Life Of Lithium-ion Batteries Research Articles

Electrochemically Dealloying Engineering toward Integrated Monolithic Electrodes with Superior Electrochemical Li-Storage Properties.

Integrated monolithic electrodes (IMEs) free of inactive components demonstrate great potential in boosting energy-power densities and cycling life of lithium-ion batteries. However, their practical applications are significantly limited by low active substance loading (< 4.0mgcm-2 and 1.0gcm-3), complicated manufacturing process, and high fabrication cost. Herein, employing industrial Cu-Mn alloy foil as a precursor, a simple neutral salt solution-mediated electrochemical dealloying strategy is proposed to address such problems. The resultant Cu-Mn IMEs achieve not only a significantly larger active material loading due to the in situ generated Cu2O and MnOx (ca. 16.0mgcm-2 and 1.78gcm-3), simultaneously fast transport of ions and electrons due to the well-formed nanoporous structure and built-in Cu current collector, but also high structural stability due to the interconnected ligaments and suitable free space to relieve the volume expansion upon lithiation. As a result, they demonstrate remarkable performances including large specific capacities (> 5.7mAhcm-2), remarkable pseudocapacitive effect despite the battery-type constitutes, long cycling life, and good working condition in a lithium-ion full cell. This study sheds new light on the further development of IMEs, enriches the existing dealloying techniques, and builds a bridge between the two.

Analytical computation of stress intensity factor for multi-physics problems

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

Unraveling the relationship between the mineralogical characteristics and lithium storage performance of natural graphite anode materials

Anode materials play critical role in cycling life of lithium-ion batteries in practical applications such as electric vehicles (EVs), portable electronic devices, and large-scale power grids. Natural graphite is one of the most successful anodes for commercial lithium-ion batteries due to its high theoretical capacity and low cost and low operating voltage. The mineralogical properties of graphite minerals have an important effect on the electrochemical performance of graphite anode, while their relationship remains ambiguous. In this work, natural graphite samples from four main graphite mining areas in China were selected as the research object, and their mineralogical characteristics were characterized by XRD, SEM, Raman, ICP, SEM, FIB, BET and other test methods. The properties of crystallinity, morphology, surface/phase defects, impurities, and specific surface area of graphite were studied. The results show that the cyclic stability of graphite is mainly affected by bulk defects and crystallinity, and the initial coulombic efficiency is mainly affected by surface defects and specific surface area. Graphite samples with appropriate surface defects, fewer volume defects, appropriate crystallinity, and small specific surface area have better electrochemical performance. It is hoped that this work should guide the manufacture and modification of the natural graphite anodes and promote its wider application in lithium-ion batteries.

Enhancing Electrochemical Stability of Silicon-Carbon and NMC based Lithium-Ion Batteries through the Synergistic Impact of Charge Balance and Direct-Contact Pre-Lithiation Strategy

The commercial feasibility of silicon (Si) anodes (≥ 30%) in full-cell systems is constrained by poor cyclic stability and capacity balancing due to continuous active lithium (Li) consumption in each cycle. We proposed a capacity balancing approach by adding additional Li at the cathode side (i.e., ratio of capacity at negative to positive electrode (N/P) ∼ 0.9) and anode side (i.e., pre-lithiated Si) before full-cell fabrication. This approach provided a capacity-balanced full-cell with 91.7% of initial Coulombic efficiency (ICE) and stability up to 50 cycles. In-situ electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT) analysis have been utilized to examine the interface of bare and balanced full cells. In a large format cell, electrolyte wettability is an issue and it has been investigated even at high temperature (50 °C). The post-cycle investigation also indicates the Si particles retained their integrity after cycling. A 110 mAh pouch cell has been constructed, and the pouch cell demonstrated exceptional cyclic stability up to 200 cycles, with a capacity retention of 83.2% at a current rate of 0.1 C. These findings present a capacity balancing approach for high-loading silicon-based anodes that eventually can scale up to high-quality and long-cycle life lithium-ion batteries.

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods.

A Lithium-Ion Battery Remaining Useful Life Prediction Model Based on CEEMDAN Data Preprocessing and HSSA-LSTM-TCN

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for reducing battery usage risks and ensuring the safe operation of systems. Addressing the impact of noise and capacity regeneration-induced nonlinear features on RUL prediction accuracy, this paper proposes a predictive model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) data preprocessing and IHSSA-LSTM-TCN. Firstly, CEEMDAN is used to decompose lithium-ion battery capacity data into high-frequency and low-frequency components. Subsequently, for the high-frequency component, a Temporal Convolutional Network (TCN) prediction model is employed. For the low-frequency component, an Improved Sparrow Search Algorithm (IHSSA) is utilized, which incorporates iterative chaotic mapping and a variable spiral coefficient to optimize the hyperparameters of Long Short-Term Memory (LSTM). The IHSSA-LSTM prediction model is obtained and used for prediction. Finally, the predicted values of the sub-models are combined to obtain the final RUL result. The proposed model is validated using the publicly available NASA dataset and CALCE dataset. The results demonstrate that this model outperforms other models, indicating good predictive performance and robustness.

