Lithium-ion Battery Degradation Research Articles

Lithium-Ion Battery Cathode-Electrolyte Interface

The global energy crisis, consumption of fossil fuels, and the associated environmental issues have stimulated extensive interest in searching for sustainable energy storage and conversion systems. Rechargeable lithium-ion batteries (LIBs) are widely used in mobile devices, hybrid, and electric vehicles. Electrode-electrolyte interface has a strong impact on battery capacity decrease and long-term cycling stability. In order to improve fundamental understanding of the involved phenomena to accelerate the rational design of cathode materials and stable interface, Li(Ni,Co,Mn)O2 (NCM) is a cathode material for rechargeable LIB and also a promising electrocatalyst for oxygen evolution reaction (OER). Their battery performance and catalytic activity are highly correlated to their structures. Multiple processes occurring at the cathode/electrolyte interface led to overall performance degradation. One failure mechanism is the dissolution of transition metals from the cathode. Our efforts employ thin film as a model cathode to monitor the Mn dissolution. Using LiMn2O4 and NCM as cathode materials, we will focus on electrochemical characterization, cation mixing, oxygen evolution, and thin film deposition with controlled crystal structures and composition. We will further introduce our work employing the scanning electrochemical microscope to study LIBs degradation and cathode-electrolyte interface in situ and in real-time.

A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory

Accurately predicting the remaining lifespan of lithium-ion batteries is critical for the efficient and safe use of these devices. Predicting a lithium-ion battery’s remaining lifespan is challenging due to the non-linear changes in capacity that occur throughout the battery’s life. This study proposes a fused prediction model that employs a multimodal decomposition approach to address the problem of non-linear fluctuations during the degradation process of lithium-ion batteries. Specifically, the capacity attenuation signal is decomposed into multiple mode functions using successive variational mode decomposition (SVMD), which captures capacity fluctuations and a primary attenuation mode function to account for the degradation of lithium-ion batteries. The hyperparameters of the long short-term memory network (LSTM) are optimized using the tuna swarm optimization (TSO) technique. Subsequently, the trained prediction model is used to forecast various mode functions, which are then successfully integrated to obtain the capacity prediction result. The predictions show that the maximum percentage error for the projected results of five unique lithium-ion batteries, each with varying capacities and discharge rates, did not exceed 1%. Additionally, the average relative error remained within 2.1%. The fused lifespan prediction model, which integrates SVMD and the optimized LSTM, exhibited robustness, high predictive accuracy, and a degree of generalizability.

Nonlinear aging knee-point prediction for lithium-ion batteries faced with different application scenarios

The capacity degradation of lithium-ion batteries (LIBs) will accelerate after long-term cycling, showing nonlinear aging features, which not only shortens the long-term life of LIBs, but also seriously endangers their safety. In this paper, by introducing the concept of nonlinear aging degree, a knee-point identification method based on the maximum distance method is established, and the nonlinear aging behavior of LIBs is identified and marked, so as to know whether the nonlinear aging phenomenon has occurred. Furthermore, two knee-point prediction methods have been proposed and compared. The direct knee-point prediction method based on stacked long short-term memory (S-LSTM) neural network and sliding window method is proposed for the scenarios of battery development, early performance evaluation and online application. For scenarios such as echelon utilization and post-safety evaluation, an indirect knee-point prediction method combining capacity prediction and knee-point identification algorithm is proposed. Through multi-dimensional comparison of the two methods, the strengths and weaknesses of their applicable scenarios are analyzed. Our work has guiding significance for finding the ideal replacement opportunity of LIBs in different scenarios, so that the user can be reminded whether to maintain or replace the battery, which greatly reduces the risk of battery safety problems.

