Lithium-ion Battery Degradation Research Articles

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.

Probing Degradation in Lithium Ion Batteries with On-Chip Electrochemistry Mass Spectrometry.

The rapid uptake of lithium ion batteries (LIBs) for large scale electric vehicle and energy storage applications requires a deeper understanding of the degradation mechanisms. Capacity fade is due to the complex interplay between phase transitions, electrolyte decomposition and transition metal dissolution; many of these poorly understood parasitic reactions evolve gases as a side product. Here we present an on-chip electrochemistry mass spectrometry method that enables ultra-sensitive, fully quantified and time resolved detection of volatile species evolving from an operating LIB. The technique's electrochemical performance and mass transport is described by a finite element model and then experimentally used to demonstrate the variety of new insights into LIB performance. We show the versatility of the technique, including (a) observation of oxygen evolving from a LiNiMnCoO2 cathode and (b) the solid electrolyte interphase formation reaction on graphite in a variety of electrolytes, enabling the deconvolution of lithium inventory loss (c) the first direct evidence, by virtue of the improved time resolution of our technique, that carbon dioxide reduction to ethylene takes place in a lithium ion battery. The emerging insight will guide and validate battery lifetime models, as well as inform the design of longer lasting batteries.

A neural-driven stochastic degradation model for state-of-health estimation of lithium-ion battery

Due to usage for a long time, lithium-ion battery degradation issues may endanger security operations for service equipment. As batteries usually operate under uncertain usage profiles and experience random aging processes, accurately estimating the battery state of health (SOH) can be challenging. Most existing SOH estimation methods are either empirical knowledge-based with complicated parameters or serve as a black box neural network-based without stochastic nature. To address this limitation, a neural-driven stochastic degradation model for battery health estimation is proposed to ensure the security and reliability of the operation. Firstly, the parameters of a stochastic process are estimated by martingale methodology to describe battery degradation characteristics. Then the optimized sequence-to-sequence (Seq2Seq) neural network is utilized to develop a mapping model in order to update drift coefficient online. Concurrently, the martingale estimating function is used to obtain the diffusion coefficient function. Finally, the transition probability density function of SOH estimation is derived. Comparative experiments are carried out on different battery datasets to show the effectiveness of the proposed model against traditional Wiener process-based models and existing methods in SOH estimation. The model proposed provides an effective solution for battery SOH estimation of energy storage equipment.

Analysis of hybrid active-passive prismatic Li-ion battery thermal management system using phase change materials with porous-filled mini-channels

The rapid expansion of electric vehicles (EVs) has created a significant demand for lithium-ion batteries (LIBs). However, ensuring safety and preventing thermal degradation of LIBs pose challenges to their overall performance and lifespan. Efficient battery thermal management systems (BTMS) are crucial in achieving long-lasting batteries with high energy density. Thermal management procedures are categorized into active and passive approaches. While active cooling methods are crucial, they consume power. Instead, passive cooling methods are not as influential in fulfilling all roles. This study examines a combination of active (mini-channel) and passive (PCM heat sink) techniques. The effects of employing a porous medium inside the mini-channel are also analyzed to enhance the heat exchange. Influences of different factors, including Darcy number, material, the thickness of porous media, and inlet velocity of mini-channel flow in multiple discharge rates (1C, 2C, 3C), are examined. Outcomes show the PCM heat sink itself can lessen the maximum temperature by about 4.2 to 5 K in different inlet velocities. The hybrid porous-filled channel and PCM heat sink can mitigate the maximum temperature by about 13 K in different inlet velocities and at the maximum discharge rate (3C) at the expense of about 14 % higher pumping power in different conditions.

Inhomogeneous Degradation in Lithium-Ion Batteries: The Effect of Thermal Gradients

