Incremental Capacity Curves Research Articles

SOH Estimation for Lithium-Ion Batteries Based on Health Indicators Extraction and MKRVR

Aiming at the problems of difficult feature extraction and low State of Health (SOH) prediction accuracy of lithium-ion batteries, this paper proposes a SOH estimation approach that involves extracting Health Indicators (HIs) and utilizing mixed kernel function relevance vector regression (MKRVR). In order to accurately and efficiently extract battery HIs, the paper first examines the limitations of extracting HIs based on Incremental Capacity (IC) curves and puts forward an alternative method of HIs extraction based on voltage-capacity (V-C) curves. Following correlation analysis of the HIs, the paper establishes the MKRVR model that combines a range of kernel functions to estimate SOH. To determine the hyper-parameters and weight coefficients of the MKRVR model, the paper integrates Differential Evolution (DE) and Levy flight into the Gray Wolf Optimizer (GWO) to enhance the population diversity and random search ability of GWO. Finally, the paper conducts experimental validation utilizing three distinct battery datasets. The results indicate that the proposed approach outperforms Support Vector Regression (SVR) and Relevance Vector Regression (RVR), with evaluation index values of MAE, MSE, and RMSE all below 1%. Moreover, the coefficients of determination exceed 0.95, which demonstrates the superiority of this approach over other methods.

Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics

Lithium-ion battery (LIB) thickness variation due to its expansion behaviors during cycling significantly affects battery performance, lifespan, and safety. This study establishes a three-dimensional electrochemical-thermal-mechanical coupling model to investigate the impacts of thermal expansion and particle intercalation on LIB thickness variation, respectively. Results indicate that thickness variation induced by particle intercalation predominantly determines LIB expansion behavior, contributing to 92 % of the observed thickness variation. Moreover, the expansion behavior of LIBs across the entire state of charge (SOC) ranges can be categorized into four stages based on the expansion rate, with turning points closely correlating with the positions of peaks in the Incremental Capacity (IC) curve. This phenomenon underscores the nuanced relationship between LIB thickness variation characteristics and SOC. Consequently, this study proposes a novel SOC estimation approach based on Gaussian regression processes utilizing expansion behavior and voltage characteristics. The experimental results indicate that the maximum error does not exceed 0.0076, and the root mean square error (RMSE) remains within 0.0018 for the constant charging/discharging conditions under different current rates. This research provides guidance for expansion mechanism investigation and SOC estimation optimization.

A Hyperparameter-Tuned LSTM Technique-Based Battery Remaining Useful Life Estimation Considering Incremental Capacity Curves

A Hyperparameter-Tuned LSTM Technique-Based Battery Remaining Useful Life Estimation Considering Incremental Capacity Curves

Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization

To enhance the model estimation performance and minimize the possibility of overfitting, we propose a statistical optimization strategy of healthy features (HFs) for estimating the state-of-health (SOH) of lithium-ion batteries (LIBs). Firstly, a series of initial HFs were extracted from the voltage, current, temperature, incremental capacity (IC) curves, and differential thermal voltammetry (DTV) curves according to the battery characteristics. Secondly, the six statistical features (i.e., mean, median, lower quartile, range, upper quartile, and standard deviation) of the initial HFs for each charge cycle were calculated. Thirdly, to minimize the impact of redundant features and noise, the optimal HF set was identified by a comparative analysis among different combinations of the six statistical features. Thus, the battery SOH can be estimated using the dual-kernel Gaussian process regression (GPR), which improves the estimation accuracy and generalization ability of the single-kernel GPR. To prevent estimation errors due to manual adjustments, the GPR hyperparameters were optimized via the northern goshawk optimization (NGO) algorithm. Experiments were conducted on the NASA and Oxford datasets, with most estimation errors below 1%. Experimental results demonstrate the lightweight NGO-Dualkernel-GPR model, by incorporating the statistical feature optimization strategy, achieves exceptional SOH estimation performance for different LIBs.