Extending Calendar Life of Si-Based Lithium-Ion Batteries by a Localized High Concentration Electrolyte

Extending Calendar Life of Si-Based Lithium-Ion Batteries by a Localized High Concentration Electrolyte

Low-expansion Si anode with an ultrathin structure for long life lithium-ion batteries

While silicon (Si) has garnered extensive research attention in lithium-ion batteries (LIBs) owing to its high specific capacity (∼4200 mAh g−1), its application is hindered by its large volume expansion during cycling. Here, we present the fabrication of a low- expansion Si anode with an ultrathin structure on a Cu foil using a physical vapor deposition method. The uneven structure of the Cu foil effectively provided a suitable volume expansion space and further buffered the interface stress during the lithiation process. Moreover, the nanosized structure of Si can withstand its own volume-change stress during cycles. The high-strength Si thin film ensures the stability of a solid electrolyte interphase (SEI) and provides a short ion transport distance, which further improves the electrochemical performance. The obtained Si anode exhibited an initial specific capacity of 2499 mAh g−1, an ultralong cycling stability (99.77% of capacity retention after 300 cycles at 0.5 C), and a rate capability of 1598 mAh g−1 at 5 C. These results suggest that the design of the ultrathin structure can provide an efficient strategy to address the stress rupture and continuous growth of the SEI in Si-based anode materials during the lithiation and delithiation processes.

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.

State of Health Estimation and Remaining Useful Life Prediction of Lithium-Ion Batteries by Charging Feature Extraction and Ridge Regression

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles.

Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network

Accurate Remaining Useful Life (RUL) prediction of lithium batteries is crucial for enhancing their performance and extending their lifespan. Existing studies focus on continuous or relatively sparse datasets; however, continuous and complete datasets are rarely available in practical applications due to missing or inaccessible data. This study attempts to achieve the prediction of lithium battery RUL using random sparse data from only 10 data points, aligning more closely with practical industrial scenarios. Furthermore, we introduce the application of a Flexible Parallel Neural Network (FPNN) for the first time in predicting the RUL of lithium batteries. By combining these two approaches, our tests on the MIT dataset show that by randomly downsampling 10 points per cycle from 10 cycles, we can reconstruct new meaningful features and achieve a Mean Absolute Percentage Error (MAPE) of 2.36% in predicting the RUL. When the input data are limited to the first 10 cycles using the dataset constructed from random downsampling and the FPNN, the predicted RUL MAPE is 0.75%. The method proposed in this study offers an accurate, adaptable, and comprehensible new solution for predicting the RUL of lithium batteries, paving a new research path in the field of battery health monitoring.

Remaining useful life prediction method of lithium-ion battery Based on KPCA-IVMD-SE-DNN

In this paper, aiming at the problems of feature processing and capacity regeneration in the prediction of remaining useful life (RUL) of lithium-ion batteries, an RUL prediction method based on kernel principal component analysis (KPCA), improved variational mode decomposition (IVMD), sample entropy (SE), and deep neural network (DNN) are proposed. Firstly, six health indicators (HI) are extracted by analyzing the character of batteries charging and discharging process, and their correlation with capacity is calculated. Secondly, the KPCA is used to denoise and simplify the dimension of the HI set and ensure that they fully contain the degradation information. Thirdly, the battery capacity is decomposed into trend and interference components by using the improved VMD of the central frequency method (CFM), and the reconstruction is carried out according to the SE of each component to increase the efficiency and accuracy of prediction. Finally, the prediction model is constructed based on DNN. The experimental analysis of NASA battery data sets shows that the proposed method has the best prediction accuracy, efficiency, and robustness than DNN, KPCA-DNN, KPCA-EMD-DNN, KPCA-VMD-SE-DNN, and so on.

