Life Of Lithium-ion Batteries Research Articles

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.

A hybrid remaining useful life prediction method for lithium-ion batteries based on transfer learning with CDRSN-BiGRU-AM

The prediction of the remaining useful life (RUL) of widely used lithium-ion batteries (LIBs) is of great importance. Existing techniques struggle to balance prediction accuracy with execution time. To achieve accurate RUL prediction quickly, a hybrid RUL prediction method for LIBs has been developed. This method first employs a channel-wise deep residual shrinkage network to adaptively extract features from input data enhancing important information features and suppressing ineffective ones based on the significance of the feature information. Subsequently, a bidirectional gated recurrent unit is used to extract bidirectional temporal features from the processed data, and an attention mechanism is introduced to maximize the extraction of significant temporal mutual information. Finally, a fully connected layer transfer strategy is applied to transition the model from offline training to online prediction, which avoids unstable predictions due to random model initialization and significantly improves the model’s computational efficiency. The simulation results show that the root mean square error of the proposed method did not exceed 1.77% and the mean absolute error did not exceed 1.44% on the NASA dataset. Consequently, the proposed method can achieve accurate online RUL prediction accuracy for LIBs.

Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative Study

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks.

Optimizing Remaining Useful Life Estimation of Lithium-Ion Batteries: A Particle Swarm Optimization-Based Grey Prediction Model

Accurate estimation of the age and condition of lithium-ion batteries (LIBs) is paramount for their safe and economically viable utilization. However, assessing the degradation of these power units proves challenging due to their dependence on various environmental and usage factors. In this study, we propose an efficient Particle Swarm Optimization (PSO)-based Grey Theory prediction model to determine the Remaining Useful Life (RUL) of lithium-ion batteries.
 The proposed model utilizes PSO to optimize the coefficients of a grey prediction model, enabling accurate forecasting of the remaining useful life of LIBs. Our results demonstrate that the presented model outperforms conventional grey prediction models in terms of both accuracy and stability. Furthermore, the proposed model offers simpler predictions compared to existing models in the literature.
 By introducing this promising technique, our study contributes to the precise forecasting of the RUL of lithium-ion batteries and holds potential for applications in similar domains. This research serves as a significant step towards ensuring effective management and utilization of LIBs, promoting their reliability and safety.

Remaining useful life prediction of lithium battery based on CEEMD-SE-IPSO-LSSVM hybrid model

Abstract In order to prevent accidents caused by battery aging, accurately predicting the remaining useful life (RUL) is a critical and highly challenging task in battery management systems. This article describes a lithium-ion battery RUL prediction method based on a hybrid model of CEEMD-SE-IPSO-LSSVM. This method integrates various technologies and algorithms, enhancing the accuracy and practicality of predictions. Initially, the complete ensemble empirical mode decomposition (CEEMD) is utilized to decompose the raw data into multiple intrinsic mode functions, aiding in denoising and feature extraction. Subsequently, the sample entropy (SE) is used to assess the complexity and irregularity of the data, merging intrinsic mode function components with similar SE values into a new component. Building upon this, the advanced iterative particle swarm optimization (IPSO) algorithm refines the parameters of the least squares support vector machine (LSSVM) model, improving the predictive performance of the model. Finally, through iterative training and refinement of the LSSVM model, accurate prediction of the remaining life of lithium-ion batteries is achieved. This hybrid model approach integrates data processing, feature extraction, and model refinement, resulting in a significant improvement over the baseline model with a 69.5% increase in mean absolute percentage error and a 49.4% decrease in root mean squared error, providing a robust solution for predicting the remaining life of lithium-ion batteries.

Zwitterion grafted polyimide separator for improving lithium-ion transport and its application in LiCoO2 batteries

