Capacity Fade In Lithium-ion Batteries Research Articles

Capacity fading and inter/intra-granular fracture of polycrystalline cathode particle induced by charge/discharge cycles: A numerical study

The capacity fading of Lithium-ion batteries (LIBs) containing polycrystalline cathode materials can be attributed to the degradation of secondary particles caused by inter- and intra-granular fractures occurring during charging and discharging cycles. In this work, a diffusion-induced stress (DIS) analysis method based on the finite-discrete element method (FDEM) is developed for studying the fracture behaviors of polycrystalline cathodes. The analysis of lithiation and delithiation processes shows that C-rate and interfacial fracture toughness have significant effects on inter-granular crack morphologies. Our method enables precise analysis of the competition between intra- and inter-granular cracks and the effect of crack-impeded diffusion on crack morphologies. We compared numerical simulation results for scenarios where fracture toughness changes with the number of cycles, and demonstrated that the decreasing fracture toughness with cycle number is an important factor contributing to the slow crack growth. Additionally, we established the relationship between crack length and capacity loss, which agreed well with experimental results.

Electrochemical–mechanical coupled model for computationally efficient prediction of long-term capacity fade of lithium-ion batteries

This paper proposes a novel physics-based (electrochemical–mechanical coupled) capacity fade model called the inhomogeneous stress-induced fracture (ISIF) model based on electrochemical and mechanical degradation mechanisms. The ISIF model models the mechanical fatigue fracture of cathode particles, which is a key aging mechanism that contributes to nonlinear aging observed in various studies. Through this approach, the ISIF model can accurately predict long-term capacity degradation, including the region of capacity loss below 80% of the initial capacity, without the need for time-consuming calculations, unlike electrochemical-based models. Additionally, the ISIF model can predict the knee point phenomenon, where there is a sudden decrease in capacity in the later-stages of battery life. We also propose a hybrid model called SVD-ISIF model that combines the ISIF model with a data-driven method called sparse variational dropout Bayesian neural network (SVDBNN) to improve accuracy and data efficiency especially when experimental data and computational resources are abundant, which helps users flexibly choose the optimal method for various situations. The proposed method was validated using experimental long-term capacity fading data from commercial 2170 NMC cells over a wide range of cycles (3000 to 20,000) that have not been studied before, because of the difficulties of conducting long-term aging cycle experiments and reproducing the knee point, to the knowledge of the authors. Both the ISIF model and the SVD-ISIF model accurately predict the long-term capacity reduction trend, including the knee point, with a mean absolute error is around or less than 2% for all data.

Stress-dependent capacity fade behavior and mechanism of lithium-ion batteries

Understanding the behavior and mechanism of capacity fade in lithium-ion batteries can assist in promoting their wider commercial and industrial applications. We develop an Electrochemical-Thermal-Mechanical coupled Capacity Fade model (ETMCF) for nickel-manganese-cobalt (NMC111)/graphite (C) lithium-ion batteries. The main fade behaviors include SEI formation, lithium plating/stripping, SEI re-formation, and loss of active material. ETMCF model focuses on the influence of stress on capacity fade behaviors, improves the accuracy of predicting battery capacity fade at high charge/discharge rates (C-rates) and a wide temperature domain, and confirms that the stress effect in lithium-ion battery capacity fade is essential and cannot be ignored. We systematically examine the impact of C-rate, operating temperature, and coupling effects on the capacity fade mechanism and discover markedly distinct dominant fade behaviors under various C-rates and temperatures. Moreover, we also construct a phase diagram of the capacity loss ratio and elucidate the dependency of the C-tare, temperature, and capacity loss relationship. This work provides new insights into fast charging strategy and temperature regulation in battery management systems and offers a comprehensive analytical framework for predicting and evaluating battery capacity fade.

H2O/HF Scavenging Mechanism in Cellulose-Based Separators for Lithium-Ion Batteries with Enhanced Cycle Life.