Data-Driven Lithium-Ion Battery Degradation Evaluation Under Overcharge Cycling Conditions

Accurately assessing degradation and detecting abnormalities of overcharged lithium-ion batteries is critical to ensure the health and safe adoption of electric vehicles. This paper proposed a data-driven lithium-ion battery degradation evaluation framework. First, a multi-level overcharge cycling experiment was conducted. Second, the battery degradation behaviours and features were analyzed and extracted using incremental capacity analysis and pearson correlation coefficient. Above all, a data-driven lithium-ion battery degradation evaluation method based on machine learning and model integration method was developed. The proposed integrated model was compared with other state-of-the-art methods and reached a mean squared error of 1.26×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . Finally, based on prediction results, rate of degradation was calculated and classified to different degrees, and overcharged cells can be effectively identified. Moreover, to verify the feasibility of the proposed overall framework, the paper carried out an experiment by connecting overcharge-induced degraded cell and fresh cells in series to simulate the real-world battery assembly and function of battery management systems. Based on the proposed scheme, the overcharged batteries in the battery series can be detected efficiently likewise.

Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries

The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion (Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system (BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health (SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell’s health at a very early stage of Li-ion battery usage. A transferred convolutional neural network (CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression

Lithium-ion battery (LIB) capacity degradation prediction plays an important role in the prediction of battery health degradation. Accurate prediction of its capacity can guide battery replacement and maintenance, and ensure the safety and stability of the energy storage system. In this paper, a hybrid method based on artificial bee colony (ABC) algorithm and multi-kernel support vector regression (MK-SVR) is proposed to predict the capacity degradation of LIB. Firstly, the capacity degradation prediction model of LIB is established by MK-SVR with few hyperparameters. Then, the hyperparameters of degradation prediction model are optimized by the ABC to improve the accuracy of prediction. Finally, the proposed method is verified by NASA's LIB degradation experiment. Compared with different optimization algorithms, ABC greatly improves the accuracy of capacity degradation prediction. Compared with traditional forecasting methods, the proposed method can predict the capacity degradation of LIB more accurately.

Synergistic effect and enhanced safety of double dynamic solid polymer electrolytes for lithium metal batteries

A solid polymer electrolyte with low mechanical strength and poor flexibility will cause mechanical degradation, short circuits, or functional failures in lithium ion batteries (LIBs). Therefore, introducing an double dynamic solid polymer electrolytes with high ionic conductivity, good mechanical strength, and excellent self-healing ability for stabilizing the solid electrolyte interface, preventing battery fire, and suppressing Li dendrite growth will further improve the reliability, security, and lifetime of the LIBs. Herein, a series of dual dynamic self-healing solid polymer electrolytes (DD-shSPEs) are developed via the Schiff base reaction between different molar ratios of polyethylene glycol derivative (tat-PEG) and terephthalaldehyde. The hydrogen bond and the highly reversible imine bonds endow DD-shSPEs with rapid self-repairing capacity to cure mechanical damage within 5 min at normal temperature. The self-healing polymer electrolytes also exhibit excellent stretchability, which can satisfy the external deformation of the battery. Meanwhile, after self-healing, the ionic conductivities of the electrolytes can hold the original values. Also, the initial discharge capacity (IDC) of the battery based on DD-shSPEs reach to 149.3 mAh g-1 under a current rate of 0.1 C, and still maintains 144.2 mAh g−1 after 100 cycles. Besides, the polymer electrolyte (PE) also exhibits good interfacial stability and have a cycle life of more than 650 h at a current density of 0.2 mA cm-2. Accordingly, this work shows that the high-performance PE based on self-healing materials is promising for constructing high stable solid-state energy storage device.

