Lithium-ion Battery Degradation Research Articles

Review of Lithium-Ion Battery Internal Changes Due to Mechanical Loading

The growth of electric vehicles (EVs) has prompted the need to enhance the technology of lithium-ion batteries (LIBs) in order to improve their response when subjected to external factors that can alter their performance, thereby affecting their safety and efficiency. Mechanical abuse has been considered one of the major sources of LIB failure due to the changes it provokes in the structural integrity of cells. Therefore, this article aims to review the main factors that aggravate the effects of mechanical loading based on the results of different laboratory tests that subjected LIBs to abusive testing. The results of different cell types tested under different mechanical loadings have been gathered in order to assess the changes in LIB properties and the main mechanisms responsible for their failure and permanent damage. The main consequences of mechanical abuse are the increase in LIB degradation and the formation of events such as internal short circuits (ISCs) and thermal runways (TRs). Then, a set of standards and regulations that evaluate the LIB under mechanical abuse conditions are also reviewed.

Correction: Tasnim Mowri et al. Assessing the Impact of First-Life Lithium-Ion Battery Degradation on Second-Life Performance. Energies 2024, 17, 501

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Unraveling the Degradation Mechanisms of Lithium-Ion Batteries

Lithium-Ion Batteries (LIBs) usually present several degradation processes, which include their complex Solid-Electrolyte Interphase (SEI) formation process, which can result in mechanical, thermal, and chemical failures. The SEI layer is a protective layer that forms on the anode surface. The SEI layer allows the movement of lithium ions while blocking electrons, which is necessary to prevent short circuits in the battery and ensure safe operation. However, the SEI formation mechanisms reduce battery capacity and power as they consume electrolyte species, resulting in irreversible material loss. Furthermore, it is important to understand the degradation reactions of the LIBs used in Electric Vehicles (EVs), aiming to establish the battery lifespan, predict and minimise material losses, and establish an adequate time for replacement. Moreover, LIBs applied in EVs suffer from two main categories of degradation, which are, specifically, calendar degradation and cycling degradation. There are several studies about battery degradation available in the literature, including different degradation phenomena, but the degradation mechanisms of large-format LIBs have rarely been investigated. Therefore, this review aims to present a systematic review of the existing literature about LIB degradation, providing insight into the complex parameters that affect battery degradation mechanisms. Furthermore, this review has investigated the influence of time, C-rate, depth of discharge, working voltage window, thermal and mechanical stresses, and side reactions in the degradation of LIBs.

Degradation of lithium-ion batteries under automotive-like conditions: aging tests, capacity loss and q-OCP interpretation

Battery electric vehicles are spreading worldwide as a relevant solution for the decarbonization of the transportation sector, ensuring high volume and weight-based energy density, high efficiency and low cost. Nevertheless, batteries are known to age in a rather complex and conditions-dependent way. This work aims at investigating battery aging resulting from close-to-real world conditions, highlighting single stressors role. Hence, aiming at representativeness for automotive application, an extensive literature review is performed, identifying a wide set of representative conditions together with their specific variations to be investigated. Realistic driving schedules like WLTP is identified and continuously applied in cycling on commercial samples, investigating the capacity loss from a q-OCP perspective with an equilibrium model. In general, loss of lithium inventory is detected as the main degradation parameter, likely related to SEI growth. Recharge C-rate and load profile appear as poorly-affecting degradation, while a dominant role is associated with operating temperature. Interestingly, temperature and cycling-related degradation appears to be independent and their effects can be effectively superimposed. Loss of active positive electrode material seems particularly affected by cycling depth of discharge, likely having mechanical origin as particle cracking.

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

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.

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.

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.

Development of a Multiplexing Injector for Gas Chromatography for the Time-Resolved Analysis of Volatile Emissions from Lithium-Ion Batteries.

Multiplex sampling, so far mainly used as a tool for S/N ratio improvement in spectroscopic applications and separation techniques, has been investigated here for its potential suitability for time-resolved monitoring where chromatograms of transient signals are recorded at intervals much shorter than the chromatographic runtime. Different designs of multiplex sample introduction were developed and utilized to analyze lithium-ion battery degradation products under normal or abuse conditions to achieve fast and efficient sample introduction. After comprehensive optimization, measurements were performed on two different GC systems, with either barrier discharge ionization detection (BID) or mass spectrometric detection (MS). Three different injector designs were examined, and modifications in the pertinent hardware components and operational conditions used. The shortest achievable sample introduction time was 50 ms with an interval of 6 s. Relative standard deviations were lower than 4% and 10% for the intra- and inter-day repeatability, respectively. The sample introduction system and column head pressure had to be carefully controlled, as this parameter most critically affects the amount of sample introduced and, thus, detector response. The newly developed sample introduction system was successfully used to monitor volatile degradation products of lithium-ion batteries and demonstrated concentration changes over the course of time of the degradation products (e.g., fluoroethane, acetaldehyde and ethane), as well as for solvents from the battery electrolyte like ethyl carbonate.

Analytical computation of stress intensity factor for multi-physics problems

This work presents a methodology for the analytical calculation of the stress intensity factor when the stress distribution on the crack surfaces is non-homogeneous. At first, a polynomial function is used to express the non-homogenous stress distribution. Subsequently, the principle of superposition of effects is applied, and the stress intensity factor is computed by multiplying each polynomial term by its respective geometric factor. Finite element fracture model is used to compute the geometric factor of the single polynomial grade. To explain the method, a spherical body is considered, with central and superficial cracks. Each geometric factor depends on a normalized geometrical parameter (the ratio between the crack length and sphere radius). The proposed methodology is applied to determine the stress intensity factor in the case of a crack driving force caused by diffusive fields, such as the concentration gradient in particles of electrodes active material in lithium-ion batteries. The methodology allows to speed up the fracture computation, then it is used to give electrode design guidelines to limit the fracture likeliness and mechanical degradation in lithium-ion batteries, as well as it is the basis for the development of algorithms assessing the capacity loss and the remaining useful life of lithium-ion batteries in real-time.

Chemical aspects of the degradation of lithium-ion batteries based on layered oxide LiNi0.6Mn0.2Co0.2O2 and graphite

Chemical aspects of the degradation of lithium-ion batteries based on layered oxide LiNi0.6Mn0.2Co0.2O2 and graphite

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.