Quantitative diagnosis of micro-short circuit for lithium-ion batteries considering aging based on incremental capacity curve

Early diagnosis of internal short-circuit faults for preventing thermal runaway in lithium-ion batteries stands as a pivotal task within battery management systems. With the continuous use of electric vehicles, the coexistence of aging and micro-short circuit (MSC) within battery packs is possible. These two faults manifest identical characteristics during discharge, rendering them susceptible to erroneous diagnoses. In this study, a novel fault diagnosis method based on the incremental capacity (IC) curves is proposed, devised to discern aging cells from those afflicted by MSC. This method screens out aging cells by feature parameters of IC curve. Based on the unique correspondence between IC peak and state of charge in this study, the cell that reaches the peak first is designated as the reference cell. By calculating the leakage current based on the temporal divergence of peak arrivals of each cell from this reference, this method estimates the MSC resistance to quantify the degree of the short-circuit fault. A series of experiments involving the presence of aging batteries, simultaneous occurrence of multiple MSC faults, or the evolution of MSC severity indicate the method is feasible and effective for diagnosing aging and MSC faults in battery packs simultaneously.

Physical Model-Based State of Health Understanding of Severely Aged Lithium-Ion Batteries Under Real-World Automotive Operational Life

Lithium ion-battery (LIB) technology, featuring upstanding energy and power density, satisfying lifetime, high round-trip efficiency and very fast dynamics in a reasonably economic package, rapidly became the undisputed ruler of portable power and it is now the main driver of the electrification of transportation sector. However, despite the fully commercial development, understanding and predicting degradation of such devices is still a great challenge for the scientific and technical community, especially when dealing with real-life-operation induced aging. The reusability of such devices in a circular economy perspective is a hot topic to the sector to improve sustainability, but requires understanding “how” batteries are faded rather than only “how much” they are, to enable a physically consistent and second-life-related estimation of residual lifetime.In the present activity, a detailed analysis of high-power LFP (lithium iron phosphate) cells samples operated on IVECO hybrid buses are performed to assess their possible reusability, in the frame of a joint research cooperation. A large batch of cells with different ages (from 9 years old to brand new) and different positions inside the modules are investigated.State-of-art electrochemical diagnostics are performed embedded in a multi-measurement optimized protocol (full discharge, pulse test, electrochemical impedance spectroscopy) developed after a sensitivity optimization for a model-based improved parameter identification [1], as visible in Figure 1. Residual performances are analyzed and compared with limited on-board data collected from vehicles BMS and analyzed by means of an appositely-improved physical modelling platform.Both capacity and power capabilities have sensibly decreased over the cells lifetime (see Figure 2 for the full discharges at 0.1C, 25°C) with a clear pattern attributable to cells position beneath the modules, prompting the importance of a homogeneous thermal management during operation. Power-based state of health of the samples is higher than a capacity-based SoH, foreseeing a possible reuse of the samples in a power-intensive second-life. Residual performance interestingly feature a high consistency with data recently published in [2], relative to similar system despite operated in a completely different geographic area; this strengthens the generalizability of the results.Aged cells diagnostics were interpreted with a previously developed Newman P2D physical model provided with heath exchange, to identify the degradation mechanisms through the EIS-based parameter identification procedure [1] involving both thermodynamic and kinetic aspects.Interpretation of thermodynamic aging modes has been conducted on all the tested cell samples, based on 0.1C thermodynamic analysis and the differential voltage. Incremental capacity curves of aged cells have been reproduced with a PSO-based (particle-swarm-optimization) identification of three ageing parameters: loss of lithium inventory (LLI), mainly corresponding to solid electrolyte interphase (SEI) growth, and loss of active materials at negative (LAMn) and positive (LAMp) electrode, indicating loss of actives sites for the electrochemical reactions. Despite the severely low residual capacity, the model reproduces satisfyingly the features of the aged cells.Interestingly, a linear trend could be identified as common beneath all the samples involved in the analysis, indicating common aging mechanism appearing with different magnitude. As visible in Figure 3, the trend points out a two-steps aging path: (I) a first pathway mainly leading to LLI close to 20%, followed by (II) a second pathway with both LLI and LAMn occurring at the same time. In the literature, similar observations have been already performed by some authors in previous works, with a lower extent of degradation [3,4]. They agree in this interpretation: the first stage is usually associated to the growth of the SEI layer, while the second stage is usually associated to the onset of lithium plating (due to a dense SEI which inhibits the lithium intercalation into graphite). Confirmation of such interpretation is obtained by means of model based interpretation on non-equilibrium measurements, permitting estimation of physical parameters value and enabling identification of aging mechanisms, together with ex-situ measurements based on electrochemical and morpho chemical analyses.Additionally, several consistencies between on-line measurements, such as EIS, and equilibrium capacity loss of cells have been identified, theoretically discussed and tested under further accelerated aging tests, enabling possible strategies of SoH implementation based on fast and informative electrochemical measurements.