Understanding lithium ion battery degradation is a consequence of multiple, tightly coupled and non-linear degradation mechanisms. The interplay between these degradation mechanisms, and how the cell is used, gives rise to path dependent behaviour, and hence data driven approaches to model behaviour and predict lifetime rapidly become prohibitively expensive. Therefore, a physics based approach to model degradation is desirable.In addition, all of the degradation mechanisms are a function of temperature, which adds another variable to the path dependency. To make matters worse, all cells experience internal thermal gradients during operation, which means the path dependency for different regions within a battery will be different. This leads to inhomogeneous degradation. After a few cycles, the degradation pathway of each region will then be affected not just by the thermal gradients and local current density but also by the state-of-health of that region, leading to complicated emergent behaviour.Solving a physics based model with multiple degradation mechanisms, in a detailed 3-dimensional thermally coupled discretised model is also prohibitively expensive. Therefore, in this paper we present a thermally coupled distributed equivalent circuit model, with degradation functions built in, so each node can degrade at different rates, as a function of the operating conditions at that node. We have reproduced experimental data previously published, showing the effect of thermal gradients on degradation for two different cooling strategies for a tab and surface cooled pouch cell. For the first time reproducing the positive feedback mechanism responsible for accelerated degradation for surface cooling.The results of this work show that thermal gradients cannot and should not be ignored, and degradation models that do not take them into account have a significant risk of overfitting and leading to incorrect conclusions.

Solvent-Driven Degradation of Ni-Rich Cathodes Probed by Operando Gas Analysis

High-capacity Ni-rich layered metal oxide cathodes are highly desirable to increase the energy density of lithium-ion batteries. However, these materials suffer from poor cycling stability, which is exacerbated by increased cell voltage due to higher interfacial reactivity than their lower Ni-content analogues. Here, we study the pivotal role of electrolyte solvents in determining the interfacial reactivity at charged LiNi0.33Mn0.33Co0.33O2 (NMC111) and LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes by using both single-solvent model electrolytes and the mixed solvents used in commercial cells (1, 2). Operando gas analysis reveals that the solvent-dependent reactivity for Ni-rich cathodes is related to the extent of lattice oxygen release and accompanying electrolyte oxidation. We demonstrate here the detrimental effect of ethylene carbonate (EC), a core component in conventional electrolytes, when NMC811 is charged above 4.3 V vs Li/Li+ - the observed onset potential for lattice oxygen release. Oxygen loss is enhanced by EC-containing electrolytes and correlates with more electrolyte oxidation/breakdown and cathode surface degradation, which increase concurrently above 4.4 V. The gas analysis also reveals the crosstalk effect caused by the products of the nascent oxygen from the NMC811 lattice and the electrolyte solvent, showing anodic reactivity. In contrast, NMC111, which does not release oxygen up to 4.6 V, shows a similar extent of degradation irrespective of the electrolyte. Higher lattice oxygen release with EC-containing electrolytes is coupled with higher interfacial impedance, a thicker oxygen-deficient rock-salt surface reconstruction layer, more electrolyte solvent and salt breakdown. Oxygen loss is suppressed in the EC-free electrolyte, highlighting the incompatibility between Ni-rich cathodes and conventional electrolyte solvents. W. M. Dose et al., Onset Potential for Electrolyte Oxidation and Ni-Rich Cathode Degradation in Lithium-Ion Batteries. ACS Energy Letters 7, 3524-3530 (2022).W. M. Dose et al., Electrolyte Reactivity at the Charged Ni-Rich Cathode Interface and Degradation in Li-Ion Batteries. ACS Applied Materials & Interfaces 14, 13206-13222 (2022). Figure 1

Modeling and Analysis of Coupled Thermal and Mechanical Capacity Degradation in Silicon-Based Lithium-Ion Batteries

Large-format cells are gaining popularity for battery electric vehicles (BEVs) to maximize volumetric and gravimetric energy densities but are associated with challenges in ensuring adequate thermal management. Spatial current density and temperature variations negatively affect overall cell utilization and lifetime.In addition, the mechanical stresses generated within the silicon-based battery electrodes also cause aging and resultant capacity loss. The coupled effects of the thermal and mechanical factors could thus particularly exacerbate cell capacity degradation. Rigorous mechano-thermo-electrochemical models for battery degradation can be a powerful complementary tool to understand these phenomena better. Simulating performance scenarios can significantly improve cell design and experimentation while reducing time and cost commitments. Model-based design approaches thus have the potential to yield substantial economic benefits.This work shall serve as an extension to the previously developed thermal degradation model for non-uniform aging in lithium-ion batteries by coupling with mechanical models predicting the effects of volume change within the batteries over cycling. The observed capacity fade trend for a given battery is ultimately the result of an interplay between multiple degradation mechanisms, which depend on a range of design and operating parameters. The errors between the model predictions and experimental data can provide information on the suitability of a given model and the relative prevalence of a given degradation mechanism, with functional and practical benefits, as there exists significant variation in the parameters, mechanisms, and predictive capability of models in the literature. In addition, a rigorous model of volume changes increases the accuracy of predictions of dynamic stress-strain profiles, which in turn helps improve predictions of associated mechanical degradation phenomena. As a first step towards including mechanical effects, the current model builds on the previously reported studies for modeling volume change in porous electrodes due to intercalation (deintercalation) during lithiation (delithiation). This volume change leads to dimensional and porosity changes in the porous electrodes. Furthermore, these mechanical models will help predict generated stresses and strain profiles within the battery and will be related to models of resultant degradation, especially in the case of silicon/graphite electrodes characterized by substantial volume changes.

