Regeneration Phenomena Research Articles

Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

Prediction of state-of-health of lithium-ion battery based on CEEMDAN-SG-LSTM combined model

State-of-health (SOH) is an important indicator for the maintenance and safe operation of batteries, and it is crucial for accurately predicting SOH. To address problems that the noise present in the original data lead to inaccurate prediction results. An Long-Short-Term-Memory (LSTM) method for SOH prediction is proposed based on the joint noise reduction model of complete ensemble empirical mode decomposition adaptive noise (CEEDMAN) and Savitzky-Golay (SG) filtering. Firstly, seven health indicators (HIs) were extracted by analyzing the voltage and current curves, and HIs with higher correlation with SOH were selected using Pearson correlation coefficient. Then, Intrinsic Mode Functions (IMF) components generated from SOH by CEEMDAN are divided into noise-component, noise-dominant-component, useful-signal-dominant-component, filtered noise-dominant-component and useful-signal-dominant-component are reconstructed into filtered SOH. Finally, the LSTM model is used for SOH prediction. Experiments show that proposed model captures the capacity regeneration phenomenon well with high prediction accuracy, and errors are all below 1.9%.

Predicting the Future Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Deep Transfer Learning

Lithium-ion batteries are widely utilized in numerous applications, making it essential to precisely predict their degradation trajectory and remaining useful life (RUL). To improve the stability and applicability of RUL prediction for lithium-ion batteries, this paper uses a new method to predict RUL by combining CNN-LSTM-Attention with transfer learning. The presented model merges the strengths of both convolutional and sequential architectures, and it enhances the model’s capability to grasp comprehensive information by utilizing the attention mechanism, thereby boosting overall performance. The CEEMDAN algorithm is used for NASA batteries with obvious capacity regeneration phenomena to alleviate the difficulties caused by capacity regeneration on model prediction. During the model transfer phase, the CNN and LSTM layers of the pre-trained model from the source domain are kept unchanged during retraining, while the attention and fully connected layers are fine-tuned for NASA batteries and self-tested NCM batteries. The final results indicate that this method achieves superior accuracy relative to other methods while addressing the issue of limited labeled data in the target domain through transfer learning, thereby enhancing the model’s transferability and generalization capabilities.

TPANet: A novel triple parallel attention network approach for remaining useful life prediction of lithium-ion batteries

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities.

A hybrid data-driven method based on data preprocessing to predict the remaining useful life of lithium-ion batteries

Abstract The estimation of remaining useful life (RUL) for lithium-ion batteries is an essential part for battery management system (BMS). A hybrid method is presented which is combining principal component analysis (PCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sparrow search algorithm (SSA), Elman neural network (Elman-NN), and gaussian process regression (GPR) to forecast battery RUL. Firstly, in the data preprocessing stage, the PCA+ICEEMDAN algorithm is creatively proposed to extract features of capacity decay and fluctuation. The PCA method is used to reduce the dimensionality of the extracted indirect health indicators (HIs), and then the ICEEMDAN algorithm is introduced to decompose the fused HI sequence and actual capacity data into residuals and multiple Intrinsic mode functions (IMFs). Secondly, in the prediction stage, feature data is corresponded one to-one with the mixed model. The prediction models of SSA-Elman algorithm and GPR algorithm are established, with the SSA-Elman algorithm predicting the capacity decay trend and the GPR algorithm quantifying the uncertainty caused by the capacity regeneration phenomenon. The final prediction results are obtained by superimposing the two sets of prediction data, and the prediction error and RUL are calculated. The effectiveness of the proposed hybrid approach is validated by RUL prediction experiments on three kinds of batteries. The comparative experimental results indicate that the mean absolute error (MAE) and root mean square error (RMSE) of the presented prediction model for lithium-ion battery capacity are less than 0.7% and 1.0%.

An Attention-Based Deep Learning Approach for Lithium-ion Battery Lifespan Prediction: Analysis and Experimental Validation

The potential for lithium-ion batteries to become unstable can lead to operational malfunctions within the system and result in safety incidents. Therefore, accurately forecasting the remaining useful life (RUL) is beneficial in mitigating the likelihood of battery failure and prolonging its operational lifespan. Hence, precise estimation of RUL can help prevent numerous safety incidents and minimize resource wastage, presenting a significant and complex issue. This paper introduces a Deep Learning (DL) model that utilizes Long Short-Term Memory (LSTM) and attention mechanism to improve the accuracy of predicting the RUL of lithium-ion batteries. Initially, the battery capacity regeneration phenomenon is captured by applying four LSTM layers, followed by implementing an attention mechanism to align input and output sequences based on the content or semantics of the input sequence. Finally, the final prediction outcomes are generated via a Fully Connected (FC) layer. The efficacy of the proposed model is assessed through the utilization of the NASA dataset, and its performance is contrasted with various deep learning models to highlight its efficacy. Results from the experiments demonstrate that the suggested At-LSTM presents a robust option for forecasting the RUL of lithium-ion batteries, as it delivers superior results compared to all other models examined.