ICFormer: A Deep Learning model for informed lithium-ion battery diagnosis and early knee detection

Accurate diagnosis of lithium-ion battery (LIB) degradation is critical for safe and reliable operation in real-world applications. In recent years, data-driven approaches powered by Machine Learning algorithms emerged as a promising solution, among which Deep Learning methods were proven to be effective for various tasks such as State of Charge and State of Health estimation and Remaining Useful Life prediction, however, their application for knee point estimation is still at an early stage. While experimental techniques, were demonstrated to be successful for detecting knees, current methods focus on tracking individual cycles rather than analyzing the overall trajectory of degradation, which could potentially offer better performance. In this paper, we introduce ICFormer, a novel Deep Learning model based on a Transformer encoder that leverages self-attention on the evolution of incremental capacity curves to accurately identify relevant changes in LIB degradation trajectories. The proposed model not only detects knees, but also anticipates them while also outperforming state-of-the-art approaches in diagnosing degradation modes. The effectiveness of the model is validated using both synthetic and experimental data. We demonstrate that the method can provide valuable knowledge on the factors contributing to capacity loss and offer advanced insights for battery management and predictive maintenance strategies.

A new data-driven diagnostic technique for in-situ capacity prediction of lithium-ion batteries

Lithium-ion battery (LIB) charging behavior is of equal importance as discharging with abundant information for the battery management system to determine the state of charge (SOC), state of health (SOH), and state of safety (SOS). However, to the best of the authors' knowledge, little work can be found regarding the fast and accurate prediction of the charging behaviors of LIBs. To fill in this gap, in this work, a data-driven diagnostic technique, long short-term memory in situ capacity prediction (LSTM-ICP), is proposed to predict battery capacity through short-term voltage measurements under constant current charging. Unlike the technique in the previous work, LSTM-ICP does not rely on the interpretation of voltage-time data as incremental capacity (IC) or differential voltage (DV) curves. Thus, the need for differentiating voltage-time data (amplifying the process of measuring noise) and the voltage measurement range including the peak of the IC/DV curve can be ignored and thus significantly shorten the prediction process. The optimization method of the mini-batch gradient descent may reduce the computational complexity and also enhance the randomness of prediction. Results highlight the performance of the proposed diagnostic technique, LSTM-ICP is applied to two datasets, which are composed of four and eight cells. In each case, the evaluation results of different performance indicators in a certain voltage range show that the prediction model has good accuracy and effectiveness.

Degradation study for 18650 NMC batteries at low temperature

This research focuses on the identification and quantification of lithium-ion battery degradation indicators at low temperatures. We studied the cycling aging of 18,650 commercial NMC lithium-ion batteries at 10 °C. For this purpose, we carried out life cycle tests and performed Galvanostatic Intermittent Titration Technique (GITT) tests in the voltage range for the charge and discharge processes for different States of Health (SoH). The diffusion time constant and the ohmic overpotential were determined from GITT for different SoH. As the batteries degrade both parameters increase. A degradation mechanism associated with faradaic effects (increase in ohmic drop overpotential) and a thermodynamic effect associated with changes in the active material (diffusional time constant) is observed. Furthermore, we performed Electrochemical Voltage Spectroscopy studies through Incremental Capacity (IC) curves. IC curves peaks and valleys are associated with battery phase transformations due to aging phenomena. Each peak has a unique peak height, area, and position associated with a degradation mode. This research focuses on the IC curves derived from the discharge capacities at 3A and OCP (Open Circuit Potential) to study the thermodynamic and faradaic effects separately. The peak at 3.6 V and 3.4 V for the 3A and OCP studies, respectively, is the main feature for the detection of the degradation mechanism. The valley in the OCP curves intensifies with the decrease in SoH and is shifted to lower potentials, an effect that is not observed in the 3A curves. So, we could associate this with thermodynamic effects, Loss of Lithium Inventory (LLI) and Loss of Active Material (LAM) of both the positive electrode and the negative electrode.

