Lithium-ion Battery Degradation Research Articles

Gaining insight into lithium-ion battery degradation by a calorimetric approach

With the growing demand for lithium-ion batteries (LIBs) in transportation and renewable energy sectors, ensuring reliability and mitigating degradation are crucial challenges for their widespread deployment. This study investigates the relationship between thermal characteristics and degradation/reliability by developing an in-situ heat measurement methodology for LIBs operating under grid services. Using adiabatic mode-based calorimetry, we determine the thermal behavior of two commercial cells with distinct cathode chemistries (Ni-rich layered oxide and olivine) before and after two years of peak shaving operation and demonstrate a strong correlation between thermal characteristics and electrochemical performance and its degradation. By further analyzing the heat sources separation and differentiating between irreversible and reversible heat, we disclose the underlying degradation mechanisms of LIBs. These findings emphasize the critical role of thermal characteristics in determining LIB reliability and degradation, while the obtained heat data can aid in the development and calibration of LIB state-of-health models for grid applications.

Effect of fast charging on degradation and safety characteristics of lithium-ion batteries with LiNixCoyMnzAl1-x-y-zO2 cathodes

Fast charging compatibility is an important technical aspect required of advanced lithium-ion (Li-ion) batteries to lead the revolution and increase the adoption of electric vehicles. Although substantial material-level innovations have greatly promoted the widespread employment of fast charging rates for Li-ion batteries, the unfavorable rapid degradation and cell-level thermal instability are still bottlenecks for commercial success. In this study, fast-charging induced aging mechanisms and thermal safety characteristics of Li-ion batteries with quaternary energy-dense LiNixCoyMnzAl1-x-y-zO2 (NCMA) cathodes are comprehensively investigated. Promising fast-charge strategies under different thermal environments are applied to reveal their specific adverse side effects on electrochemical performances and cell lifetime. Post-mortem analysis is conducted to understand the distributions, microstructures, and chemical states of electrodepositions on cell components. Cell-level thermal safety evaluations are carried out based on accelerating rate calorimeter tests to determine evolutions of thermal safety hazards as a result of imposed fast charging conditions. This research highlights the significant role of lithium plating and aluminum dissolution in accelerating the thermo-electrochemical failure of the chosen NCMA-based Li-ion chemistry, providing new insights on degradation-safety interactions and effective mitigation strategies under fast charging conditions.

A nonparametric degradation modeling method for remaining useful life prediction with fragment data

Condition-based maintenance (CBM) is an effective way to keep the safety of industrial equipment by predicting the remaining useful life (RUL) and scheduling the maintenance plan before failure. Condition monitoring data is the basis of health state evaluation and RUL prediction. In ideal cases, the monitoring data are collected consecutively starting from the healthy stage until the end of the lifetime. In real industrial cases, however, fragment data often exist due to the interruption of monitoring and/or the loss of sensor readings. The major characteristic of fragment data is that they only record a random period of degradation process. The initial degradation time information is generally lost. Therefore, it is unable to be modeled using the common time-dependent modeling framework. To deal with the above issue, this paper proposes a nonparametric degradation modeling method for RUL prediction with fragment data. In this method, a new state-dependent degradation modeling framework is constructed via two-step axis transform. It formulates the RUL using a function depending on the health state. Based on functional principle component analysis (FPCA), a principal analysis via maximum likelihood estimation (PAMLE) algorithm is developed to recover the missing data of failed units. In addition, a RUL prediction-oriented optimization (POO) algorithm is proposed to predict the RUL of in-service units based on a piece of fragment data. Consequently, the proposed method is capable of dealing with the issues of both data recovery and RUL prediction. The effectiveness of the method is demonstrated using a fatigue crack growth dataset and a lithium-ion (Li-ion) battery degradation dataset.

Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery

Lithium-ion batteries may suffer an abnormal degradation defined by a significantly accelerated performance drop after a period of linear and low-rate degradation, resulting in severe danger to operational safety and reliability. Existing supervised data-driven prognostics for abnormal degradation rely heavily on adequate high-quality training samples, thus hindering their real-world utilization. Therefore, this paper develops an unsupervised dynamic prognostics framework that is dynamically executed as the cycle number increases to provide accurate and timely warnings for abnormal degradation. In the framework, an extended asymmetric quantum clustering (EAQC) can preliminarily identify the risky battery with the abnormal degradation trend. Its use of multi-dimensional feature degradation rates and potential function for clustering makes it outperform other density-based clustering methods. Then, for the identified battery, a time-varying double-layer autoregression (TVDLAR) can accurately predict its knee point of abnormal degradation. TVDLAR produces much earlier warning than other autoregression methods, which owes to its modeling ability of the time-varying correlation in battery historical degradation data. Through applying to two experimental datasets consisting of 174 lithium-ion batteries of different types under various working conditions, the framework is proven highly effective and shows significant superiority over some alternative approaches; all abnormal degradation can be timely warned before the corresponding knee points, and the average earlier warning cycles are 86 and 116, respectively. Compared with supervised data-driven methods, the proposed unsupervised dynamic prognostics framework has the advantages of low data requirement, low computational consumption, and good interpretability, indicating its substantial potential for application.

A Comprehensive Review of In Situ Measurement Techniques for Evaluating the Electro-Chemo-Mechanical Behaviors of Battery Electrodes.

The global production landscape exhibits a substantial need for efficient and clean energy. Enhancing and advancing energy storage systems are a crucial avenue to optimize energy utilization and mitigate costs. Lithium batteries are the most effective and impressive energy utilization system at present, with good safety, high energy density, excellent cycle performance, and other advantages, occupying most of the market. However, due to the defects in the electrode material of the battery itself, the electrode will undergo the process of expansion, stress evolution, and electrode damage during electro-chemical cycling, which will degrade battery performance. Therefore, the detection of property changes in the electrode during electro-chemical cycling, such as the evolution of stress and the modulus change, are useful for preventing the degradation of lithium-ion batteries. This review presents a current overview of measurement systems applied to the performance detection of batteries' electrodes, including the multi-beam optical stress sensor (MOSS) measurement system, the digital image correlation (DIC) measurement system, and the bending curvature measurement system (BCMS), which aims to highlight the measurement principles and advantages of the different systems, summarizes a part of the research methods by using each system, and discusses an effective way to improve the battery performance.

Diagnosis of Li-ion battery degradation based on resistive and diffusion-related transient voltage changes at early stage of discharge

Lithium-ion batteries degradation can be classified into capacity reduction and increase of the internal resistance. Accurate estimation of the degradation is important to determine the economic value and the remaining useful life of the battery. This paper proposes a method which analyzes the transient voltage response at the early stage of discharge, starting from the fully charged state. An equivalent circuit composed by a series resistance and a Warburg element is adopted to fit the transient voltage. The Warburg element is modeled as a transmission line composed by a ladder of resistances and capacitances. The method is validated with data obtained before and after aging cycles performed at 1.4 C charge and 2.0 C discharge rates, at 25 °C, 10 °C and −5 °C. The loss of cyclable lithium is deemed to be the main reason behind the battery degradation at all the tested temperatures. The dependency of the Warburg parameter to the remaining cell capacity is the same regardless of the aging temperature, suggesting that estimation of the Warburg parameter provides information about the state of health of the cell. On the other hand, the relationship between the capacity reduction and the increase of resistance has a temperature-dependent slope with low increase at lower temperatures where lithium plating is likely to occur. Accordingly, a relatively low resistance increase is deemed to be the sign of lithium plating.

A Lithium-Ion Battery Degradation Prediction Model With Uncertainty Quantification for Its Predictive Maintenance

Battery degradation modeling in the presence of uncertainty is a key but challenging issue in the applications of battery predictive maintenance. This paper develops a capacity prediction model with uncertainty quantification for lithium-ion batteries and proposes a dynamic maintenance strategy which can help to make an optimized decision at each battery cycle stage. To be specific, after using the one-dimensional convolution neural network (1dCNN), deep representative features hidden in original measured signals are extracted. Then, the bi-directional long short-term memory (Bi-LSTM) is applied to estimate the battery capacities, while the quantile regression (QR) layer is embedded into the construction of the Bi-LSTM network to obtain the capacities for different quantiles. Next, the kernel density estimation (KDE) is utilized to derive the probability density of the predicted points at each battery cycle stage. Thus, the combination of 1dCNN, Bi-LSTM, QR and KDE, named 1dCNN-BiLSTMQR-KDE, forms an efficacious capacity prediction model with reliable uncertainty management. Finally, the costs of different decisions at each battery cycle stage are evaluated, and the decision with the lower cost will be chosen. The whole proposition is verified on battery degradation datasets from NASA, and the comparison with other methods show that the proposed method is competitive.