A sequence to sequence prediction model for remaining useful life of lithium-ion batteries with Bayesian optimisation process visualization

Accurate battery remaining useful life (RUL) prediction plays an important role in ensuring reliable operation of electric vehicles. In this paper, a hybrid model based on Bayesian optimization of deep convolutional neural network and long short-term memory neural network (BO-DCNN-LSTM) is proposed for battery RUL prediction. Feature extraction of raw charging characteristic curves is performed by the multilayer CNN and preliminary capacity prediction is performed by the multilayer LSTM. The model performance is explored with different training, validation and testing strategies and different prediction starting points. Validation using NASA battery aging data shows that the mean absolute error (MAE) and root mean square error (RMSE) of the RUL prediction are 0.0139 Ah and 0.0195 Ah, respectively, when the prediction starting point is the 50th cycle. In addition, this paper visualizes the process of how the Bayesian optimization (BO) algorithm searches for the global optimal combinations in the high-dimensional hyperparameter space and discusses the impact of these hyperparameters on the prediction, filling the gap in this part of the research.

In-situ surface growth strategy to synthesize MXene@graphdiyne heterostructure for achieving high capacity and desirable stability in lithium-ion batteries

Two-dimensional (2D) heterostructured electrodes, combining graphdiyne (GDY) and MXenes, have exhibited substantial promise in augmenting the mobility of both ionic and electron movement. However, the widespread advancement and industrial utilization have been impeded by intricate manufacturing procedures and insufficient stability. This study introduces a more streamlined, in-situ growth approach for preparing MXene@GDY electrodes, presenting a novel heterostructure. The method simplifies the manufacturing process while enhancing the specific capacity of the electrode and the more stable cycle life of lithium-ion batteries (LIBs). The resulting electrode exhibited an impressive initial specific capacity of 464.4 mA h g−1, maintaining a high capacity of 492.9 mA h g−1 after 100 cycles at 100 mA current density. More importantly, after 1200 cycles, MXene@GDY showed a capacity of 340.7 mA h g−1, significantly outperforming pure MXene, which only reached 122.3 mA h g−1 at a current density of 1.0 A g−1. The proposed in-situ construction of heterojunction on the MXene surface has demonstrated immense potential for designing high-performance electrode materials which are applicable to LIBs.

Health prediction of lithium-ion batteries by combining with empirical mode decomposition and PF-GPR algorithm

A new model based on empirical mode decomposition (EMD) and particle filter-gaussian progress regression (PF-GPR) algorithm is developed for estimating state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LIBs). The capacity degradation process of LIBs is investigated and reveals that the new model is convenient and accurate for estimating SOH and RUL of LIBs. The relative error of SOH prediction is less than 1.5%, while the maximum absolute error of RUL prediction is one cycle. Compared with other models reported in previous works, the present model shows smaller absolute error and root mean square error. In addition, the EMD-PF-GPR fusion model based on combined kernel function and mahalanobis distance possesses a good generalization ability and ability of learning from local variations. The work provides a new method to accurately predict health of LIBs.

Pentafluorophenyl diethoxy phosphate: An electrolyte additive for high-voltage cathodes of lithium-ion batteries

Increasing the upper limit of the charging voltage in commercial lithium-ion batteries (LIBs) can enable them to have a higher energy density, but during high-voltage charging and discharging, the electrolyte will accelerate the decomposition, and the interfacial adverse reaction among the cathode and electrolyte is serious, which corrodes the cathode active material and consumes excessive electrolyte, and ultimately lead to a shorter service life of lithium-ion batteries. To address this issue, an electrolyte additive with properties suitable for high voltage cathodes, named pentafluorophenyl diethoxy phosphate (FPOP), was synthesized in this work and successfully applied to LiNi0.8Co0.1Mn0.1O2 cathode (NCM-811). Density Functional Theory (DFT) computations reveal that the FPOP additive undergoes preferential oxidation and self-polymerizes on the NCM-811 cathode surface to establishing a safeguarding film at the cathode-electrolyte interface (CEI). This film effectively inhibits detrimental side reactions. The electrochemical performance test data indicated that the retention rate of discharge-specific capacity in batteries was markedly enhanced with 0.1 wt% of FPOP when added to the standard electrolyte. Specifically, after subjecting the batteries to 200 cycles at a current of 0.2C and under voltages ranging from 4.2 V to 4.5 V, the retention of discharge ratio capacity increased by 37.6 %, 39.2 %, 26.9 %, and 37.1 % correspondingly. Morphology characterization and spectroscopic characterization showed that FPOP could not only construct a stable CEI film by oxidation during the first stages of lithium precipitation but also remove HF by strongly binding to the HF in the electrolyte or dissociating H+ and F− from HF, which ultimately enhanced the batteries' capacity to operate effectively at elevated voltage levels. Therefore, incorporating FPOP as an additive in electrolyte presents a cost-efficient approach to achieving elevated energy density.