Lithium-ion batteries (LIBs) are increasingly being integrated into the transportation industry due to their high energy density, durability, and low cost. With the growing demand for transportation and other emerging applications, there is a concurrent rise in concern over LIB material sourcing and recycling. This urges the development of LIBs with extended cycle lifespans. One mechanism of capacity fading in LIBs is the dissolution of transition metals into the electrolyte after the cathode is etched with hydrofluoric acid (HF). HF is readily generated by the hydrolysis of the LIB electrolyte salt, lithium hexafluorophosphate (LiPF6), which makes minimizing moisture in the electrolyte a priority in manufacturing. In this study, a nonwoven, cellulose-based separator (CBS) is introduced as an alternative separator for battery technologies to scavenge residual water and HF from the electrolyte. The CBS is shown to be capable of scavenging varying amounts of water from the electrolyte based on different drying processes of the CBS, and a mechanism for this water scavenging is identified based on the materials present in the CBS. In addition, the chemical and electrochemical performance of the CBS in real battery conditions is investigated. Results suggest an effective H2O/HF scavenging capability in the CBS that allows LIB coin cells to have over 17% higher capacity retention than those with conventional separators. Furthermore, studies of the industrially manufactured, commercially relevant cylindrical and pouch cells show remarkable 761 and 103% improvements in the 60% capacity lifetime, respectively. The environmental friendliness, low cost, and easy application empowered by the cycle life improvements shown in this work make this nonwoven CBS a promising candidate for improving industry-level LIB performance.

Unraveling capacity fading in lithium-ion batteries using advanced cyclic tests: A real-world approach

Battery lifespan estimation is essential for effective battery management systems, aiding users and manufacturers in strategic planning. However, accurately estimating battery capacity is complex, owing to diverse capacity fading phenomena tied to factors such as temperature, charge-discharge rate, and rest period duration. In this work, we present an innovative approach that integrates real-world driving behaviors into cyclic testing. Unlike conventional methods that lack rest periods and involve fixed charge-discharge rates, our approach involves 1000 unique test cycles tailored to specific objectives and applications, capturing the nuanced effects of temperature, charge-discharge rate, and rest duration on capacity fading. This yields comprehensive insights into cell-level battery degradation, unveiling growth patterns of the solid electrolyte interface (SEI) layer and lithium plating, influenced by cyclic test parameters. The results yield critical empirical relations for evaluating capacity fading under specific testing conditions.

Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction

Machine Learning (ML) is a promising technique for battery health estimation and prediction. However, with more and more types of batteries entering the market, building an ML model from scratch for each new battery requires collecting a large amount of data, which is very expensive and time-consuming. This paper proposes a transfer learning approach to reduce the amount of data that needs to be recollected for a new battery. The key idea is to train an ML model for a new battery of interest (i.e., target battery) with a limited amount of data by transferring the knowledge contained in a well-studied battery (i.e., source battery) with sufficient data. We illustrate this approach using two types of batteries, i.e., the battery with Li0.86Ni0.86Co0.11Al0.03O2-based positive electrode (NCA battery, source battery) and the battery with Li0.84Ni0.83Co0.11Mn0.07O2-based positive electrode (NCM battery, target battery), which have similar degradation patterns but dramatically different cycle life. Specifically, we first pre-train a long short-term memory (LSTM) network, using cycling data of 20 NCA cells at 25 °C and at 45 °C, to predict the following capacity fade based on the previous capacity sequence. Then, to make the model applicable to NCM cells, we employ the transfer learning method to retrain the model, using cycling data of only 2 NCM cells at 25 °C, and propose a two-stage approach to further improve the model performance. The proposed two-stage model can predict the cycle life of NCM cells at 45 °C using the capacities of the first 13 cycles and obtain a cycle life root-mean-squared-error (RMSE) of 25.23 cycles and a capacity trajectory RMSE of 17.80 mAh (0.51 %).

Quantitative Analysis of Origin of Lithium Inventory Loss and Interface Evolution over Extended Fast Charge Aging in Li Ion Batteries.