Towards Long-Term Monitoring of Commercial Lithium-Ion Batteries Enabled by Externally Affixed Fiber Sensors and Strain-Based Prognostic Strategies

Abstract Real-time monitoring of both continuous and spontaneous degradation in lithium-ion batteries is challenging due to the limited number of quantitative metrics available during cycling. In this regard, improved sensing approaches enabled by sensors of high accuracy, precision, and durability are key to achieving comprehensive state estimation and meeting rigorous safety standards. In this work, external temperature and strain monitoring in commercial Li-ion button cells was carried out using tandem pairs of polymer-based and silica-based optical fiber Bragg grating sensors. The decoupled data revealed that the sensors can reliably track strain and temperature evolution for over 500 cycles, as evidenced by periodic patterns with no sign of sensor degradation or loss of fidelity. Moreover, monitoring the strain signal enabled early detection of an anomalous cell over ~60 cycles ahead of an electrochemical signature and abrupt drop in capacity, suggesting that mechanical sensing data may offer unique benefits in some cases. Detailed mechanical monitoring via incremental strain analysis suggests a parallel path toward understanding cell degradation mechanisms, regardless of whether they are continuous or discrete in nature. The accuracy and durability of such a package-level optical fiber sensing platform offer a promising pathway for developing robust real-time battery health monitoring techniques and prognostic strategies

Application of machine learning in ultrasonic diagnostics for prismatic lithium-ion battery degradation evaluation

Lithium-ion batteries are essential for electrochemical energy storage, yet they undergo progressive aging during operational lifespan. Consequently, precise estimation of their state of health (SOH) is crucial for effective and safe operation of energy storage systems. This paper investigates the viability of ultrasound-based methods for assessing the SOH of prismatic lithium-ion batteries. In the experimental framework, a designated prismatic lithium-ion battery was subjected to numerous charging and discharging cycles using a battery cycling system. Subsequently, ultrasonic detection experiments were conducted to record the waveforms of the transmitted and received signals. These signals were then processed through wavelet transforms to extract signal amplitude and time-of-flight data. To analyse these data, we applied four algorithms: linear regression, support vector machines, Gaussian process regression, and neural networks. The predictive performance of each algorithm was evaluated through extensive experimentation and analysis. The combination of ultrasonic signals with computational models has emerged as a robust technique for precise battery degradation assessment, suggesting its potential as a standard in battery health evaluation methods.

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Monitoring the health of lithium-ion batteries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell’s major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

Improved lithium-ion battery health prediction with data-based approach

The accurate modeling and forecasting of Battery State of Health (SOH) is crucial for ensuring reliable performance and longevity of lithium-ion batteries. This article introduces a data-driven approach for SOH prediction using Gaussian Process Regression (GPR), selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information. Recognizing the effect of reversible capacity recoveries on prediction accuracy, this work employs the ISEA-RWTH 48 Li-ion Cells dataset, deliberately devoid of such recoveries prior to training the GPR model. The GPR model was evaluated and compared with Support Vector Regression (SVR) using the publicly available dataset. First and second End of Life (EOL) scenarios were considered, relevant to primary and secondary battery applications. The results demonstrated the GPR model's superior performance. Particularly, mid-life and late-life predictions displayed better accuracy with GPR, showcasing higher R2 values and lower MAPE values (e.g., mid-life prediction: GPR's average R2 = 0.99, SVR's = 0.9789; GPR's average MAPE = 0.1916, SVR's = 1.3028). Moreover, GPR exhibited the ability to quantify uncertainty in capacity degradation and forecast first and second EOL instances effectively (e.g., mid-life predictions had 1.7 cycle error at 1st EOL and 8.9 cycle error at 2nd EOL). The research also offers valuable insights into the application of machine learning methods for predicting the health degradation of lithium-ion batteries.

A framework to quantify battery degradation in residential microgrid operate with maximum self-consumption based energy management system

Battery Energy Storage System (BESS) is a crucial component for improving the performance of Hybrid Renewable Energy System (HRES) based microgrid. This paper aims to quantify the battery capacity fade due to battery charging/discharging cycling in a DC microgrid operate with well-known rule-based energy management system, Hence, based on a reduced-order physical model for Lithium-ion battery degradation, a framework was established to assess the state of health (SOH) of the battery. The simulation results show that limiting charging/discharging cycling help in prevent high-capacity loss, however its effect on the self-consumption and self-sufficiency in residential remains debatable in the future.

