Lithium-ion Battery Remaining Useful Life Prediction Research Articles

RUL prediction of lithium-ion battery based on BiLSTM

Abstract In this paper, in order to avoid the unavoidable degradation of the Remaining Useful Life(RUL) of lithium batteries in course of use, which may lead to the inability of lithium batteries to work normally or even cause safety accidents, finding a way to effectively predict the remaining life of lithium batteries in advance is significant. Therefore, the generator and discriminator models are constructed through a multilayer bidirectional length and Bidirectional Long Short-Term Memory(BiLSTM) model, which expands fewer data samples, making the sample data volume greatly improved, and avoiding the overfitting of the subsequent deep learning models under the fewer sample data. In BiLSTM, the hidden layer preserves the internal state of bidirectional data, and the key information is preserved and discarded through the internal Long Short-Term Memory(LSTM) module, so as not to cause any damage to the battery. information is preserved and discarded to avoid the problem of gradient disappearance that occurs when the data is dependent for a long time, and the GAN-BiLSTM model is suggested to deal with the issue of low precision and accuracy of deep learning methods with small samples.

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.

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on a Hybrid Deep Learning Model

Lithium-ion batteries are widely utilized in various fields, including aerospace, new energy vehicles, energy storage systems, medical equipment, and security equipment, due to their high energy density, extended lifespan, and lightweight design. Precisely predicting the remaining useful life (RUL) of lithium batteries is crucial for ensuring the safe use of a device. In order to solve the problems of unstable prediction accuracy and difficultly modeling lithium-ion battery RUL with previous methods, this paper combines a channel attention (CA) mechanism and long short-term memory networks (LSTM) to propose a new hybrid CA-LSTM lithium-ion battery RUL prediction model. By incorporating a CA mechanism, the utilization of local features in situations where data are limited can be improved. Additionally, the CA mechanism can effectively mitigate the impact of battery capacity rebound on the model during lithium-ion battery charging and discharging cycles. In order to ensure the full validity of the experiments, this paper utilized the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery datasets and different prediction starting points for model validation. The experimental results demonstrated that the hybrid CA-LSTM lithium-ion battery RUL prediction model proposed in this paper exhibited a strong predictive performance and was minimally influenced by the prediction starting point.

RUL Prediction of Lithium-Ion Battery in Early-Cycle Stage Based on Similar Sample Fusion Under Lebesgue Sampling Framework

Remaining useful life (RUL) prediction of lithium-ion battery in early-cycle stage is of great significance to improve battery performance and reduce losses caused by failure. Due to complex degradation mechanism and insufficient data in early-cycle stage, current RUL prediction schemes for lithium-ion battery have trouble obtaining degradation characteristics to achieve satisfactory prediction accuracy. Aiming at this problem, this paper proposes an RUL prediction method of lithium-ion batteries in early-cycle stage based on similar sample fusion under Lebesgue sampling framework. First, a novel similarity measurement index based on fusion of Pearson correlation coefficient and Euclidean distance is proposed, and the fusion parameter is optimized by jumping spider optimization algorithm. Similar samples are selected as reference for the prediction model. Then, Lebesgue sampling theory is introduced to complete data structure transformation for similar samples, so as to ensure that the fused points of different similar samples are under the same degradation state. Finally, similar sample fusion result is transformed to Riemann sampling framework and perform a linear fitting. Fitting results are used to construct a particle filter model for capacity degradation process and RUL prediction. Experimental results and comparison studies on APR18650M1A battery dataset demonstrate the effectiveness of the proposed method.

An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration

Remaining useful life (RUL) prediction is the core part of battery management system. The capacity regeneration phenomenon during battery capacity degradation interferes with the accuracy of RUL prediction. Therefore, this paper proposes an interpretable scheme named VPA model for lithium-ion battery RUL prediction by integrating algorithms with sufficient mathematical support. Firstly, trend signal (TS) and capacity regeneration signal (CRS) are obtained from capacity degradation sequence by variational mode decomposition algorithm. Then, prediction of TS and CRS is implemented by particle filter model and autoregressive integrated moving average model, respectively, and the prediction results of TS and CRS are superimposed as capacity degradation forecast. Finally, RUL prediction is performed based on degradation prediction result and failure threshold. Experimental results in engineering data prove the effectiveness of the proposed scheme.

A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

For accelerating the technology development and facilitating the reliable operation of lithium-ion batteries, accurate prediction for battery remaining useful life (RUL) are both critical. In this paper, a 1D CNN-BiLSTM method is proposed to extract the RUL prediction of lithium-ion battery of Electric Vehicles (EVs). By using one dimensional convolutional neural network (1D CNN) and bidirectional long short-term memory (BiLSTM) neural network simultaneously, selecting the ELU activation function to apply to the convolutional layer, a hybrid neural network is proposed to improve the accuracy and stability of lithium-ion battery RUL prediction. The 1D CNN is used to fully mine the deep features of lithium-ion SOH data, while the BiLSTM is adopted to study the deep features in two directions, and the RUL prediction of lithium-ion battery is output through dense layer. To verify the effectiveness of the proposed method, the battery data of the National Aeronautics and Space Administration (NASA) are utilized to make some comparisons among the RNN model, LSTM model, BiLSTM model and hybrid neural network model. The results show that the hybrid one has higher generalization ability and prediction accuracy than the others.

A lithium-ion battery remaining useful life prediction method based on unscented particle filter and optimal combination strategy

Remaining useful life (RUL) prediction is crucial for the lithium-ion battery prognosis and health management. To track the battery degradation process of the highly nonlinear characteristic and predict accurately its RUL, Particle filter (PF) methods are widely used. However, in the state estimation of PF, the degeneracy and impoverishment of particles make the prediction results unreliable and inaccurate. To solve this problem, this paper proposes an integrated lithium-ion battery RUL prediction method based on a particle resampling strategy, namely optimal combination strategy (OCS), and unscented particle filter (UPF). First, the unscented Kalman filter is used to generate the proposal distribution of particles for the calculation of the particle weights in PF. Then, the OCS is employed for the resampling process to improve particles distribution and keep their diversity. Finally, two lithium-ion battery RUL prediction experiments have been conducted, in which the mainstream methods are compared. The results show that the proposed method effectively predicts the battery RUL and validates its superiority and robustness.

An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares support vector machine

The lithium-ion battery degradation in electric vehicles is inevitable among its lifetime. Therefore, it is important to predict the remaining useful life (RUL) for battery management system. This paper proposed a RUL prediction method of lithium-ion battery based on dual filters with data-driven and model-based fusion algorithm. The key of the algorithm framework is that two fusion algorithm filters update the battery capacity degradation state and least squares support vector machines (LSSVM) model parameters with iteration online simultaneously. In fusion algorithm processing, the LSSVM model is used as the measurement equation of the unscented particle filtering (UPF) to provide the virtual measurement value of the future time for the UPF-LSSVM fusion algorithm. The uncertainty of the prognostic result is expressed by probability density function (PDF) in UPF processing. Finally, the simulation results show that the proposed dual filters fusion method is superior for lithium-ion battery RUL prediction.

A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction

Abstract Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can improve the durability, reliability, and maintainability of battery system operation in electric vehicles. To achieve high-accuracy RUL predictions, it is necessary to develop an effective method for long-term nonlinear degradation prediction and quantify the uncertainty of the prediction results. To this end, this paper proposes a hybrid approach for lithium-ion battery RUL prediction based on particle filter (PF) and long short-term memory (LSTM) neural network. First, based on the training set, the model parameters are iteratively updated using the PF algorithm. Second, the LSTM model parameters are obtained using the training set. The mean and standard deviation in the prediction stage are obtained through Monte Carlo (MC) dropout. Finally, the mean value predicted by MC-dropout is used as the measurement for the PF in the prediction phase, the standard deviation represents the uncertainty of the prediction result, and the mean and standard deviation are integrated into the measurement equation of the model. The experimental results show that the proposed hybrid approach has better prediction accuracy than the PF, LSTM algorithm, and two other types of hybrid approaches. The hybrid approach can obtain a narrower confidence interval.

Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation

Accurate prediction of remaining useful life (RUL) for lithium-ion battery (LIB) plays a key role in increasing the reliability and safety of battery related industries and facilities. In this paper, RUL prediction of LIB is investigated by employing a hybrid data-driven method based support vector regression (SVR) and error compensation (EC). Firstly, two health indicators (HIs) are established by using capacity and discharging voltage difference of equal time interval (DVD), respectively. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to preprocess the obtained HIs, which is used to reduce the influence of capacity regeneration and noise. Especially, phase space reconstruction (PSR) with C–C technique is introduced to achieve optimal input sequence selection pattern, it has an important influence on the accuracy of SVR prediction. As an important innovation of the paper, the idea of EC is implemented by combining the predictions of both forecast error and RUL prediction with PSR-SVR. Last but not least, the genetic algorithm (GA) is utilized to optimize the key parameters of SVR so as to achieve more accurate RUL prediction. To verify the effectiveness of the proposed approach, the real data set of LIBs from National Aeronautics and Space Administration (NASA) is carried out, and the dominant is emphasized by comparison with other important methods.

A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

Simulator Assessment Theory for Remaining Useful Life Prediction of Lithium-Ion Battery Under Multiple Uncertainties

As Lithium-ion batteries become the main power source in various electronics, it is important to predict the remaining useful life (RUL) of these batteries, in order to make the maintenance strategy and avoid serious consequences caused by the failure of power supply. With the convenience in fitting field measurements, the model based methods are widely used in RUL prediction for lithium-ion batteries. However, these predictions are usually unreliable because of incomplete uncertainty quantification. This paper proposes a model update method for the RUL prediction of lithium-ion battery based on the Bayesian simulator assessment theory. With an empirical degradation model, the method quantifies the uncertainties in model parameters, model form and measurements error. It infers the reality prediction to battery failure threshold with a combination of multiple uncertainties. The main innovation of the proposed method is that it doesn't only adjust the model parameters, but also the bias function which accounts for the model form uncertainty. And a modular Markov chain Monte Carlo method is employed to implement the model update with multiple uncertain parameters. As uncertainties are considered systematically in the inference process, it can provide a reliable RUL prediction. We demonstrate the predictive capability of the method by the real life cycle dataset of lithium-ion batteries from NASA.

A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other’s prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.

A holistic comparison of the different resampling algorithms for particle filter based prognosis using lithium ion batteries as a case study

Prognostic health management (PHM) is a critical and essential aspect of any robust maintenance program in the manufacturing industry for early failure detection and prediction of the remaining useful life (RUL) for the entire system or for a component (sub-system) whose condition is being monitored in real-time. In recent years, a lot of research has been done on developing better performing prognostic algorithms for RUL prediction with the “particle filter (PF)” framework being the most widely used amongst them. To address the problems of particle degeneracy and particle impoverishment, several adaptations of standard particle filters have been proposed by improvising the resampling strategies. However, the efficacy of these algorithms is assessed only under specific conditions involving relatively clean degradation data (low noise), large training data sets and limited degradation patterns (mostly linear or “almost” linear). The purpose of this study is to make a comparison of four most frequently used resampling strategies: Multinomial resampling, Stratified resampling, Systematic resampling and Residual Systematic resampling for lithium-ion battery RUL prediction. They are similar in terms of operation but differ only in the way the ordered sequence of random numbers is generated for resampling thus enabling a standardized comparison in terms of computational complexity of O(N). The robustness of these resampling techniques is tested by adding 50 dB of noise to the measurement data and by considering three different time instants at different stages of the device lifecycle for prediction with different amount of training data. We use the mean squared deviation (MSD), relative accuracy (RA), execution time and the α – λ plot as the performance metrics for comparing the effectiveness of the different resampling techniques. Our analysis shows that the residual systematic resampling algorithm is the most preferred approach considering the reasonable accuracy and short computational time.

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.

Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach

Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real data set of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.

An improved model for remaining useful life prediction on capacity degradation and regeneration of lithium-ion battery

The regeneration phenomena of the lithium-ion battery are widely existed in reality but rarely studied due to the gap between experiment conditions and practical working conditions. In this paper, the capacity regeneration phenomena are considered during the degradation process of batteries. An improved empirical model incorporating both rest time and discharge cycles for remaining useful life (RUL) prediction is proposed. The degradation process and regeneration process have been described by different components and integrated to formulate the whole model. The dual estimation framework is employed to decouple the states and parameters during the degradation and regeneration process. The datasets from NASA Prognostics Center of Excellence (PCoE) have been adopted for model validation. The proposed model is compared with other empirical model and also different estimation methods. The results are satisfactory, and demonstrate the capability of the proposed model for the RUL prediction of Lithium-ion battery.

Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Lithium-ion batteries are widely used as power sources in various portable electronics, hybrid electric vehicles, aeronautic and aerospace engineering, etc. To ensure an uninterruptible power supply, the remaining useful life (RUL) prediction of lithium-ion batteries has attracted extensive attention in recent years. This paper proposed an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction based on Markov chain Monte Carlo (MCMC). The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm. Additionally, the IUPF method is proposed on the basis of UPF, so it can also suppress the particle degradation existing in the standard PF algorithm. In this work, the IUPF method is introduced firstly. Then, the capacity data of lithium-ion batteries are collected and the empirical capacity degradation model is established. The proposed method is used to estimate the RUL of lithium-ion battery. The RUL prediction results demonstrate the effectiveness and advantage.

Remaining Useful Life Prognostics for Lithium-ion Battery Based on Gaussian Processing Regression Combined with the Empirical Model


 
 
 Data-driven techniques based on Bayesian framework like Gaussian Process Regression (GPR) can not only predict the lithium-ion battery Remaining Useful Life (RUL), but also provide the uncertainty representation. However, it is always difficult to choose the covariance function of GPR and the confidence bound is usually large if the training data are not enough. In order to solve this problem, a combining method is proposed, it is a prognostic framework based on GPR model combined with Empirical Model (EMGPR) to realize the lithium-ion battery RUL prediction. EMGPR has the advantages of predicting the tendency and uncertainty management for RUL estimation. The modeling process of EMGPR consists of two steps. The self-deterministic part, which reflects the real physical process of battery degradation, is approximated by the empirical model. And the disturbance part, which is caused by random noise such as measurement and environment noise, is expressed by the GPR model. In application, two key factors of EMGPR are focused. Firstly, the prediction result is not accurate enough if the training data are not very reliable. In this case, more reliable training data should be selected optimized. Secondly, the characteristic of the disturbance is involved to determine the kernel function of GPR model. With this EMGPR framework, the RUL result is estimated with uncertainty representation, as well, the covariance function of GPR is easy to choose. Experiments with NASA PCoE and CALCE battery data show the satisfactory result can be obtained with the EMGPR approach.