To improve the lithium-ion transport ability and cycle life of lithium-ion batteries, a high-performance polyimide-based lithium-ion battery separator (PI-SO3) was prepared by chemically grafting zwitterion onto the surface of heat-resistant polyimide nanofiber matrix with the assistance of amino-rich polyethyleneimine (PEI). The PI-SO3 not only maintains good dimensional stability (over 200 °C) and flame retardancy, but also ensures the safety of the battery under extreme conditions. Electrochemical experimental results show that PI-SO3 has high ion conductivity (1.99 mS·cm−1), Li+ transference number (0.632), and excellent oxidation potential (5.28 V), which are all higher than those of pure PI separator (1.14 mS·cm−1, 0.444, and 4.56 V, respectively). Meanwhile, LiCoO2/PI-SO3/Li batteries exhibit excellent high-rate performance (139.5 mAh·g−1 at 5C current) and long-term cycle stability (138.9 mAh·g−1 after 100 cycles at 1C current, and 127.9 mAh·g−1 after 100 cycles at 2C current). In addition, the symmetrical battery, Li/PI-SO3/Li, shows stable more than 360 cycles. Compared with batteries using commercial separators, such as Celgard 2325 and pure polyimide matrix, the assembled coin cells with PI-SO3 separators have higher lithium-ion conductivity, Li+ transference number, and diffusion coefficient after grafting zwitterion, showing better rate ability and capacity retention. In this paper, the modification strategy of thermal-resistant separator by grafting zwitterions provides an efficient way to prepare next generation high-performance lithium-ion batteries with good safety.

Two strain modes and transition point of 18650 lithium-ion battery at different charging rates

The safety issues of lithium-ion batteries are one of the biggest obstacles restricting the development of lithium-ion batteries. Excessive charging rate is not conducive to extending the cycle life of lithium-ion batteries. However, there is a lack of research on the upper limit of the charging rate of lithium batteries. The strain of the battery can reflect the change of the internal material during the charging process. The purpose of this study is to explore the charging rate of the strain transition point of lithium-ion batteries and the strain characteristics of the batteries after exceeding this rate. The experiments conducted at different rates and temperatures on battery strain revealed that the transition point of strain in the battery occurs at a rate of 2C. When the charging rate is lower than 2C, the battery strain increases monotonically, which is similar to the step response of the first-order system. When the charging rate exceeds 2C, the battery strain experiences excessive strain due to severe lithium plating, showing a step response similar to a second-order system with a spike representing an overshoot phenomenon. As the charging rate continues to increase, the overshoot peak of the battery ‘s strain becomes more obvious. The experimental results confirm that the charging rate of the 18,650 ternary lithium-ion battery used in the experiment should not exceed 2C, as it would lead to excessive strain in the battery.

Controllable long-term lithium replenishment for enhancing energy density and cycle life of lithium-ion batteries

A controllable and sustainable lithium replenishment strategy was developed to achieve high-energy-density and long-lifespan lithium-ion batteries.

Cycle life and influencing factors of cathode materials for lithium-ion batteries--a case study of lithium-cobalt oxides

Abstract Lithium-cobalt oxide has become a new generation of highly promising anode materials for lithium-ion batteries due to its low price, environmental friendliness, high platform voltage, and high theoretical capacity. In this paper, the working characteristics and related parameters of lithium-ion batteries are sorted out, and the influence factors of the decline mechanism of lithium-ion batteries are investigated from the perspective of chemical composition. Regarding the cycle life of lithium-ion battery cathode materials, this paper establishes a cycle life prediction model for lithium-ion batteries based on the LSTM model. It optimizes the hyperparameters of the model using the PSO algorithm. In addition, this paper also prepared lithium cobalt oxide cathode materials and carried out a validation analysis of the factors affecting their electrochemical performance. It is found that the cycle life prediction of lithium-ion battery based on LSTM has an RMSE of 3.27%, and the capacity of lithium cobalt oxide soft pack full battery decays from 249.81mAh to 137.04mAh at 26°C. The cycle life of its lithium cobalt oxide lithium-ion battery is around 250 cycles, and the average decay is 0.445mAh during one charge/discharge. Different charge/discharge cycles and diversity will have a significant effect on the cycle life of lithium-ion battery cathode materials, and it is necessary to pay attention to the parameter changes in the preparation of lithium cobalt oxide cathode materials in order to increase the cycle life of lithium-ion batteries.

Maximizing the performance of data-driven capacity estimation for lithium-ion battery

Predicting the cycle life of lithium-ion batteries is crucial for optimizing their long-term utilization. Traditional prediction methods struggle with real-world data due to diverse aging conditions and computational constraints. Our study circumvents these obstacles, exclusively utilizing discharge capacity curves. We introduce a data-driven method that refines similarity-based estimates using correction factors from neural network. Impressively, this approach, using just 300 cycles of discharge capacity curves, reduces error rates to 7.16% and 21.88% for primary and secondary test data, respectively, marking a significant reduction compared to the 27% and 46% errors reported by Severson et al. This study promises substantial potential for diverse applications requiring remaining useful life predictions. Our code is open-sourced and available at https://github.com/HyosikMoon/RUL-code/.