Battery Remaining Useful Life Research Articles

Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering

To address the limitations in the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, stemming from model accuracy, particle degradation, and insufficient diversity in the particle filter (PF) algorithm, this paper proposes a battery RUL prediction method utilizing a randomly perturbed unscented particle filter (RP-UPF) algorithm, based on the constructed battery capacity degradation model. The method utilizes evaluation metrics adjusted R-squared (Radj2) and the Akaike Information Criterion (AIC) to select the battery capacity decline model C5 with a higher goodness of fit. The initial values for constructing the C5 model are obtained using the relevance vector machine (RVM) and nonlinear least squares methods. Based on the constructed battery capacity decline model C5, the RP-UPF algorithm is employed to estimate the posterior parameters and iteratively approach the true battery capacity decline curve, thereby predicting the battery’s RUL. The research results indicate that, using battery B0005 as an example and starting the prediction from the 50th cycle, the RUL prediction results obtained with the RP-UPF algorithm demonstrate reductions in absolute error, relative error, and probability density function (PDF) width of 2%, 2.71%, and 10%, respectively, compared to the PF algorithm. Similar conclusions were drawn for batteries B0006 and B0018. Under the constructed battery capacity degradation model C5, the RP-UPF algorithm shows higher prediction accuracy for battery RUL and a narrower PDF range compared to the PF algorithm. This approach effectively addresses the issue of particle weight degradation in the PF algorithm, providing a more valuable reference for battery RUL prediction.

Application of DBN-based KRLS method for RUL prediction of lithium-ion batteries

The Remaining Useful Life (RUL) of lithium batteries is vital for maintaining and safely operating the batteries, making precise RUL predictions highly significant. This paper introduces a method for predicting the RUL of lithium-ion batteries, utilizing a kernel adaptive filtering algorithm integrated with Deep Belief Networks (DBN). The method constructs a novel prediction model based on the Fixed-Budget Kernel Recursive Least Squares (FB-KRLS) algorithm. In this approach, the DBN extracts features from the original lithium battery data to reduce data complexity. The Square-root Cubature Kalman Filter (SCKF) is integrated with the FB-KRLS algorithm, employing a dual al-ternating learning strategy to improve the model's nonlinear fitting performance. The model was validated using NASA's lithium battery data, showing that the minimum val-ues for the MAPE, RMSE and MAE were 0.102%, 0.0016 and 0.0014, respectively. Therefore, the proposed method demonstrates potential for application in predicting the RUL of lithium-ion batteries.

A comprehensive framework for estimating the remaining useful life of Li-ion batteries under limited data conditions with no temporal identifier

The escalating applications of Lithium-ion (Li-ion) batteries in renewable energy and electric vehicles underscore the need for enhanced prognostics and health management systems to reduce the risk of sudden failures. Remaining useful life (RUL) determination is one of the most critical tasks in the field of battery prognostics nowadays. Even though statistical and machine learning (ML) methods have proven effective in research setups, many challenges prevent applying these prediction methods to real-life scenarios. These challenges include (1) scarcity of run-to-failure datasets with similar experimental conditions, (2) low data granularity when presented in capacity vs. discharge cycle pairs, and (3) lack of “temporal identifiers” in real-life scenarios. A temporal identifier is any label that provides knowledge about the current degradation state of a working battery. The research question developed for this study was, ‘Can the remaining useful life of a Li-ion battery having limited data without a temporal identifier be predicted?’ The specific aims were to estimate the temporal identifier of limited data and to predict the remaining useful life (RUL). An innovative framework incorporating reliability analysis and deep learning addresses these specific aims. Experimental data is used to test the framework's capabilities, limiting the training dataset to only three batteries and the testing dataset to a small sample (< 10 data points) of another battery. This new approach enabled the RUL prediction to achieve errors as low as ∼5 cycles and root mean square error of 6.24 cycles, outperforming other benchmark studies on Li-ion battery RUL prediction that use more battery degradation data without temporal identifier.

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.

Research on the Remaining Useful Life Prediction Method of Energy Storage Battery Based on Multimodel Integration.

The remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction approach lacks sufficient theoretical support at the same time. In this paper, we first analyze the prediction principles and applicability of models such as long and short-term memory networks and random forests, and then propose a method for predicting the RUL of batteries based on the integration of multiple-model, and finally validate the proposed model by using experimental data. The experimental results show that (1) for the proposed model, in the best case, the root-mean-square error (RMSE) does not exceed 0.14%, which has a stronger generalization; (2) for the comparison with the single model used, the average RMSE is reduced by 46.2%, 43.7%, and 80.6%, which has a better fitting performance. These results show that the model has good prediction accuracy and application prospects for predicting the RUL of energy storage batteries.

A Novel Hybrid Approach for Remaining Useful Life (RUL) and Short-Term Capacity Prediction of Batteries

Lithium-ion battery management requires an accurate determination of the batteries’ remaining usable lives. A novel hybrid method used for the RUL and short-term capacity prediction of batteries is presented in this manuscript. The proposed technique is combined with a White Shark Optimizer (WSO) with the Multi-kernel Support Vector Machine (MSVM); therefore, it is called the WSO-MSVM technique. The major goal of this WSO-MSVM method is to attain effective future capacities and RUL forecast for the Lithium-Ion Battery (LIB) with reliable management of uncertainty. The hybrid technique solves the difficulty of forecasting the RUL of LIB. This proposal trains the WSO-MSVM model using the LIB data. The trained model is utilized to forecast the capacity and RUL of LIBs. In this proposed system, capacity, current, and voltage are considered in the discharge operation. The proposed technique validates good reliability and prediction accuracy with this suitable mechanism. Compared to the existing techniques like artificial neural network (ANN), ant lion optimizer-optimized artificial neural network, and adaptive network-based fuzzy inference system, the proposed technique shows fewer errors under charge and discharge conditions. In addition, the uncertainty intervals of the proposed WSO-MSVM technique for the predicted RUL may include the actual remaining life of the battery. The proposed technique’s charging error is lower the other exciting methods.

A novel sequential estimation framework for battery state of health and remaining useful life based on sparse and limited data

Battery State of Health (SOH) estimation and Remaining Useful Life (RUL) prediction are significant for battery management systems in both electric vehicles and energy storage systems. Various battery Health Indicators (HIs) and abundant health information are generally utilized for SOH and RUL estimation. However, HIs and health information may not be available for various reasons such as sensor installation limitations, computational burden and communication delay of the system. To overcome the issue of sparse and limited data acquisition in real applications, this study proposed a novel battery SOH and RUL sequential estimation framework only utilizing sparse segments of time series voltage data. The Extreme Learning Machine (ELM) and Long Short Term Memory (LSTM) network are applied for SOH estimation and RUL prediction respectively. Locally Weighted Scatterplot Smoothing (LOWESS) is used for both data smoothing and sparse data reconstruction. Three common battery datasets are fully investigated for validation. The experiment results show well estimation accuracies in both battery SOH and RUL, demonstrating the effectiveness of the framework in limited data prediction. Several popular SOH and RUL estimation algorithms and two state-of-the-art works are compared to further elaborate the superiority of the proposed framework.

TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

A model-data-fusion method for real-time continuous remaining useful life prediction of lithium batteries

Ensuring the reliable operation of electric vehicles relies on accurate predictions of the remaining useful life (RUL) for lithium batteries. Existing model-based prediction methods face limitations due to insufficient full-life experimental data, while online monitoring data-driven methods lack empirical guidance and interpretability of the training data. To address these challenges, a model-data-fusion prediction method is proposed. Firstly, the degradation process is characterized using a generalized Wiener process model. Secondly, A Whale Optimization Particle Search Filter (WOS-PF) is introduced for real-time parameter updating of the degradation model. This approach ensures that even with a small sample size, parameters can be reliably estimated. Finally, leveraging the continuously updated parameters, a probability density function is derived to predict the RUL of lithium batteries in real-time. Experimental results indicate that this method enhances prediction accuracy compared to other existing methods, leading to better real-time monitoring of lithium battery health.

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%.