During the extreme fast charging (XFC) of lithium-ion batteries, lithium inventory loss (LLI) and reaction mechanisms at the anode/electrolyte interface are crucial factors in performance and safety. Determining the causes of LLI and quantifying them remain an essential challenge. We present mechanistic research on the evolution and interactions of aging mechanisms at the anode/electrolyte interface. We used NMC532/graphite pouch cells charged at rates of 1, 6, and 9 C up to 1000 cycles for our investigation. The cell components were characterized after cycling using electrochemical measurements, inductively coupled plasma optical emission spectroscopy, 7Li solid-state nuclear magnetic resonance spectroscopy, and high-performance liquid chromatography/mass spectrometry. The results indicate that cells charged at 1 C exhibit no Li plating, and the increase of SEI thickness is the dominant source of the Li loss. In contrast, Li loss in cells charged at 9 C is related to the formation of the metallic plating layers (42%) the SEI layer (38.1%) and irreversible intercalation into the bulk graphite (19%). XPS analysis suggests that the charging rate has little influence on the evolution of SEI composition. The interactions between competing aging mechanisms were evaluated by a correlation analysis. The quantitative method established in this work provides a comprehensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, forecasting SEI failure scenarios, and assessing the XFC lithium-ion battery capacity fade.

Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods

This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms.

Elucidation of Side Reactions in Lithium-ion Batteries with Electrolyte Decomposition Products via Overdischarge for Li[Li<sub>1/3</sub>Ti<sub>5/3</sub>]O<sub>4</sub>/Li[Li<sub>0.1</sub>Al<sub>0.1</sub>Mn<sub>1.8</sub>]O<sub>4</sub> Cells with an Imbalanced State-of-Charge

A major factor in the capacity fading of lithium-ion batteries is the imbalance in the state-of-charge (SOC) between the positive and negative electrodes, which is caused by the difference in the side-reaction current between the two electrodes. Therefore, it is crucial to understand these side reactions to extend battery life. There are two types of side-reaction current, ISR: “intrinsic”-ISR results from electrolyte decomposition, while “additional”-ISR results from the reaction of decomposition products. The “additional”-ISR is closely related to crosstalk reactions. In this study, we conducted overdischarge tests on Li[Li1/3Ti5/3]O4 (LTO)/Li[Li0.1Al0.1Mn1.8]O4 (LAMO) cells with the SOC of the positive and negative electrodes intentionally imbalanced to understand the nature of the “additional”-ISR. While the cell capacity initially increased after the overdischarge, the recovered capacity decreased in subsequent cycles. This phenomenon can be explained well by a side-reaction model that considers the oxidant generated in the cell due to overdischarge, which indicates that the concentrations of oxidants and reductants in the cell are important for the ISR.

Evolving Elman neural networks based state-of-health estimation for satellite lithium-ion batteries

With the rapid development of aerospace industry, accurate and timely state-of-health (SOH) estimation by in-orbit measurable parameters is critical for the safe and reliable operation of satellite lithium-ion batteries. In this paper, a novel SOH estimation strategy is proposed based on the k-means clustering analysis (KMCA) and the evolving Elman neural network (EENN) by using the in-orbit discharging voltage data. The time series of equal discharging voltage difference (TSEDVD) is firstly developed as the feature to describe the capacity degradation in each charge-and-discharge cycle. Then, the KMCA is used offline to rank the capacity fading of lithium-ion battery into different health levels by virtue of the TSEDVD feature derived from the historical dataset throughout the battery lifecycle. Furthermore, a set of EENN models are constructed offline corresponding to the health levels and employed in estimating the SOH value online, where the improved evolving algorithm combining the genetic and simulated annealing algorithms is proposed to optimize the initial weights and thresholds in order to get the accurate estimation results. For the in-orbit application, the SOH value can be easily estimated online only via the monitored voltage signal exciting the corresponding EENN. The experimental results using the datasets from the NASA Ames Prognostics Center and the real GEO communication satellite demonstrate the validity of the proposed approach. It is feasible for the in-orbit SOH estimation of satellite lithium-ion batteries.