State of health estimation of lithium-ion batteries based on multi-feature extraction and temporal convolutional network

Accurate estimation of the state of health (SOH) is vital for maintaining the safe and stable operation of lithium-ion batteries. However, the utilization of extant data-driven methods for SOH estimation often poses a dichotomy between the diversity of feature selection and the intricacy of network models. This study proposes an estimation methodology combining multi-feature extraction with temporal convolutional network (TCN). The experimental curves of charging and discharging, alongside the incremental capacity curve of lithium-ion batteries, were subjected to principal component analysis to extract Class I features. Class II features were derived by performing empirical mode decomposition on the battery capacity decay curve, thereby securing multi-feature data. Moreover, a channel attention module based on TCN was utilized to process multi-dimensional features and select appropriate weights. Concurrently, to enhance the adaptive threshold training ability of the model with multiple input parameters, a residual shrinkage network was introduced. The SOH estimation of lithium-ion batteries was ascertained by training and processing these multi-features using an improved TCN. The results were subsequently compared with long short-term memory and conventional TCN models. The proposed model demonstrated a mean absolute percentage error of 1.47 % in estimating the SOH of lithium-ion batteries.

Experimental study on self-heating strategy of lithium-ion battery at low temperatures based on bidirectional pulse current

Preheating is an effective solution to the severe degradation of lithium-ion battery (LIB) performance at low temperatures. In this study, a bidirectional pulse-current preheating strategy for LIBs at low temperatures without external power is proposed, which involves the incorporation of a direct current/direct current converter and a series of resistances, inductances, and switches. The effects of ambient temperature, initial state of charge, and preheating strategy on the voltage and current evolution, heating rate, and heating efficiency are experimentally analysed in a climatic chamber, in addition to the effects of various preheating strategies on battery degradation. A comparative analysis is conducted using the classical pulse self-heating strategy. The results indicate that the bidirectional pulse-current preheating strategy enables preheating under an ambient temperature of −15 °C at a rate of 6.38 °C/min and affords a thermal efficiency of 31.9%. By contrast, the pulse heating method affords a heating rate of 3.46 °C/min and a thermal efficiency of 24.2%. As the battery temperature decreases, both the charge/discharge switching period and heating rate decrease, whereas the thermal efficiency and energy consumption ratio improve. No significant degradation occurs after 30 heating cycles, and the bidirectional pulse-current preheating strategy is demonstrated through capacity testing, incremental capacity curves, and electrochemical impedance spectroscopy testing. This study proposes a new approach for preheating LIBs internally and provides experimental evidence for a bidirectional-pulse preheating strategy.

A Prediction Framework for State of Health of Lithium-Ion Batteries Based on Improved Support Vector Regression

As one crucial function of battery management system (BMS), the state of health (SOH) prediction of lithium-ion battery is of great significance to system safe operation and battery’s service life. This paper proposes a framework for SOH prediction, which includes the feature points extraction and SOH prediction. Firstly, based on the incremental capacity (IC) curve, the improved incremental capacity (IIC) curve is deduced by taking the derivative of the IC curve, and the grey relational analysis (GRA) is adopted to select the four feature points with the highest grey relational grade (GRG). Then, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of the support vector regression (SVR) for more precise SOH prediction. Finally, experiments are carried out and the results show that the proposed feature points extraction method based on the IC/IIC curves and GRA is efficiently to improve the SOH prediction accuracy. Furthermore, compared with three traditional algorithms, the ISSA-SVR can restrict the SOH prediction error within 1.7%, and it also shows the proposed SOH prediction framework has strong robustness and high universality.