Understanding Lithium-Ion Battery Degradation through P2D Model Parameters Evolution

Despite its commercial level, lithium-ion battery ageing is still a hot topic in the research community. Indeed, the relation between ageing and the operating conditions and loads in a real-life operation is under investigation and the corresponding residual life prediction is still a challenging task.In this work, a combined approach with experiments and modeling is applied. A wide driving cycle campaign is carried out, involving 12 different profiles to test different combinations of temperature, C-rate, SoC and DoD, whose values were defined after a literature review about the most common values in a real application, and highlight their effect on battery ageing. Two calendar ageing campaigns at the same temperatures are also conducted to quantify the acceleration of the degradation rate due to cycling and analyse if the degradation path is different.For the interpretation of the results, the P2D model is employed. No explicit description of the main degradation mechanisms is applied. Indeed, the degradation is described through the variation of the physical parameters of the usual model implementation, like a reduction of lithium inventory or active electrode material or a variation of reaction rates and diffusion constants. A specific sequence of tests has been developed as check-up procedure. As detailed in 1, it is a trade-off between testing time and the operating conditions that maximise the identifiability of the parameters of the model.Therefore, the parameter identification problem is performed periodically upon check-ups and parameters trend over time/cycles is derived. Since it is a physical model, every parameter has a physical meaning and its evolution can be associated to a certain range of degradation phenomena. In this way, the model can support the identification of the most likely phenomena that are ongoing in the battery and the value of the parameters constitutes a new concept of state of health, more comprehensive than the residual capacity value.Finally, the model interpretation is proved in two ways. First, a validation dataset is provided, to evaluate the capabilities of the calibrated model in reproducing the behaviour of an aged cell under different operating conditions with respect to the check-up procedure. Then, some cells undergo post-mortem analyses to verify the origin of degradation and measure the values of the physical parameters, like optical and chemical analyses, q-OCP discharges of harvested electrodes in coin cell setup for thermodynamic parameters or EIS for kinetic parameters.This approach, though not suitable on-board, provides a novel physics-based method for a reliable analysis of lithium-ion batteries degradation. 1 G. Sordi, C. Rabissi, A. Casalegno, Reliable thermal-physical modeling of lithium-ion batteries: consistency between high-frequency impedance and ion transport. Under Review

A Two-State-Based Hybrid Model for Degradation and Capacity Prediction of Lithium-Ion Batteries with Capacity Recovery

The accurate prediction of Li-ion battery capacity is important because it ensures mission and personnel safety during operations. However, the phenomenon of capacity recovery (CR) may impede the progress of improving battery capacity prediction performance. Therefore, in this study, we focus on the phenomenon of capacity recovery during battery degradation and propose a hybrid lithium-ion battery capacity prediction framework based on two states. First, to improve the density of capacity-related information, the simultaneous Markov blanket discovery algorithm (STMB) is used to screen the causal features of capacity from the initial feature set. Then, the life-long cycle sequence of batteries is partitioned into global degradation regions and recovery regions, as part of the proposed prediction framework. The prediction branch for the global degradation region is implemented through a long short-term memory network (LSTM) and the other prediction branch for the recovery region is implemented through Gaussian process regression (GPR). A support vector machine (SVM) model is applied to identify recovery points to switch the branch of the prediction framework. The prediction results are integrated to obtain the final prediction results. Experimental studies based on NASA’s lithium battery aging data highlight the trustworthy capacity prediction ability of the proposed method considering the capacity recovery phenomenon. In contrast to the comparative methods, the mean absolute error and the root mean square error are reduced by up to 0.0013 Ah and 0.0043 Ah, which confirms the validity of the proposed method.