Thermal processing of pomegranate seed oils underscores their antioxidant stability and nutritional value: Comparison of pomegranate seed oil with sesame seed oil.

In the present study, the oxidative stability and antioxidant activity of seed oils were investigated in three Iranian pomegranate cultivars, Shirin Khafr, Torsh Sabz, and Rabab, along with the sesame (Sesamum indicume L. cv Dezful) seed oil. Punicic acid was the primary fatty acid in the pomegranate seed oils, with contents ranging from 75.5 to 80.9% (w/w). The tocopherol levels in pomegranate seed oils ranged from 1439 to 2053 mg/kg, whereas the phenolics ranged from 130 to 199.3 mg/kg, respectively. Comparatively, in the seed oil of sesame "Dezful," these substances' contents were 1053 and 79 mg/kg, respectively. Contrary to common perception, the seed oil of the three pomegranate cultivars cultivated in Iran had high oxidative stability and antioxidative activity during the 32 h of thermal processing at 170°C. The oxidation stability assayed by peroxide value, p-anisidine value, and TOTOX index revealed that the pomegranate seed oils had a much higher resistance to the oxidation process than the sesame oil. The content of tocopherols increased during thermal processing due to the regeneration phenomenon. Tocopherols are not always free and may form a matrix with themselves or other compounds. Changes in the antioxidant activity during the thermal processing assessed by DPPH free radical scavenging power and by the FRAP test were consistent with those for the antioxidants. Therefore, these oils can be added to other edible oils as a natural antioxidant to improve their oxidative stability.

Semi-Empirical Model of Nickel Manganese Cobalt (NMC) Lithium-Ion Batteries Including Capacity Regeneration Phenomenon

Semi-Empirical Model of Nickel Manganese Cobalt (NMC) Lithium-Ion Batteries Including Capacity Regeneration Phenomenon

Transfer learning based remaining useful life prediction of lithium-ion battery considering capacity regeneration phenomenon

Lithium-ion battery (LIB) has been widely used in various energy storage systems, and the accurate remaining useful life (RUL) prediction for LIB is critical to ensure the normal operation of system. However, the capacity regeneration (CR) phenomenon caused by the non-working state of LIB will seriously affect the capacity degradation trajectory of LIB, thus leading to an inaccurate RUL prediction result. To solve this problem, this paper proposes a non-parametric CR detection algorithm to detect the CR phenomenon online and quantitatively correct the prediction errors caused by different CR phenomena. In addition, a data reconstruction algorithm and a parameter update scheme are proposed to solve the problem of unit-to-unit difference among LIBs. Finally, the long and short term memory neural network-based transfer learning model is applied to predict the RUL of target LIB. The effectiveness of the proposed model is verified by the real LIB dataset of NASA. Compared with some existing RUL prediction models considering CR phenomenon, the proposed model exhibits higher accuracy and better universality.

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

The capacity regeneration phenomenon is often overlooked in terms of prediction of the remaining useful life (RUL) of LIBs for acceptable fitting between real and predicted results. In this study, we suggest a novel method for quantitative estimation of the associated uncertainty with the RUL, which is based on adaptive fractional Lévy stable motion (AfLSM) and integrated with the Mellin–Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long-range dependence, has a non-Gaussian distribution, and accurately describes heavy-tailed properties. Additionally, the nonlinear drift coefficients of the model can be adaptively updated on the basis of the degradation trajectory. The performance of the proposed RUL prediction model was verified by using the University of Maryland CALEC dataset. Our forecasting results demonstrate the high accuracy of the method and its superiority over other state-of-the-art methods.

Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

In the field of battery health state prognostics, the inaccurate lithium-ion battery's health status prediction is usually caused by the capacity regeneration (CR) phenomenon triggered by relaxation effects during the degradation process. To address this issue, we pay more attention to the main rapid degradation trend of battery capacity instead of many researches focusing on only CR phenomenon. This paper presents a prognostic framework called CEEMDAN-LSTM to decouple the normal capacity degradation process while eliminating local regenerative capacity, capturing degradation characteristics for battery state-of-health (SOH) prognostics. It introduces the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original battery capacity degradation curve into principal trend sequence and other high-frequency subsequences, and Pearson correlation coefficient (PCC) is calculated to remove irrelevant high-frequency subsequences. Predictions task is completed by bidirectional long short-term memory network (Bi-LSTM) and long short-term memory network (LSTM) groups. Experimental validations are conducted on two lithium-ion battery datasets from NASA Ames Research Center and the Advanced Life-Cycle Engineering Center of the University of Maryland. The results demonstrate that the proposed framework achieves more accurate SOH prediction than many previous mainstream methods.

Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM

The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.

Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

The instability of lithium-ion batteries may result in system operation failure and cause safety accidents, thus predicting the remaining useful life (RUL) accurately is helpful for reducing the risk of battery failure and extending its useful life. In this paper, a hybrid model based on TCN-GRU-DNN and dual attention mechanism is proposed for enhancing the RUL prediction accuracy of lithium-ion batteries. Firstly, the Temporal Convolutional Network (TCN) with a feature attention mechanism is applied to form an encoder module to capture the battery capacity regeneration phenomenon, and then a Gated Recurrent Unit (GRU) with a temporal attention mechanism is denoted as a decoder module for better characterizing the decay trend of the capacity series. Finally, the final prediction results are output through a Deep Neural Network (DNN). We conducted experiments on two data sets NASA and CALCE. The prediction errors are presented in subsequent experiments under different evaluation standards such as absolute error (AE), root mean square error (RMSE), mean absolute error (MAE), and R-squared error (R²). The experimental results demonstrate that the proposed model can achieve a more accurate prediction for RUL on lithium-ion batteries, with RMSE does not exceed 2.407% in the NASA dataset and does not exceed 0.897% in the CALCE lithium-ion battery dataset respectively.

A Cauchy perturbation cuckoo search particle filtering algorithm for remaining useful life prediction of lithium-ion battery considering capacity regeneration

The remaining useful life prediction of the lithium-ion battery can evaluate the battery's reliability, identify the occurrence of faults, and reduce battery risks. In this study, the particle filtering algorithm is employed along with the introduction of the Cauchy perturbation factor to enhance the local optimization capability of the cuckoo search algorithm. The improved cuckoo search optimization algorithm is utilized to transfer particles from prior distribution areas to the maximum likelihood area, resulting in the development of the Cauchy perturbation cuckoo search particle filtering algorithm. To achieve high-precision prediction of the remaining useful life, an attenuation model is established, taking into account the complex capacity regeneration phenomenon that occurs after a long period of resting of the lithium-ion battery storage. This model includes three types of capacity: underlying, recovery, and surface capacity. The experimental results, based on an aging dataset from the University of Maryland, demonstrate that the suggested algorithm provides clear advantages over widely used particle filtering and unscented particle filtering algorithms in terms of estimation accuracy. Additionally, it exhibits a fast convergence rate and low resampling rate, further enhancing its benefits.

State of Health Assessment for Lithium-Ion Batteries Using Incremental Energy Analysis and Bidirectional Long Short-Term Memory

The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden.

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Improved Mode Decomposition and Time Series

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries holds significant importance for their health management. Due to the capacity regeneration phenomenon and random interference during the operation of lithium-ion batteries, a single model may exhibit poor prediction accuracy and generalization performance under a single scale signal. This paper proposes a method for predicting the RUL of lithium-ion batteries. The method is based on the improved sparrow search algorithm (ISSA), which optimizes the variational mode decomposition (VMD) and long- and short-term time-series network (LSTNet). First, this study utilized the ISSA-optimized VMD method to decompose the capacity degradation sequence of lithium-ion batteries, acquiring global degradation trend components and local capacity recovery components, then the ISSA–LSTNet–Attention model and ISSA–LSTNet–Skip model were employed to predict the trend component and capacity recovery component, respectively. Finally, the prediction results of these different models were integrated to accurately estimate the RUL of lithium-ion batteries. The proposed model was tested on two public lithium-ion battery datasets; the results indicate a root mean square error (RMSE) under 2%, a mean absolute error (MAE) under 1.5%, and an absolute correlation coefficient (R2) and Nash–Sutcliffe efficiency index (NSE) both above 92.9%, implying high prediction accuracy and superior performance compared to other models. Moreover, the model significantly reduces the complexity of the series.

The future capacity prediction using a hybrid data-driven approach and aging analysis of liquid metal batteries

Liquid metal batteries (LMBs) are wildly considered for large-scale energy storage due to the advantages of simple construction, low cost, and long life. It is of great importance to find a reliable and accurate approach to predict the future capacity for battery management and failure evaluation. A hybrid data-driven framework is presented to predict not only the long-term capacity degradation but also the local regeneration. Firstly, the Empirical mode decomposition (EMD) method is employed to decompose original battery capacity data into a residual and some intrinsic mode functions (IMFs). Then based on the data characteristics, Gaussian Process Regression (GPR) and Relevance Vector Machine (RVM) with combined kernel function is utilized to predict the local fluctuations caused by regeneration phenomena and the capacity degradation, respectively. Furtherly, we can obtain accurate capacity prediction by adding individual predicted results. Finally, the aging process and the mechanism of capacity plunge are analyzed by the incremental capacity analysis (ICA) method. The Root Mean Square Error (RMSE) of prediction is 0.117 Ah for a 20 Ah battery and 0.141 Ah for a 50 Ah battery by using the proposed framework. Compared with existing mainstream methods, the proposed framework has a more accurate prediction performance to capture the degradation tendency before the capacity plunge, which indicates the positive promise for practical applications of this framework.

State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

State of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems (BMS). However, accurate SOH and RUL prediction still need to be improved due to the complicated battery aging mechanism. This work combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) based on the second-order RC model with an improved Bidirectional Gated Recurrent Unit (BiGRU) to develop SOH and RUL prediction framework. Firstly, the voltage is reconstructed through the second-order RC model to obtain the incremental capacity (IC) and differential voltage (DV) curves to avoid the influence of measurement noise and the complex parameter adjustment process in the filtering method on the IC and DV curves. Then, a new set of battery aging features are extracted from the reshaped IC and DV curves to improve SOH and RUL prediction accuracy and robustness. Next, the BiGRU method with attention mechanism (BiGRU-AM) is used to build the prediction models for battery aging features, SOH, and RUL. To reduce the impact of the capacity regeneration phenomenon, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is used to decompose the SOH prediction results, and the decomposed residual is used as the input to improve the prediction accuracy of RUL. The uncertainty of RUL prediction results is analyzed by Monte Carlo (MC) simulation. Finally, the proposed method is verified by experimental battery data from Center for Advanced Life Cycle Engineering (CALCE) and Sandia National Laboratory. Experimental results show that the voltage reconstruction results based on the second-order RC model are applied to ICA and DVA analysis, effectively avoiding the influence of noise. The RMSE of voltage reconstruction is within 0.0006, and the Pearson correlation coefficient between the four aging features extracted from the reconstructed IC/DV curve and SOH is above 0.9. Moreover, this method has good robustness to the cell inconsistency, temperature uncertainty, and a satisfied generalization ability to different battery chemistries, which the maximum RUL predicted AE of CALCE and Sandia battery is within 10 and 5, respectively.

Echocardiographic assessment of Xenopus tropicalis heart regeneration

BackgroundRecently, it was reported that the adult X. tropicalis heart can regenerate in a nearly scar-free manner after injury via apical resection. Thus, a cardiac regeneration model in adult X. tropicalis provides a powerful tool for recapitulating a perfect regeneration phenomenon, elucidating the underlying molecular mechanisms of cardiac regeneration in an adult heart, and developing an interventional strategy for the improvement in the regeneration of an adult heart, which may be more applicable in mammals than in species with a lower degree of evolution. However, a noninvasive and rapid real-time method that can observe and measure the long-term dynamic change in the regenerated heart in living organisms to monitor and assess the regeneration and repair status in this model has not yet been established.ResultsIn the present study, the methodology of echocardiographic assessment to characterize the morphology, anatomic structure and cardiac function of injured X. tropicalis hearts established by apex resection was established. The findings of this study demonstrated for the first time that small animal echocardiographic analysis can be used to assess the regeneration of X. tropicalis damaged heart in a scar-free perfect regeneration or nonperfect regeneration with adhesion manner via recovery of morphology and cardiac function.ConclusionsSmall animal echocardiography is a reliable, noninvasive and rapid real-time method for observing and assessing the long-term dynamic changes in the regeneration of injured X. tropicalis hearts.

Remaining Useful Life Prediction of Lithium-Ion Batteries: A Hybrid Approach of Grey–Markov Chain Model and Improved Gaussian Process

Accurate prediction of the lithium-ion battery’s future capacity and remaining useful life (RUL) is of great significance to battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey–Markov chain model (GMCM) and the improved Gaussian process is proposed in this article. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multioutput mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multistep prediction.