A new method to perform Lithium-ion battery pack fault diagnostics – Part 1: Algorithm development and its performance analysis

The successful integration of Li-ion batteries in the automotive industry paved the way for their integration into a new generation of aircraft with zero emission. However, potential hazards associated with Li-on batteries are a major concern for the safety-critical aerospace sector. Removing or reducing the hazards from Li-ion batteries while preferable, requires fundamental changes to the battery chemistry. Another way to address this is the implementation of fault diagnostics and prognostics for forecasting or detecting the presence of faults before an aircraft is airborne. This article, part of a three-part series, presents a novel algorithm exploiting the data collected while charging a battery pack for diagnosing faults. The algorithm works off-board, thus not adding any computational burden or weight to the aircraft while minimising aircraft turn-around time, which is crucial for commercial flight operation. This paper presents the underlying method of the algorithm involving the computation of resistance, charge energy and incremental capacity curve using the charging data followed by a combined horizontal and vertical comparative scheme for diagnosis of faults. The performance of the proposed algorithm was validated via module-level experiments considering three cell inactivation faults. Results indicated that the algorithm successfully segregated faulty and unfaulty cells in multiple test scenarios with different number of faults in a laboratory-assembled module. Upon testing the algorithm in experiments conducted using a real-life aerospace battery module, there was a slight reduction in the performance, attributed to greater thermal variation. Nevertheless, the results indicated promising prospects for practical implementation in eVTOL batteries.

Health Diagnosis for Lithium-Ion Battery by Combining Partial Incremental Capacity and Deep Belief Network During Insufficient Discharge Profile

Accurate state of health (SOH) estimation of lithium-ion batteries provides scientific basis for secure operation and stepwise utilization in on-board powertrain. However, the variable discharge depths inevitably reduce the elasticity and precision of the estimation method in prevalent partial discharge situations. In this work, multiple candidate health indicators are extracted from the peaks and valleys of the partial incremental capacity curves and screened firstly. Specifically, the fine-tuning process of deep belief network (DBN) based on particle swarm optimization are elaborated and synthetic comparison in terms of error and time consumption with three classical deep networks is performed. To better accommodate practical scenarios, three datasets of the LiFePO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{4}$</tex-math></inline-formula> cells under different discharge depth are applied to verify the proposed framework. The experimental results indicated that the presented framework is feasible and the prediction error can be minimized to less 2%.

Remaining useful life prediction of lithium-ion batteries based on peak interval features and deep learning

Anticipating the lifespan of a lithium-ion battery is a challenging task due to the unstable external working conditions and complex internal response. To enhance the accuracy of the remaining usable life prediction of lithium-ion batteries, this study proposes a feature extraction method that relies on the peak value of the incremental capacity (IC) curve for a data-driven prediction approach. This method can significantly reduce the amount of data required for training the model. Furthermore, the grey correlation method (GCM) is employed to select features that have a high correlation with the target value, thereby further reducing the amount of data required for training the model. It is explored how well characteristics may be extracted from various peak intervals. The time series prediction-capable long short-term memory (LSTM) network is used to create the data-driven model. Finally, the experimental findings demonstrate that the proposed data-driven model's prediction error is smaller than 1.18 %. The proposed data-driven in this work is found to have a superior prediction impact on the same data set in different deep learning methods when compared to other data-driven approaches.

Early prediction of battery lifetime based on graphical features and convolutional neural networks