Assessing the Impact of First-Life Lithium-Ion Battery Degradation on Second-Life Performance

The driving and charging behaviours of Electric Vehicle (EV) users exhibit considerable variation, which substantially impacts the battery degradation rate and its root causes. EV battery packs undergo second-life application after first-life retirement, with SoH measurements taken before redeployment. However, the impact of the root cause of degradation on second-life performance remains unknown. Hence, the question remains whether it is necessary to have more than a simple measure of state of health (SoH) before redeployment. This article presents experimental data to investigate this. As part of the experiment, a group of cells at around 80% SoH, representing retired EV batteries, were cycled using a representative second-life duty cycle. Cells with a similar root cause of degradation in the first life (100–80% SoH) exhibited the same degradation rate in second life after being cycled with the same duty cycle during the second life. When the root cause of degradation in the first life is different, the degradation rate in the second life may not be the same. These findings suggest that the root cause of a cell’s first-life degradation impacts how it degrades in its second life. Postmortem analysis (photographic and SEM images) reveals the similar physical condition of negative electrodes which have similar degradation rates in their second life cycle. This demonstrates that cells with a similar first life SoH and root cause of degradation indeed experience a similar life during their second life. The experimental results, along with the subsequent postmortem analysis, suggest that relying solely on SoH assessment is insufficient. It is crucial to take into account the root causes of cell degradation before redeployment.

Lifetime prediction and maintenance assessment of Lithium-ion batteries based on combined information of discharge voltage curves and capacity fade

Current two-stage Lithium-ion battery degradation models commonly treat the change point (CP) in two ways. First, it is a random variable, which increases model complexity and the computational cost for predicting the remaining useful life (RUL). Second, it is a deterministic value, which will simplify the degradation models. The value of CP needs to be well identified for an accurate description of the degradation process. However, the capacity data is generally non-monotonic due to energy regeneration, which makes it hard to use in determining the CP. In addition, our laboratory data on the discharge voltage profile shows a potential for CP detection. Thus, we developed a hybrid method that combines dynamic time warping and nonnegative matrix factorization to detect the CPs of battery cells by using voltage discharge profiles. Then, we proposed a two-stage Wiener process incorporating CP detection method to describe the battery capacity degradation pattern. The proposed model is applied to our lab data and NASA data to predict RUL. Finally, maintenance strategies are analyzed to enhance the management of Lithium-ion batteries by minimizing the long-term cost rate under different replacement policies.

Targeting the low-temperature performance degradation of lithium-ion batteries: A non-destructive bidirectional pulse current heating framework

The poor low-temperature performance of lithium-ion batteries (LIBs) significantly impedes the widespread adoption of electric vehicles (EVs) and energy storage systems (ESSs) in cold regions. In this paper, a non-destructive bidirectional pulse current (BPC) heating framework considering different BPC parameters is proposed. To determine the lithium plating boundary of graphite anode, a three-electrode battery is first fabricated. Then, an electro-thermal coupled model is established and the model parameters consider not only the effects of temperature and heating frequency but also the effect of current amplitude. Subsequently, a novel lithium plating criterion is derived based on the thermodynamic theory and the graphite anode model. Finally, leveraging the electro-thermal coupled model and considering the constraints of lithium plating and terminal voltage, the effects of different BPC heating parameters on battery heat generation are discussed and a novel BPC heating strategy is designed. The results show that the heat generation power is highest when the ratio of discharge time to charge time is 0.55. Additionally, the BPC heating strategy proposed in this paper is 45 s faster than the traditional BPC heating strategy when the battery is heated from -15 °C to 10 °C at 100 Hz. The heating rate of the new BPC strategy is 3.51 °C/min and there is no observed capacity degradation after 100 heating cycles. The BPC heating framework proposed in this paper effectively improves the low-temperature performance of the battery and contributes positively to the promotion of EVs and ESSs in cold regions.

Understanding of Lithium-ion battery degradation using multisine-based nonlinear characterization method

The nonlinearity of lithium-ion battery voltage response has been recently gained high attention in battery characterization and health diagnosis. The multisine-based nonlinear characterization method has the potential for development as an expedient on-board technique for analyzing nonlinear responses. Despite this, it remains challenging to analyze the effect of aging degradation on LIB nonlinearity. In this study, the odd random-phase multisine method is performed on fresh and aged three-electrode experimental cells. This allowed for the separation of impedance-related linear approximation and odd or even order nonlinearity toward the full-cell voltage into their respective electrodes. The results demonstrate that, as the LIB degrades, the increase of impedance-related linear approximation estimated by the multisine-based method agrees well with the results of conventional EIS. The variation of nonlinearities is demonstrated in relation to the effect of degradation modes. The multisine-based method presents the advantage of simultaneously capturing impedance-related and nonlinearity information. This makes it become a fast diagnostic method that can be implemented in a BMS to quantify the causes of battery degradation, thereby supporting battery utilization optimization and future battery designs.

