorded during ageing, including e.g., dQ/dV (incremental capacity analysis) and DC resistances. Thermal properties of selected new and aged cells were studied in an Accelerated Rate Calorimeter (ARC), see Lian (abstract #176439). A post-mortem study of selected new and aged cells was performed to relate the collected diagnostics data to different ageing paths and safety properties. Based on detailed cell characterization, an experimental parameter set for an electrochemical pseudo-two-dimensional (P2D) model was established.Based on the dQ/dV (ICA) analysis, a distinct change in diagnostic patterns was observed for cells aged and cycled at low temperature (5-15 °C) compared to cells cycled at higher temperatures (35 -45 °C). This correlates to the observed differences in thermal stability reported by Lian (abstract #176439). To verify the observed ageing mechanisms from the post-mortem studies, emulated ageing modelling was performed both based on the Alawa tool [5], as well as the P2D model coupled with different ageing mechanisms. References McKinnon, M.B., S. DeCrance, and S. Kerber, Four Firefigheters Injured In Lithium-Ion Battery Energy Storage System Explosion - Arizona, U. Laboratories, Editor. 2020, Underwriters Laboratories: Columbia, MD. p. 66.Norwegian Maritime Authority, Supporting preliminary findings after battery incident. 2019: Norwegian Maritime Authority.BBC, Samsung confirms battery faults as cause of Note 7 fires, in BBC. 2017, BBC.Lian, T., et al., (Invited) Changes in Thermal Stability of Cyclic Aged Commercial Lithium-Ion Cells. ECS Transactions, 2019. 89(1): p. 73-81.Dubarry, M., et al., State of health battery estimator enabling degradation diagnosis: Model and algorithm description. Journal of Power Sources, 2017. 360: p. 59-69.

Using a Reference Electrode inside Li-Ion Cell As an Operando Sensor to Detect Aging Mechanisms

Lithium-ion batteries (LIBs) are a key technology in the massive deployment of EVs. Besides enhancing the capabilities and performances of the batteries, we want to accelerate their charging rate to increase their driving range. However, special attention must be paid to safety and thus to understanding the aging mechanisms inside the cells. Evaluating the origin of capacity decay that is linked to loss of cyclable lithium (LLI: Loss of Lithium Inventory) or active materials (LAM: Loss of Active Material) can be obtained by two different methodologies but both have certain limitations: Non-invasive [1], of which one of the most popular is Incremental Capacity Analysis (ICA). ICA operates by slipping or contracting the electrochemical profiles of the electrodes to fit the experimental curve of the cell obtained at low current. This method can provide contradictory information if not well used [2].Post Mortem Analysis (PMA) [3], LLI and LAM are evaluated after sampling of disks of electrodes retrieved by dismantling the cell at discharged state and re-assembled in half coin-cells versus lithium. However, coin cells are assembled by introducing fresh electrolyte and separator that can be not representative of the real state of impregnation of the electrodes and separator in the cell after cycling. Moreover, sampling must be performed on different areas where heterogeneities are observed (gas bubble, lithium deposition) to be representative of the global behavior of the electrode considering their respective surface [4,5]. Embedded sensors inside the batteries are a major asset to monitor the internal parameters and prevent failures. The implementation of a reference electrode [6], as an electrochemical sensor, provides interesting results in terms of operando degradation detection since it allows the potential of the positive and negative electrodes to be monitored individually. The objective of our study was to assess the efficiency of the reference electrode, as a sensor, to quantify the aging mechanisms occurring inside the cell and detect lithium plating onset. The reference electrode could be used to modulate the current and reduce the overpotential of the negative electrode avoiding lithium metal being plated. Such sensor implementation is in the scope of several European projects including the INSTABAT project [7]. We will try to demonstrate in the presentation that the reference electrode can be an additional tool for lithium-ion cell diagnosis and cell management. For that, we will base our demonstration on cycling tests performed on instrumented NMC/Graphite pouch cells. The cycling protocol is either formulated as a worldwide harmonized light vehicle test cycle (WLTC) for discharging and 1C for charging and or at CC-CV/CC in charge/discharge and at different temperatures. A correlation with post-mortem analyses will give answer about the consistency of the response in potential given by the reference electrode and for example if it is able to detect the formation of localized metallic lithium.Figure 1 : (left) Electrode balancing during a cell checkup allowing precise monitoring of the cell degradation using individual electrode potentials. (right) Understanding the conditions of the appearance of lithium plating when increasing the charging rate.