Accurate lifetime prediction of lithium-ion batteries in the early cycles is critical for timely failure warning and effective quality grading. Convolutional neural network (CNN), with excellent performance in feature extraction, has gained increasingly attentions in battery prognostics. However, since degradation test normally takes years to complete, employing end-to-end CNNs directly for battery lifetime prediction is impractical due to the limited number of available training samples and the scarcity of features in the early cycles. Instead of directly feeding the raw data, in this work, we propose to use graphical features for early lifetime prediction. Three feature curves, including capacity-voltage curve, incremental capacity curve, and capacity difference curve are used to construct graphical features. Specifically, the incremental capacity curve and capacity difference curve are derived from capacity-voltage curve, aiming to extract more information from both intra-cycle and inter-cycle perspectives. The evolution patterns of these feature curves over the initial 100 cycles show evident correlations with battery lifetime, and are termed as the graphical features. The three graphical features, after some proper transformation, are stacked into a three-channel image before feeding to the CNN model. Five classical CNNs, with different structures and key parameters, are investigated for battery lifetime prediction. Comparative experiments are conducted to study the influence of different feature combinations, voltage segments, and discharge cycles on the prediction performance. Experimental results demonstrate that simple CNNs with only a few convolutional layers can achieve satisfying prediction results. Additionally, networks with rectified linear unit are shown to outperform those with other activation functions.

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

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

A state-of-health estimation method based on incremental capacity analysis for Li-ion battery considering charging/discharging rate

Incremental capacity analysis (ICA) is an effective method for analyzing the degradation mechanism and estimating the state of health (SOH) of lithium-ion batteries. However, the incremental capacity (IC) curve is sensitive to the initial state of charge (SOC) and the charging/discharging rate (CDR), which will lead to the deformation of the IC curve and the displacement of the health feature. In this paper, an ICA-based SOH estimation method that considers the charging/discharging rate is proposed, which can achieve accurate SOH estimation. Furthermore, a method for quickly obtaining a smooth IC curve based on finite-time differentiator is developed, which realizes the online processing of high-frequency sampling data. Experiments with different initial SOCs and charging/discharging rates were designed and performed. The degradation data during standard charging are used to construct the mapping relationship between the health feature and SOH. Considering different initial SOCs, health features without being affected by different charging/discharging rates are selected for SOH estimation. The validation results show that the SOH estimation errors are within 3% for batteries at different aging levels.

A feature extraction approach for state-of-health estimation of lithium-ion battery

Accurate state-of-health (SOH) estimation is essential to ensure the reliable and safe usage of lithium-ion batteries (LIBs). A novel health feature extraction approach is proposed in this manuscript for battery SOH estimation. Firstly, the degradation data are collected from LIBs with two different life stages, and then the discrete incremental capacity (IC) curve is obtained under different constant voltage intervals dv. The corresponding charging voltage range with obvious variation trend of IC is selected and divided into several subintervals with Δv. The average IC of each subinterval is obtained. Furthermore, the consistency between the average IC of each voltage subinterval and battery capacity is analyzed and evaluated based on raw discrete IC curve. The average IC with the most consistent in relation to battery capacity degeneration is selected as the health feature. The impact of varying dv and Δv values on the feature is conducted based on Spearman correlation analysis, and the health feature with maximum Spearman correlation coefficient is used to build battery SOH estimation model. Finally, two SOH estimation models and comparative analysis of the performance between proposed health feature and other accepted features are utilized to verify the proposed health feature extraction approach. The results demonstrate that our extracted health feature effectively reveals the battery performance degeneration.

Experimental Study on High-Temperature Cycling Aging of Large-Capacity Lithium Iron Phosphate Batteries

Large-capacity lithium iron phosphate (LFP) batteries are widely used in energy storage systems and electric vehicles due to their low cost, long lifespan, and high safety. However, the lifespan of batteries gradually decreases during their usage, especially due to internal heat generation and exposure to high temperatures, which leads to rapid capacity degradation. In-depth research is needed on the degradation characteristics of large-capacity LFP batteries under high temperatures. To study the degradation characteristics of large-capacity LFP batteries at high temperatures, a commercial 135Ah LFP battery is selected for 45°C high-temperature dynamic cycling aging experiments and 25°C reference performance experiments. A detailed analysis of the degradation process is conducted by examining the patterns of changes in charge-discharge voltage curves, capacity, internal resistance, open circuit voltage (OCV), and incremental capacity curve. The study uncovers that the OCV displays diverse degradation patterns at different states of charge (SOC). Furthermore, it identifies the loss of lithium inventory and active material as the fundamental factors contributing to the degradation observed during high-temperature cycling. This study provides references for developing battery life prediction algorithms and designing long-cycle-life battery cells.