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning

Probing the Ni(OH)2 Precursor for LiNiO2 at the Atomic Scale: Insights into the Origin of Structural Defect in a Layered Cathode Active Material.

In lithium ion batteries (LIBs), the layered cathode materials of composition LiNi1- x - y Cox Mny O2 are critical for achieving high energy densities. A high nickel content (>80%) provides an attractive balance between high energy density, long lifetime, and low cost. Consequently, Ni-rich layered oxides cathode active materials (CAMs) are in high demand, and the importance of LiNiO2 (LNO) as limiting case, is hence paramount. However, achieving perfect stoichiometry is a challenge resulting in various structural issues, which successively impact physicochemical properties and result in the capacity fade of LIBs. To better understand defect formation in LNO, the role of the Ni(OH)2 precursor morphology in the synthesis of LNO requires in-depth investigation. By employing aberration-corrected scanning transmission electron microscopy, electron energy loss spectroscopy, and precession electron diffraction, a direct observation of defects in the Ni(OH)2 precursor preparedis reported and the ex situ structural evolution from the precursor to the end product is monitored. During synthesis, the layered Ni(OH)2 structure transforms to partially lithiated (non-layered) NiO and finally to layered LNO. The results suggest that the defects observed in commercially relevant CAMs originate to a large extent from the precursors, hence care must be taken in tuning the co-precipitation parameters to synthesize defect-free Ni-rich layered oxides CAMs.

Numerical assessment of thermal management on the capacity fade of lithium-ion batteries in electric vehicles

Electric vehicles, as a major strategy for climate change mitigation, uses lithium-ion batteries extensively as the power source. However, the operation, performance and lifetime of lithium-ion batteries depend on the battery temperature, which can have a wide range due to heat generation within the battery and significant variations in the ambient conditions due to changes in seasons and geographical locations where electric vehicles are operated. In the present study, thermal management methods/strategies on the capacity fade of lithium-ion batteries are assessed through a validated capacity fade model for lithium-ion batteries along with a thermal model for the heat generation in the battery and dissipation over battery surface, represented by various thermal management methods. The driving conditions are simulated through a constant and various standard drive cycles. It is shown that battery temperature has the predominant impact on the capacity fade, and it can be controlled through effective thermal management. A much more significant spread in battery capacity fade occurs with various thermal management methods for a lower initial battery temperature (20°C) compared to the higher temperatures (35°C and 50°C), hence, thermal management is much more effective in reducing capacity fade at battery temperatures close to 20°C, which is considered the optimum operating temperature for lithium-ion batteries. Further, the results indicate that using a lower charge voltage can result in slightly less capacity fade over cycling. Regenerative braking makes it more realistic to use lower charge voltages, since the battery can be recharged during operation, thereby increasing driving range, while preventing increased capacity fade. Effective thermal management is more imperative for realistic intense and aggressive driving behaviors.

Quantification analysis and kinetic modeling of crosstalk reactions in lithium-ion batteries using a four-electrode cell

• Crosstalk reactions greatly influence capacity fading of lithium-ion batteries. • A four-electrode cell with Al 2 O 3 electrodes was built to study crosstalk reactions. • Constant-current electrolysis and cyclic voltammetry were used in this regard. • A kinetic model was setup to quantitatively elucidate the changes in electricity. • The model provided adequate fits to the crosstalk-reaction-induced electricity. Crosstalk reactions in Li-ion batteries play an important role in the capacity fading caused by imbalance of the states-of-charge between positive and negative electrodes. A quantitative understanding of these reactions can enable estimation of battery life. Because crosstalk reactions are complex phenomena in which chemical species produced by a side reaction at one electrode react at the other electrode, a generator–collector-type electrochemical cell is required to investigate these reactions. Therefore, a new four-electrode cell was constructed in this study, in which oxidative decomposition of an electrolyte was achieved by constant-current electrolysis at one Al 2 O 3 electrode, and the reduction of the generated oxidants was detected by cyclic voltammetry (CV) at the other Al 2 O 3 electrode. CV indicated that the cathodic current due to the crosstalk reaction increased during electrolysis, and that two cathodic peaks appeared at 1.3 and 1.8 V. The cathodic current eventually stabilized and decreased to zero after electrolysis. A kinetic equation that expressed the crosstalk-reaction-induced cathodic current provided a decent fit to the observed current via kinetic parameters. This kinetic equation is remarkably useful for quantitatively understanding the electrochemical nature of crosstalk reactions and can help predict the side-reaction currents in actual batteries through the derived kinetic parameters, which can determine battery life.