Characterizing degradation in lithium-ion batteries with pulsing

Degradation in lithium-ion batteries is traditionally characterized with the pseudo open-circuit voltage (pOCV) or incremental capacity (IC) but these methods have hours-long diagnostics times and cannot easily measure impedance change. It is shown here that a pulse with amplitude 1 C-rate can perform both IC and impedance characterization in just 2 min. Pulses and C/20 pOCV from 6 lithium-ion cells at 1328 unique combinations of state of charge, state of health, and temperature are evaluated using the convolution-defined diffusion equivalent circuit model, ridge regression, and neural networks. Ridge regression of the IC extrema and the pulse harmonics predicts SoH and nominal SoC with less than 1% and 6% error, respectively. Individual contributions of the ohmic, charge transfer, and diffusion overpotentials, as well as open-circuit voltage or hysteresis, are quantified for the charge pulse. Neural networks reconstruct IC extrema from the pulse harmonics with less than 1% error. The pulse response therefore reflects internal kinetic parameters and electrode phase transitions which are best uncovered using neural networks. Our results extend the uses of pulsing and suggest novel methods for degradation diagnostics in battery management systems.

Influence of voltage profile and fitting technique on the accuracy of lithium-ion battery degradation identification through the Voltage Profile Model

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

Capacity Degradation and Aging Mechanisms Evolution of Lithium-Ion Batteries under Different Operation Conditions

Since lithium-ion batteries are rarely utilized in their full state-of-charge (SOC) range (0–100%); therefore, in practice, understanding the performance degradation with different SOC swing ranges is critical for optimizing battery usage. We modeled battery aging under different depths of discharge (DODs), SOC swing ranges and temperatures by coupling four aging mechanisms, including the solid–electrolyte interface (SEI) layer growth, lithium (li) plating, particle cracking, and loss of active material (LAM) with a P2D model. Additionally, the mechanisms causing accelerated capacity to drop near a battery’s end of life (EOL) were investigated systematically. The results indicated that when the battery operated with a high SOC range, the capacity was more prone to accelerated degradation near the EOL. Among the four degradation mechanisms, li plating was mainly sensitive to the operation temperature and SOC swing ranges, while the SEI growth was mainly sensitive to temperature. Furthermore, there was an inhibitory interaction between li plating and SEI growth, as well as positive feedback between LAM and particle cracking during battery aging. Additionally, we discovered that the extremely low local porosity around the anode separator could cause the ‘knee point’ of capacity degradation.

A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms

Lithium-ion batteries are a key storage technology for electric vehicles and renewable energy applications. However, the complex degrading behaviour of batteries impacts their capacity and lifetime. Thus, battery capacity loss prediction is crucial for ensuring the longevity, safety, and reliable operation of the battery. This research proposes a smart feature selection (SFS) strategy-based machine learning framework for battery calendar and cyclic loss prediction. The presented methodology selects input parameters from the battery data of the current time step as well as the previous time step which are then utilized for model training and testing. Results demonstrate that the proposed SFS method in combination with the ML algorithms enhances the prediction accuracy and reduces the mean absolute error for all the machine learning algorithms applied in this study. The proposed SFS method is capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for capacity loss of the lithium-ion battery under real EV usage conditions. Furthermore, the results also depict that the performance accuracy of ML methods for battery calendar and cyclic loss prediction improves when combined with the SFS method. Greater improvement in prediction accuracy of battery capacity loss is observed for Gaussian Process Regression (GPR), random forest (RF), and XGBoost methods when applied in combination with the proposed SFS. This is the first-known feature selection-based ML application that is utilized to independently perform battery calendar and cyclic loss prognosis.

Lithium-ion battery degradation diagnosis and state-of-health estimation with half cell electrode potential

Lithium-ion batteries (LiBs) have been widely used in electric vehicles and portable electronics. However, the performance and safety of these applications are highly dependent on degradation of LiBs. In this paper, three contributions have been made to achieve reliable degradation diagnosis and State-of-Health (SOH) estimation: (1) Open-circuit voltage is reconstructed to diagnose degradation modes of LiBs by performing scaling and translation transformations on open-circuit potential curves. (2) A degradation diagnosis model is developed to quantify aging characteristics of LiBs. In this model, a segment of charging data is taken to estimate SOH and the degradation modes in a degradation path. (3) An appropriate voltage range of the charging data is selected to improve model estimation accuracy. Experimental results show that the proposed method can achieve reliable degradation diagnosis and accurate SOH estimation with the maximum error of 1.44%.