A structural pruning method for lithium-ion batteries remaining useful life prediction model with multi-head attention mechanism

The utilization of data-driven models in predicting the remaining useful life (RUL) of lithium-ion batteries has gained traction. However, deploying these models on edge devices presents challenges attributed to their limited generalization capacity and substantial computational demands amidst varying operational conditions. In this study, we formulate a RUL prediction model for lithium-ion batteries by leveraging a multi-layer convolutional network alongside a multi-head attention mechanism. Through the incorporation of a domain adapter and a residual structure, we bolster the model's generalization and anti-overfitting capabilities. Additionally, we introduce an innovative global pruning strategy that hinges on scaling coefficients and adaptive sparse pruning. This strategy outperforms existing pruning methods in terms of model parameter reduction, prediction accuracy, and operational efficiency. The amalgamation of our proposed model and pruning strategy proffers a viable avenue for implementing data-driven models within embedded battery management systems.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM

Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is one of the core tasks in battery health management. This study aims to enhance the accuracy of RUL prediction by proposing a battery RUL prediction method that integrates source domain battery iteration transfer with Long Short-Term Memory (LSTM) neural networks. Firstly, multiple batteries with known degradation trends are utilized as source domain batteries, and combined with full-lifetime capacity degradation data to construct the Source Domain Battery Iterations Module (SIM) to obtain the optimal LSTM pre-training model. Secondly, the pre-trained model is transferred to the target domain and fine-tuned using the target domain training set. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction. The effectiveness of the algorithm is validated on three open-source datasets. Experimental results demonstrate that in scenarios where the source domain and target domain battery types are the same, the absolute error of RUL prediction is less than 2 cycles. Moreover, in cases where the battery types differ, except for the 20% prediction starting point, the RUL prediction error is less than 10 cycles, indicating a high level of prediction accuracy.

Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage

Lithium-ion batteries behave nonlinear degradation during long-term usage. Prediction of the nonlinear degradation is of guiding significance in taking proactive measures to prolong battery life and ensure battery safety. In this study, a new nonlinear degradation knee-point prediction method is proposed utilizing relaxation voltage as the feature sequence, and it is the first attempt with the joint prediction of the knee-point and remaining useful life. A remaining useful life prediction framework integrating degradation features of the knee-point is established, which leads to stable improvements in the accuracy of remaining useful life prediction. Through transfer learning, the proposed joint prediction method is validated on different battery datasets, obtaining mean absolute errors within 26 cycles for the knee-point and remaining useful life prediction, with root mean square errors below 28 cycles. The predicted results can serve as evaluation indicators for various application scenarios, including battery design, ability evaluation, and functionality enhancement.

Machine learning-based prediction of lithium-ion battery life cycle for capacity degradation modelling

With a particular focus on capacity degradation modeling, this paper offers a ground-breaking examination of the use of machine learning approaches for the precise prediction of lithium-ion battery life cycles. Because lithium-ion batteries are essential to many technological applications, it's critical to comprehend and anticipate their life cycles in order to maximize performance and guarantee sustainable energy solutions. The study starts with an extensive examination of the literature, assessing current approaches critically and setting the stage for the introduction of models based on machine learning. The process entails the methodical collecting of data across a range of operational settings, environmental variables, and charging-discharging cycles. Thorough preprocessing guarantees the dataset's consistency and quality for further machine learning model training. Predictive models are created using a variety of machine learning algorithms, including regression models, support vector machines, and deep neural networks. In order to improve prediction accuracy, the paper focuses on the reasoning behind model selection, parameter tuning, and the incorporation of ensemble approaches. In order to uncover important elements influencing the life cycles of lithium-ion batteries and provide important insights into degradation mechanisms, feature selection approaches are used. Using cross-validation techniques and real-world lithium-ion battery datasets, the built machine learning models go through rigorous evaluation and validation processes to determine their robustness, capacity for generalization, and performance metrics. Comparing machine learning-based predictions with conventional models, the results are presented and discussed, offering insights into the interpretability of the models and the identification of important affecting elements. In order to promote proactive maintenance and optimize battery usage, predictive models are integrated into real-time monitoring systems. The consequences for battery management systems are examined. The paper continues by discussing the challenges that come with using machine learning to estimate the life cycle of batteries and outlining possible directions for further research and development, such as scalability, interpretability, and the incorporation of emerging technologies. This research contributes to the ongoing efforts to increase the reliability and sustainability of lithium-ion battery technologies by highlighting the potential impact of machine learning on energy storage system optimization.