Modeling capacity fade of lithium-ion batteries during dynamic cycling considering path dependence

Aging models are important tools to optimize the application of lithium-ion batteries. Usually, aging models are parameterized at constant storage or cycling conditions, whereas during application, storage and cycling conditions can change. In the literature, two different methods for modeling capacity fade during such dynamic operation are proposed. These methods use either the cumulated charge-throughput (CCT-method) or the current capacity (CAP-method) as reference points, when aging conditions are changing. In this work, we show that the CAP-method models capacity fade more accurately when applied to dynamic cyclic aging tests with periodically changing mean state-of-charge, depth-of-discharge, ambient temperature and discharge rates for a commercial NCA cell with a silicon-doped graphite anode. However, in cases where the difference between actual and reference charge-throughput of the CAP-method becomes large, the capacity gradient is modeled more accurately with the CCT-method. Because the relative capacity fade error of the CAP-method is small at <6%, we assume that capacity fade behaves path-independently for the dynamic cyclic aging tests since the CAP-method assumes path independence through history independence. Moreover, because the measured capacity fade is non-commutative, which is sometimes labeled path-dependent, we recommend to not consider non-commutative capacity fade as a definitive sign of path-dependent degradation.

Modeling the effect of acid attack on the capacity fading in lithium-ion batteries during cycling

Modeling the effect of acid attack on the capacity fading in lithium-ion batteries during cycling

An accurate and computationally efficient method for battery capacity fade modeling

• An efficient and fast model is presented to capture capacity fade in lithium-ion batteries. • Efficiently model a comprehensive array of degradation physics. • Simulation times are reduced by an average of 91%. The industry demand for accurate and fast algorithms that model vital battery parameters, e.g., state-of-health, state-of-charge, pulse-power capability, is substantial. One of the most critical models is battery capacity fade. The key challenge with physics-based battery capacity fade modeling is the high numerical cost in solving complex models. In this study, an efficient and fast model is presented to capture capacity fade in lithium-ion batteries. Here, the high-order Chebyshev spectral method is employed to address the associated complexity with physics-based capacity fade models. Its many advantages, such as low computational memory, high accuracy, exponential convergence, and ease of implementation, allow us to efficiently model a comprehensive array of degradation physics such as solid electrolyte interface film formation, hydrogen evolution, manganese deposition, salt decomposition, manganese dissolution, and electrolyte oxidation. In this work, we developed a modeling framework that accurately and efficiently predicted degradation in a lithium-ion battery over extended cycles. For example, in long cycle battery operation, the implemented Chebyshev spectral method algorithm was found to be within 0.1358% – 0.28% of a high-fidelity model, while simulation times were reduced by an average of 91%. The developed Chebyshev spectral method algorithm shows great potential in advanced battery management systems, where maintaining accuracy and achieving a fast response is critical.

Multimodal electrochemistry coupled microcalorimetric and X-ray probing of the capacity fade mechanisms of Nickel rich NMC - progress and outlook.