Degradation of lithium-ion batteries that are simultaneously servicing energy arbitrage and frequency regulation markets

Lithium-ion batteries are currently deployed around the world to provide both energy intensive and power intensive electricity grid services. Because of this, there is interest in simultaneously servicing multiple markets to maximize the use of both the energy and power operating envelope of a given battery. To evaluate the long-term economic potential of such strategies, many researchers apply degradation curves based on a simple cycling regimen rather than a complex grid services profile. Building upon previous work that examined single service battery degradation, this study evaluates the degradation patterns of simultaneous services by superimposing energy arbitrage and frequency regulation signals. This new methodology was applied to two common lithium-ion chemistries (NMC and LFP) and the results were compared to single service degradation results obtained from the same cell types. Frequency regulation alone caused the least amount of capacity degradation. The simultaneous services increased the degradation rate as the energy arbitrage proportion increased, with the highest degradation rate caused by energy arbitrage alone. Degradation rate is highest throughout the first 1000 cycles and becomes constant after that (up to the 2500 cycles tested), suggesting that as batteries are cycled, they are less sensitive to the severity of the service profile. From this a degradation factor is derived to inform developers on the impacts of different service profiles. Energy efficiency only marginally declined throughout the cycling period. This information will improve the fidelity of economic models by increasing degradation modeling accuracy for simultaneous services.

On the Relations between Lithium-Ion Battery Reaction Entropy, Surface Temperatures and Degradation

Understanding and mitigating the degradation of batteries is important for financial as well as environmental reasons. Many studies look at cell degradation in terms of capacity losses and the mechanisms causing them. However, in this study, we take a closer look at how degradation affects heat sources in batteries, thereby requiring dynamic cooling strategies for battery systems throughout the battery life. In this work, we have studied and compared reversible (entropy-related) and non-reversible heat sources in a commercial LCO-graphite lithium-ion battery (LIB) alongside measuring the surface temperature as a function of the State of Health (SoH). In addition, we studied the effect of different thermal management strategies on both degradation and cooling efficiency. We found that entropic heating plays a major role in overall heat generation. This causes large variations in heat generation and battery temperature over both State of Charge (SoC) and charge versus discharge. The maximum battery temperature increases when the cell degrades as irreversible heat generation increases. Temperature variations over the cell thickness are substantial and increase drastically when the cell degrades. In addition, significant increases in thickness were observed as a result of cell degradation. Furthermore, cycling at elevated temperatures resulted in a larger thickness increase with significant gas production.

Digital twin-long short-term memory (LSTM) neural network based real-time temperature prediction and degradation model analysis for lithium-ion battery

Real-time temperature prediction is essential to circumvent thermal safety issues for lithium-ion batteries (LIB). However, its industrial applications are challenging due to operating temperature, voltage range, capacity degradation, and current rate (C-rate) variations. This study proposes a digital twin (DT) technology and long short-term memory (LSTM) neural network-based method for real-time temperature prediction and degradation pattern analysis. The DT model is established based on lumped thermal equivalent circuits to describe the dynamic thermal behavior of LIB and identify the parameters by calculations. Furthermore, the real-time temperature prediction framework considering voltage, current, and operating temperature is further designed following identifying results, which consists of indicators extraction, correlation analysis, LSTM neural network, and DT model four parts. In addition, the incremental capacity analysis (ICA) method is used to analyze LIB degradation patterns. The experimental results indicate that the primary degradation pattern of the experimental sample is loss of lithium inventory (LLI), and the loss of active material (LAM) appears after 600 cycles. Moreover, the maximum error and root mean square error (RMSE) of the temperature prediction framework is 0.31 °C and 0.17 °C under the constant current (CC) discharging condition, 0.85 °C and 0.47 °C under the dynamic discharging condition, respectively. The results demonstrate that the proposed real-time temperature prediction framework delivers acceptable accuracy for CC discharging and dynamic discharging conditions, which can complete the requirements of practical applications. This study dramatically reduces the response time of temperature prediction and guides optimizing battery thermal management systems (BTMS).