Capacity Degradation Assessment of Lithium-Ion Battery Considering Coupling Effects of Calendar and Cycling Aging

Capacity degradation of lithium-ion batteries largely determines the cost, performance and environmental impact of various products such as renewable energy production systems, portable electronics, and electric vehicles. Degradation assessment is thus of great importance for prognostic and health management of batteries, which ensures a stable production and operation. There are typically two leading sources contributing to the degradation of a lithium-ion battery, namely, cycling aging during charge/discharge cycles and calendar aging during idle states. However, most existing studies on degradation assessment either only consider a single source or ignore the coupling of these two sources, which makes the evaluation inefficient. A common observation in practice is that a battery tends to degrade faster when it experiences charge/discharge cycles continuously for a longer time. To capture this feature, this paper introduces the concept of the cumulative uninterrupted cycling duration (CUCD), which is defined as the consecutive operation duration since a battery ends the idle state. The CUCD can also help to model the coupling between the calendar and cycling aging. This paper then proposes to model the drift rate of cycling aging as a function of the CUCD and the functional form is determined with the monotonic spline. We estimate the model parameters by maximizing the likelihood function. Hypothesis testings toward the significance of the coupling between two aging sources and the monotonicity of the drift rate function under cycling aging are also provided. The effectiveness and the superiority of the model are validated using numerical and real case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study aims to characterize the degradation of lithium-ion battery simultaneously considering calendar and cycling agings and the coupling effect. Unlike previous research that only supplied experimental results to qualitatively illustrate the existence of the coupling effect between these two aging sources, this study provides procedures to quantitatively test the significance of such an effect. In addition, previous research ignored the coupling effect in the degradation modeling as it is difficult to capture, this study propose the concept of CUCD to characterize it. The CUCD is also introduced as a stress factor for cycling aging, which is based on the fact that a battery will degrade faster when it experiences charge/discharge cycle continuously for a longer time. Hypothesis testing is also provided to illustrate this monotonicity. To deal with another challenge that the expert knowledge of the functional form between cycling aging and CUCD is unavailable except for the monotonicity, a nonparametric method with the shape-restricted spline is adapted. The estimation procedures for the model parameters are also presented.

Diagnosis of high-Ni NCA/Gr-Si cells before rapid capacity drop by monitoring the heterogeneous degradation.

Understanding the degradation of lithium-ion batteries is of utmost significance for preventing unexpected capacity drops and addressing safety concerns. The manner in which batteries degrade during operation has a notable influence on their subsequent cycle performance. In particular, the rapid capacity drop related to the spatial heterogeneity of the anode degradation highlights the necessity of a health indicator for an accurate battery diagnosis. A novel health indicator established in this study, the Dominant degradation factors among Negative and Positive electrodes (DNP) scores, enables clear identification of degraded states despite comparable capacity levels. Specifically, batteries with heterogeneous anode degradation exhibited negative scores and the aggravation of the cycle performance. It is anticipated that this health indicator can provide a distinct evaluation of batteries based on their degraded states, supporting onboard battery management and the efficient allocation of resources for the battery reuse industry.

Suppressing Anodic Degradation of Lithium-Ion Batteries by Sinusoidal Waveform Charging Method

The effects of Constant current-Constant voltage charging and combined sinusoidal waveform charging methods on the internal structure of LiFePO4 battery have been discussed in detail in this study. Experimental results clearly indicated that after 600 cycles, the battery that utilized the charging method had retained about 94% of state of health. In the electrochemical impedance analysis and cyclic voltammetry tests, the portion of the SEI film impedance was increased about 0.08 mΩ. In the micro structured view, no significant SEI film formation was observed on the surface of the negative electrode that used the battery in the present charging method. Finally, in the analysis of the SEI film composition, it was found that the Li2CO3 and Li2O content in the Li element was only about 15.97% and 4.42% in the battery employing this charging method, and the battery component contents in the CC-CV charging method were 27.2% and 6.69% respectively.

A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.