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 Degradation Empirical-Model-Free Battery End-of-Life Prediction Framework Based on Gaussian Process Regression and Kalman Filter

Predicting the battery’s end-of-life (EOL) with uncertainty quantification is critical for ensuring system safety and reliability. This paper presents a hybrid framework for battery EOL prediction and its uncertainty assessment based on Gaussian process regression (GPR) and Kalman filter (KF). First, a KF-based empirical-model-free state tracking phase is applied for the available partial battery degradation data. Then, the original time series forecasting problem of degradation curves is converted to the prediction of the virtual degradation rate and acceleration. Next, the prediction of the virtual degradation rate and acceleration is executed by the iterative GPR multi-step ahead prediction strategy with moving sliding windows. Finally, the uncertainty assessment is carried out based on the sliding window length determination process. The effectiveness of our proposed method is validated on the open-source lithium-ion battery degradation dataset from the University of Oxford. Extensive EOL prediction tests have been carried out from 40% (early-stage), 60% (middle-stage), and 80% (late-stage) of the dataset, respectively. Compared with the popular EOL prediction method within particle filter framework, the predicted mean EOL cycle by our method is closer to the true value with a smaller range of prediction uncertainty.

Understanding the Knee Point of Aged Lithium-Ion Batteries: A Physics-Based Modeling of Electrolyte Dry-Out and Gas Bubble Entrapment

This research addresses the performance and degradation characteristics of lithium-ion batteries (LIBs), focusing on the implications of electrolyte depletion and gas bubble entrapment within the cell. Using a comprehensive electrochemical-thermal model, this study provides simulation results of electrolyte depletion and gas bubble entrapment and their influence on the lifespan of lithium-ion batteries (LIBs). Th study notably explores the battery life beyond the critical “knee-point” - a transition point marking the shift from linear to accelerated capacity decline. An intricate exploration of the relationship between electrolyte saturation levels and various battery parameters, including performance, thermal behavior, and transport properties, is carried out through a detailed mathematical model. The findings highlight the critical impact of electrolyte levels on LIB performance and thermal behavior. It also shows that electrolyte depletion can facilitate lithium plating. Providing insights into electrolyte depletion and gas production processes paves the way for strategies to enhance battery life, safety, and performance, ultimately fostering a deeper understanding of LIB degradation mechanisms.

An electrochemical-thermal-aging effects coupled model for lithium-ion batteries performance simulation and state of health estimation

Numerous interrelated coupled aging mechanisms contribute to the degradation of lithium-ion batteries (LIBs), making it challenging to confirm the influence of aging effects on the performance and state of health (SOH). Inspired by electrochemical impedance spectroscopy (EIS) analysis and observed morphological changes, this paper considers double layer resistance increase, solid electrolyte interface (SEI) film growth, and cathode aggregate crack propagation as the main aging effects, and the influence of temperature is considered, leading to development of an electrochemical-thermal-aging coupled model. The model achieves high-precision simulation for different temperature and aging conditions by updating the model parameters based on the aging effects and internal temperature. Compared with experimental data, the relative error of voltage simulation is within 1%. Additionally, the aging effects parameters exhibit a consistent trend with data analysis and mechanism inference, and this strong correlation is beneficial for estimating SOH. The estimation error of SOH based on four linear regression methods is less than 0.5%. This novel electrochemical-thermal-aging effects coupling model addresses two major challenges in battery management system (BMS): performance simulation and SOH estimation under real-world operating conditions.

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.

Enhancing real-time degradation prediction of lithium-ion battery: A digital twin framework with CNN-LSTM-attention model

Lithium-ion batteries (LIBs) have gained widespread usage in electric vehicles (EVs) due to their high energy density, long cycle life, and environmental friendliness. However, as LIBs undergo repeated charging and discharging cycles, they experience performance degradation. When the rated capacity of LIBs drops to approximately 80 %, retirement becomes necessary. Therefore, accurately determining real-time battery degradation is of paramount importance. This study presents a digital twin framework for analyzing and predicting LIB degradation performance. Within this framework, the back propagation neural network (BPNN) is employed to predict and complete the partial discharge voltage curve of the actual battery cycle. Building upon this, in conjunction with the battery's state of charge (SOC), the convolutional neural networks-long short term memory-attention (CNN-LSTM-Attention) model is utilized to real-time forecast the maximum available capacity of LIBs and reveal the battery's degradation state. Experimental results demonstrate a 99.6 % accuracy in completing the partial discharge voltage. Moreover, the prediction accuracy for maximum available capacity surpasses 99 % with a maximum error of less than 3 mAh. Thus, this research substantiates the efficacy and practical applicability of the proposed approach.

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.