State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization

Owing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working voltage. To improve the accuracy and adaptability of SOE estimation, in the paper, we study the energy state estimation of a EULIB. First, the four-order resistor–capacitance equivalent model of a EULIB is established, and an unscented transformation is introduced to further improve the estimation accuracy of the SOE. Second, a EULIB’s SOE is estimated based on adaptive unscented Kalman filter (AUKF), and the OIR and AE of a EULIB are estimated based on the AUKF. Third, a Takagi–Sugeno fuzzy model is introduced to optimize the OIR and AE of the EULIB, and the SOE estimation method is established based on an adaptive dual unscented Kalman filter (ADUKF). Through simulation experiments, verification, and comparison, energy decayed to 80%, 60%, and 40% of the rated energy, respectively, even with a large initial error; with the initial value of the SOE starting at 100%, 60%, or 20%, the estimated SOE can track the actual value. It can be seen that the method has a strong adaptive ability, and the estimation accuracy error is less than 1.0%, indicating that the algorithm has high accuracy. The method presented in this paper provides a new perspective for SOE estimation of EULIBs.

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

A Wiener Process Model With Dynamic Covariate for Degradation Modeling and Remaining Useful Life Prediction

Most industrial products degrade with usage. To capture degradation patterns and schedule condition-based maintenance actions, it is necessary to model the degradation process and predict the system remaining useful life (RUL). In practice, the operating environments and loading conditions are dynamic and stochastic, which introduces extra randomness in the degradation process. To account for the dynamic environmental impacts, this article proposes a nonlinear Wiener process model with a random time-varying covariate. We model the dynamic covariate with an Ornstein–Uhlenbeck process, and link it to the time-varying degradation rate by an exponential form covariate-effect function. Therefore, the proposed Wiener process has a time-varying degradation rate impacted by the dynamic environment. We propose to estimate the model parameters by maximum likelihood estimation based on given observations of the degradation and covariate. We also propose a simulation-based procedure to obtain the RUL for the proposed model. The model is verified by Monte Carlo simulations. We apply the proposed model to a dust-cleaning fan degradation dataset and a lithium-ion battery degradation dataset, and the results show that the proposed model outperforms the existing degradation models in fitting the real data and RUL prediction.

Early-Stage end-of-Life prediction of lithium-Ion battery using empirical mode decomposition and particle filter

The predictive maintenance is a major challenge for improving battery safety without compromising performance. Its main objective is to predict the end-of-life (EOL) and assess the associated prediction uncertainty. In this paper, we consider this issue and propose a hybrid method combining empirical mode decomposition (EMD) and particle filter (PF) to perform the battery early EOL prediction and its uncertainty assessment. The proposed approach is tested on the two most widely accessible public lithium-ion battery degradation datasets from the Prognostics Center of Excellence at NASA Ames and the Center for Advanced Life Cycle Engineering at the University of Maryland. To avoid the overfitting, a major constraint in the prediction phase, only the residual sequence obtained after EMD decomposition of the raw data is used as the measurement input for PF. Besides, the sum of standard deviation of all intrinsic mode functions (IMFs) is used to determine the noise characteristic for the PF algorithm. Extensive experiments are conducted to show the importance of uncertainty assessment for the early EOL prediction. Although the distance between the true EOL and the mean predicted EOL has no obvious decrease when more operation data is available, the results show a clear decreasing trend of EOL prediction uncertainty when the prediction starts from later operation cycles. Particularly, a strong experimental support is provided for filtering-based prediction methods in the early EOL prediction.