Lithium nickel manganese cobalt oxide (NMC) is a commercially successful Li-ion battery cathode due to its high energy density; however, its delivered capacity must be intentionally limited to achieve capacity retention over extended cycling. To design next-generation NMC batteries with longer life and higher capacity the origins of high potential capacity fade must be understood. Operando hard X-ray characterization techniques are critical for this endeavor as they allow the acquisition of information about the evolution of structure, oxidation state, and coordination environment of NMC as the material (de)lithiates in a functional battery. This perspective outlines recent developments in the elucidation of capacity fade mechanisms in NMC through hard X-ray probes, surface sensitive soft X-ray characterization, and isothermal microcalorimetry. A case study on the effect of charging potential on NMC811 over extended cycling is presented to illustrate the benefits of these approaches. The results showed that charging to 4.7 V leads to higher delivered capacity, but much greater fade as compared to charging to 4.3 V. Operando XRD and SEM results indicated that particle fracture from increased structural distortions at >4.3 V was a contributor to capacity fade. Operando hard XAS revealed significant Ni and Co redox during cycling as well as a Jahn-Teller distortion at the discharged state (Ni3+); however, minimal differences were observed between the cells charged to 4.3 and 4.7 V. Additional XAS analyses using soft X-rays revealed significant surface reconstruction after cycling to 4.7 V, revealing another contribution to fade. Operando isothermal microcalorimetry (IMC) indicated that the high voltage charge to 4.7 V resulted in a doubling of the heat dissipation when compared to charging to 4.3 V. A lowered chemical-to-electrical energy conversion efficiency due to thermal energy waste was observed, providing a complementary characterization of electrochemical degradation. The work demonstrates the utility of multi-modal X-ray and microcalorimetric approaches to understand the causes of capacity fade in lithium-ion batteries with Ni-rich NMC.

Quantitative Estimation of Turning Point of Ageing Based on a Two-Stage Model for Lithium-Ion Batteries

The capacity fading of lithium-ion batteries (LIBs) is commonly reported by a linear dependency followed by a nonlinear ageing process. The linear ageing process is dominated by solid electrolyte interphase formation and reformation (SEI and SEI-re), whereas the nonlinear ageing process is dominated by lithium plating. The turning point is usually alleged when the remaining capacities of LIBs are 80%. This empirical experience may deviate from the turning points of the LIBs in practice, especially under complicated conditions. In this work, a two-stage model is developed to quantitatively predict the turning point during the capacity fading of LIBs, which features the coupling of electrochemical and thermal models accounting for SEI, SEI-re and lithium plating. On the basis of this model, a quantitative evaluation method of the turning point is proposed by attributing the transition of the capacity fading to the balance of consumption of active lithium for SEI growth and lithium plating per cycle in the two stages. The characteristics of capacity fading of LIBs is quantitatively analyzed and discussed under various operation conditions and design parameters. An NCM111/graphite battery is used to validate the proposed model. The results show that it is effective to quantitatively divide the capacity fading curve into two stages by the proposed model, and the presence of the turning point of the two stages reflects the capacity fading of the LIBs. The turning points of the capacity fading processes are extensively influenced by the operation conditions and design parameters of the LIBs, where the reaction rate of lithium plating and SEI or SEI-re growth reign. According to the effect on the turning point, the order of significance of the factors is charging current, charging cut-off voltage, temperature, and N/P ratio, respectively. Moreover, the SEI and SEI-re growth are influenced by temperature, charging current, charging cut-off voltage and N/P ratio, whereas the lithium plating is influenced by charging current, temperature, charging cut-off voltage, and N/P ratio.

Modelling the effect of anode particle radius and anode reaction rate constant on capacity fading of Li-ion batteries

This paper investigates the effect of anode particle radius and anode reaction rate constant on the capacity fading of lithium-ion batteries. It is observed through simulation results that capacity fade will be lower when the anode particle size is smaller. Simulation results also show that the reaction rate constant for the anode reaction has a good impact on the capacity loss of a lithium-ion battery. The potential drop across the SEI layer (solid electrolyte interphase) is studied as a function of the anode particle radius and anode reaction rate constant. Modelling results are compared with experimental data and found to compare well.&#x0D;