Novel, in situ, electrochemical methodology for determining lead-acid battery positive active material decay during life cycle testing

Understanding the thermodynamic and kinetic aspects of lead-acid battery structural and electrochemical changes during cycling through in-situ techniques is of the utmost importance for increasing the performance and life of these batteries in real-world applications. Here, we describe the application of Incremental Capacity Analysis and Differential Voltage techniques, which are used frequently in the field of lithium-ion batteries, to lead-acid battery chemistries for the first time. These analyses permit structural data to be retrieved from simple electrical tests that infers directly the state of health of the positive active material. We present evidence from full-scale 12 V batteries to demonstrate the techniques and prove their utility to the space. Evaluation of capacity and cycle life testing supports a new theory of Positive Active Material utilization, the Single-Entity/Dual Behaviour-Hypothesis, which is described within our work.

Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization

Battery research depends upon up-to-date information on the cell characteristics found in current electric vehicles, which is exacerbated by the deployment of novel formats and architectures. This necessitates open access to cell characterization data. Therefore, this study examines the architecture and performance of first-generation Tesla 4680 cells in detail, both by electrical characterization and thermal investigations at cell-level and by disassembling one cell down to the material level including a three-electrode analysis. The cell teardown reveals the complex cell architecture with electrode disks of hexagonal symmetry as well as an electrode winding consisting of a double-sided and homogeneously coated cathode and anode, two separators and no mandrel. A solvent-free anode fabrication and coating process can be derived. Energy-dispersive X-ray spectroscopy as well as differential voltage, incremental capacity and three-electrode analysis confirm a NMC811 cathode and a pure graphite anode without silicon. On cell-level, energy densities of 622.4 Wh/L and 232.5 Wh/kg were determined while characteristic state-of-charge dependencies regarding resistance and impedance behavior are revealed using hybrid pulse power characterization and electrochemical impedance spectroscopy. A comparatively high surface temperature of ∼70 °C is observed when charging at 2C without active cooling. All measurement data of this characterization study are provided as open source.

The Influence of Testing Conditions on State of Health Estimations of Electric Vehicle Lithium-Ion Batteries Using an Incremental Capacity Analysis

The increasing growth of the second-hand electric vehicle market demands reliable methods for evaluating the state of health of deployed electric vehicle batteries. Among these methods, incremental capacity analysis is a commonly used technique for state of health evaluation via the quantification of degradation modes. While the optimal conditions for its application typically involve low currents and a controlled temperature, this cannot be easily applied to deployed batteries. It is therefore essential to understand the impact of varying charging rates and temperatures on the accuracy of the analysis. In this study, the characteristics and behavior of incremental capacity features for seven electric vehicle batteries tested under different calendar aging conditions were investigated. The results show that accurate state of health estimations under different test conditions could be obtained using specific electrochemical features.

The Influence of Electrolyte Volume on Calendaric Aging of Lithium‐Ion Batteries

Lithium‐ion cells with graphite anodes and nickel–manganese–cobalt oxide (NMC622) cathodes are filled with three different amounts of electrolyte. During formation, incremental capacity analysis indicates small differences in the formation processes between cells with insufficient electrolytes to fill all pores and those with an electrolyte volume above the total pore volume of the cell. Complimentary analysis of the gases developed during formation shows that the composition of these cells differs from the cells with a sufficient electrolyte. The aging of cells under high temperature (60 °C) and high constant voltage of 4.2 V is studied. During aging, cells with higher amounts of electrolyte degrade substantially slower. Based on data available from electrical tests, a theory explaining the volume‐dependent rise of resistance and capacity decay is proposed.

Predicting the discharge capacity of a lithium-ion battery after nail puncture using a Gaussian process regression with incremental capacity analysis

This work uses a Gaussian process regression to predict the discharge capacity of small Lithium-ion pouch cells after a nail puncture. Previous studies have shown that cells can operate at a reduced capacity after experiencing abuse similar to what can be seen during extreme field operation, where the ability to predict cell functionality can be critical to safety. Other studies have shown that different features of cell incremental capacity curves can be used to determine the extent of cell degradation during normal operation, which can be used to predict future operation. For this work, 15 cells are punctured with a nail and allowed to continue operating for 100 total cycles to collect data. The incremental capacity curves are calculated, then the magnitude and corresponding voltage of the highest peak are determined. A Gaussian process regression is used to predict the discharge capacity during operation after the nail punctures. The results show a mean coefficient of determination of 0.923 with a median value of 0.95, a mean root mean square error of 0.013 and median value of 0.09, and a mean absolute error of 0.011 with a median value of 0.08, indicating the regression can be useful in predicting discharge capacity.

Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies.