Battery Remaining Useful Life Research Articles

Impact of Data Partitioning to Improve Prediction Accuracy for Remaining Useful Life of Li-Ion Batteries

Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability, the charge–discharge information of cathode materials with different chemical elements was adopted from previous research, including lithium iron phosphate, lithium nickel cobalt aluminum oxide, and lithium nickel cobalt manganese oxide cells. Among the partitioning procedures, the method of adding predicted data from the surrogate model to the training set exhibited the best accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which only used certain cycles before the test set as the training set, exhibited the worst MAE value of 60 cycles. In conclusion, the proposed data partitioning method could be implemented to predict the RUL of batteries to develop next-generation cathode materials with improved performance and stability, shorten the quality assessment time, and achieve stable predictive maintenance.

Sliding Window Pooling Extreme Learning Machine Based Capacity Prediction of Lithium-ion Batteries

Lithium batteries have been widely used in various fields. Accurately predicting the remaining useful life (RUL) of batteries is greatly significant for the safety, reliability and health management of batteries. Based on the measured parameters in the process of charge and discharge of battery, the extraction method of singular value decomposition (SVD) is used to get the health indicators (HIs) related to capacity. And the traditional extreme learning machine (ELM) method has been used to predict the RUL of lithium battery, but the prediction results are divergent, random and low precision. Therefore, this paper proposes sliding window pooling ELM (SW-PELM) method to solve these problems by adding a sliding window and pooling connections. The simulation experiment was carried out through the data of lithium-ion battery from three different research institutions. Experimental results show that the proposed algorithm can well improve the divergence and random ELM prediction results, and has higher prediction accuracy compared with other improved ELM algorithms.

Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis

<abstract> <p>The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.</p> </abstract>

An Improved Generic Hybrid Prognostic Method for RUL Prediction Based on PF-LSTM Learning

Accurate estimation and prediction of the State-of-Health (SOH) and Remaining Useful Life (RUL) are fundamental to optimal maintenance strategies formulation for Prognostics and Health Management (PHM) of degraded equipment. However, the performance assessment of health state prognostics and RUL prediction is strongly dependent on the errors and uncertainties in physical measurements, and heterogeneous degradation of equipment in time-varying operating conditions. The objective of the paper is to provide a hybrid prognostic framework that integrates a two-phases clustering scheme and a PF-LSTM learning algorithm based on Particle Filter (PF) and Long Short-Term Memory (LSTM) networks for dynamic classification of SOH and long-term RUL prediction in the absence of future observations. The proposed generic hybrid PF-LSTM prognostic approach is demonstrated and compared with other adaptive learning and machine learning methods such as Unscented Particle Filter (UPF) and Radial Basis Function Network (RBFN) on the degradation modeling and RUL prediction for Lithium-ion batteries. The comparison results show that robust prediction performance can be obtained by the hybrid PF-LSTM prognostic approach with accurate characterization of equipment degradation states based on the integrated subtractive-fuzzy clustering analysis. The more accuracy on prognostic estimations in Probability Density Function (PDF) of prior and posterior distributions of battery capacity and RUL that are achieved by particle filtering can gain extensive insights to predictive maintenance action guide.

Probabilistic State of Health and Remaining Useful Life Prediction for Li-Ion Batteries

Lithium-ion batteries are widely used in electric vehicles due to their high specific energy value. The continuous charge/discharge cycles pf batteries, determining a change of their State of Health (SoH) and a reduction of their Remaining Useful Life (RUL). The relevant literature describes a variety of methods for the prognostic of battery degradation and RUL estimation, based on electrochemical models or on statistical and artificial intelligence techniques. However, the capacity degradation of lithium-ion batteries depends on many uncertain parameters that are suitable to be estimated and modeled within a probabilistic framework. Therefore, the development of new probabilistic methodologies for the prognostic of lithium-ion batteries can be useful and effective. In this paper, probabilistic models based on time series (AutoRegressive Integrated Moving Average model with eXogenous predictors, (ARIMAX)) and regression approaches (Linear Quantile Regression (LQR), Bootstrap Multiple Linear Regression (B-MLR) and Bayesian Bootstrap Multiple Linear Regression (BB-MLR)) are developed and compared for this purpose. All the considered models are specifically suited up to exploit data coming from Accelerated Degradation Tests (ADTs), and developed under two different approaches (i.e., non-differentiation and differentiation of the target time series) to predict SoH and RUL. Moreover, a dedicated procedure to extract a single, point value from the probabilistic predictions is presented to let the models work also in deterministic scenarios. The performance and the comparison between the proposals and several benchmarks taken from the literature are carried out using data from publicly available databases, confirming the validity and the accuracy of the proposed solutions.

Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization

<abstract><p>The performance of lithium-ion batteries declines rapidly over time, inducing anxiety in their usage. Ascertaining the capacity of these batteries is difficult to measure directly during online remaining useful life (RUL) prediction, and a single deep learning model falls short of accuracy and applicability in RUL predictive analysis. Hence, this study proposes a lithium-ion battery RUL indirect prediction model, fusing convolutional neural networks and bidirectional gated recurrent units (CNN-BiGRU). The analysis of characteristic parameters of battery life status reveals the selection of pressure discharge time, average discharge voltage and average temperature as health factors of lithium-ion batteries. Following this, a CNN-BiGRU model for lithium-ion battery RUL indirect prediction is established, and the Tree-structured Parzen Estimator (TPE) adaptive hyperparameter optimization method is used for CNN-BiGRU model hyperparameter optimization. Overall, comparison experiments on single-model and other fusion models demonstrate our proposed model's superiority in the prediction of RUL in terms of stability and accuracy.</p> </abstract>

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life (RUL) of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage. A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development. However, it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries, as well as dynamic operating conditions in practical applications. Moreover, due to insignificant capacity degradation in early stages, early prediction of battery life with early cycle data can be more difficult. In this paper, we propose a hybrid deep learning model for early prediction of battery RUL. The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction. We also design a non-linear correlation-based method to select effective domain knowledge-based features. Moreover, a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost. Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set, but also generalizes well to the secondary test set having a clearly different distribution with the training set. The PyTorch implementation of our proposed approach is available at https://github.com/batteryrullbattery_rul_early_prediction.

Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is a key, challenging research direction. In this study, a battery degradation model is built based on the LIB dataset of NASA. A data decomposition prediction method, which extracts the linear trend from the capacity degradation data and then predicts the residuals of time series with nonlinear relations, is proposed. Then, an autoregressive integrated moving average-long short-term memory (ARIMA-LSTM) combined model for predicting the RUL of LIBs is established. The linear trend of capacity degradation is predicted by the ARIMA model and the LSTM model is established to predict the nonlinear residuals separated from the capacity degradation. The summation of both obtains the final battery RUL prediction result. Compared with the LSTM model and prediction models of similar types in existing literature in recent two years, the experimental results show that the RUL prediction error of the proposed model in this article is no more than one cycle, which is smaller than that of all the models involved in the comparison. Thus, this method has higher prediction accuracy and model generalization ability in the prediction of the remaining useful life of LIBs.

Data-Driven Approach for Lithium-Ion Battery Remaining Useful Life Prediction: A Literature Review

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment's remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery's working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery's state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery's safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery's cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction.

Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model.

The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.

Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM

The remaining useful life (RUL) of a lithium-ion battery is directly related to the safety and reliability of the electric system powered by a lithium-ion battery. Accurate prediction of RUL can ensure timely replacement and maintenance of the batteries of the power supply system, and avoid potential safety hazards in the lithium-ion battery power supply system. In order to solve the problem that the prediction accuracy of the RUL of lithium-ion batteries is reduced due to the local capacity recovery phenomenon in the process of the capacity degradation of lithium-ion batteries, a prediction model based on the combination of the whale optimization algorithm (WOA)-variational mode decomposition (VMD) and short-term memory neural network (LSTM) was proposed. First, WOA was used to optimize the VMD parameters, so that the WOA-VMD could fully decompose the capacity signal of the lithium-ion battery and separate the dual component with global attenuation trend and a series of fluctuating components representing the capacity recovery from the capacity signal of the lithium-ion battery. Then, LSTML was used to predict the dual component and fluctuation components, so that LSTM could avoid the interference of the capacity recovery to the prediction. Finally, the RUL prediction results were obtained by stacking and reconstructing the component prediction results. The experimental results show that WOA-VMD-LSTM can effectively improve the prediction accuracy of the RUL of lithium-ion batteries. The average cycle error was one cycle, the average RMSE was less than 0.69%, and the average MAPE was less than 0.43%.

Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health

The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. The principal part of an EV is the energy storage system, which is usually the batteries. Thus, the accurate estimation of the remaining useful life (RUL) of the batteries, for an optimal health management and a decision-making policy, still remains a challenge for automakers. In this paper, the problem of battery RUL prediction is studied from a new perspective. Unlike other estimation strategies existing in the literature, the proposed technique uses an intelligent prediction of the lifespan of lithium–iron–phosphate (LFP) batteries via a modified version of neural networks. It uses a data-driven life estimation approach and optimization method and does not require any prior comprehension and initialization of any parameters of the battery model. To validate and verify the proposed technique, we use LFP battery data sets, and the experimental results showed that the proposed methodology can well learn the characteristic relationship of battery discharge capacities as well as its state of health (SOH), where the battery life cycle changes as the battery ages with time and cycles.

An Adaptive Noise Reduction Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Lithium-ion batteries are widely used in the electric vehicle industry due to their recyclability and long life. However, a failure of lithium-ion batteries can cause some catastrophic accidents, such as electric car battery explosion fires and so on. To prevent such harm from occurring, it is essential to monitor the remaining useful life of lithium-ion batteries and give early warning. In this paper, an adaptive noise reduction approach is proposed to predict the RUL (Remaining Useful Life) of lithium-ion batteries, which uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with wavelet decomposition to achieve adaptive noise reduction decomposition, and then inputs the obtained IMF (Intrinsic Mode Function) components into LS–RVM (Least Square Relevance Vector Machine) for training, prediction, and reconstruction, so as to achieve high-precision prediction of RUL. Moreover, in order to verify the validity of the model, the model in this paper is compared with other common models. The results demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.008678, 0.005002, and 0.006894, and that it has higher accuracy than the other common prediction models.

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.

Remaining useful life prediction method of EV power battery for DC fast charging condition

To realize the online rapid prediction of the remaining useful life (RUL) of electric vehicle (EV) power battery under direct current (DC) fast charging conditions and reduce the influence of complex health indicator (HI) on the prediction accuracy, an adaptive prediction method of the RUL of EV power battery based on whale swarm algorithm–long short-term memory (WSA-LSTM) algorithm is proposed in this paper. Firstly, a complete set of HI is built based on a dynamic data-driven application system (DDDAS) to ensure the real-time combination of condition monitoring data and simulation systems. Then, combined with the calculation of Pearson and Spearman correlation coefficients and the entropy weight method, the main features that affect the battery capacity are screened out to reduce the interference of secondary HI on RUL prediction. Thirdly, the whale swarm algorithm is used to globally optimize the hyperparameters of the long short-term memory to achieve rapid RUL prediction. Finally, we conduct simulation experiments based on real-time data under EV DC charging conditions. The simulation results show that the HI screening method proposed in this paper is effective, and the RUL prediction method can achieve a fast and accurate prediction of DC fast charging conditions.

Bayesian deep-learning for RUL prediction: An active learning perspective

Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction in the recent decade. Although with high precision and flexibility, DL methods need sufficient run-to-failure data to guarantee their performance. However, run-to-failure data is fairly expensive to obtain in many industrial applications. How to economically achieve high accuracy with few run-to-failure data becomes a critical and emergent issue. In this study, a Bayesian deep-active-learning framework is proposed for RUL prediction, which goes beyond traditional passive learning and introduces a novel active learning perspective. We use Bayesian neural networks with Monte Carlo dropout inference to predict RUL with uncertainty quantification for samples without run-to-failure labels. The prediction uncertainty is further used to develop an acquisition function for actively selecting target samples to obtain their run-to-failure labels. A recursive model training and active data selection mechanism are then developed to maintain accuracy while reducing the size of the training data. Two practical examples, one from a public bearing dataset and the other from our lab testing on battery degradation, are presented to demonstrate the proposed method. Experimental results demonstrate that 20 and 40% of run-to-failure data can be saved for the bearing and the battery RUL prediction, respectively.

Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network

It is imperative to accurately predict the remaining useful life (RUL) of lithium-ion batteries to ensure the reliability and safety of related industries and facilities. In view of the noise sequence embedded in the measured aging data of lithium-ion batteries and the strong nonlinear characteristics of the aging process, this study proposes a method for predicting lithium-ion batteries’ RUL based on the wavelet threshold denoising and transformer model. To specify, firstly, the wavelet threshold denoising method is adopted to preprocess the measured discharging capacity data of lithium-ion batteries to eliminate some noise signals. Second, based on the denoised data, the transformer model output’s full connection layer is applied to replace the decoder layer for establishing the RUL prediction model of lithium-ion batteries. Finally, the discharging capacity of each charging–discharging cycle is predicted iteratively, and then the RUL of lithium-ion batteries can be calculated eventually. Two groups of lithium-ion batteries’ aging data from the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the laboratory at Anqing Normal University (AQNU) are employed to verify the proposed method, individually. The experimental results demonstrate that this method can overcome the impacts of data measurement noise, effectively predict the RUL of lithium-ion batteries, and present a sound generalization ability and high accuracy.