Design of regression neural network model for estimating the remaining useful life of lithium-ion battery

Lithium-ion batteries are widely utilized in a variety of transportation sectors, including highways, airplanes, and defensive military applications because of their positive features, which include a low self-discharge rate, raised operating voltage, prolonged cycle life, and high energy density. Nevertheless, during operation, the battery undergoes adverse reactions that may eventually cause material aging and capacity deterioration. For this reason, the prediction of remaining useful life (RUL) for LiBs is important and necessary for ensuring reliable system operation. Moreover, accurate RUL prediction can effectively offer maintenance strategies to certify the system’s dependability and safety. The impedance of the battery plays a vital role in the degradation process and hence its measurement accounts for the changes in the battery’s internal parameters as aging occurs. The objective of this paper is to create a forecasting model for the lifespan of batteries through the application of data analysis. Moreover, it aims to use a Regression Neural Network (RNN) based mathematical model to assess the degradation of the battery across diverse operational scenarios. The co-efficient of the RNN model is found by solving the RNN equations. In this research, MATLAB is employed for data analysis, utilizing open-source battery data sourced from the NASA dataset. The conclusive prediction outcomes indicate the effectiveness of the proposed methodology in accurately forecasting the battery RUL.

Modeling deterioration and predicting remaining useful life using stochastic differential equations

The deterioration of engineering systems might reduce the system reliability and prompt maintenance operations that may disrupt the ability of the systems to provide regular service. Estimating the Remaining Useful Life (RUL) of the system requires an understanding of the deterioration processes acting on it. Recent formulations have shifted the focus of deterioration modeling from the system as a whole to the individual, time-varying state-variables that define the characteristics of the system. These state-dependent formulations depend on the selected models for the evolution of the state-variables over time. However, most available models rely on simplifying assumptions that disregard the true nature of the processes, either by discretizing the time domain or by assuming independence among the several processes acting on the system. This paper proposes using a system of Stochastic Differential Equations (SDEs) to model the state variables' evolution. The proposed formulation captures the continuous nature of the processes and accounts for the possible interactions among them. In addition, results from stochastic calculus can be used to facilitate the simulation of the processes and to obtain closed-form solutions for the distribution of the state variables over time and the RUL of the system. Moreover, the proposed SDEs can be calibrated based on observations of the state variables, should those be obtained via testing/monitoring or simulations. A procedure for calibration is introduced, and several numerical examples are investigated to highlight the different scenarios encountered in practice. Finally, the proposed SDE formulation is used to predict the RUL of lithium-ion batteries using actual Structural Health Monitoring data.

A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images

Accurately predicting the state of health (SOH) and remaining useful life (RUL) is significant for battery-powered electric devices. Since the images generated from raw cyclic data of the batteries can reveal more inherent battery degradation characteristics than raw cyclic data studied in most of previous studies, the bimodal images generated by two methods, such as images of curves and Gramian angular field (GAF), are effectively integrated into a unified deep learning framework to predict the health states of lithium-ion batteries. To this end, a bimodal fusion regression network (BFRN) is elaborately designed to jointly predict SOH and RUL of the battery, in which the features of bimodal images for the battery are fully extracted, interactively fused and aggregated by specific-designed network modules. Experiments on the public MIT/Stanford dataset indicate that the proposed method can achieve accurate joint state prediction for lithium-ion batteries, with the coefficient of determination (R2) for the SOH and RUL tasks are 0.98 and 0.93, respectively, which is superior to existing deep learning methods. Experiments on another public battery dataset demonstrate that it can be practical and generalized for the batteries with other chemistries.

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

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

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.

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.

Fusion model based RUL prediction method of lithium-ion battery under working conditions

Under working conditions, since the remaining useful life (RUL) prediction of lithium-ion battery is subject to uncertainties of random charging and discharging, and infeasibility of battery capacity test, a fusion model based RUL prediction method was proposed. First, the feature learning method of lithium-ion batteries was developed by synthesizing manual extraction and one-dimensional convolutional neural network (1DCNN) extraction. Then, a fused method was proposed to estimate the historical available capacity through exploring the spatial and temporal relationship of features, and the long short-term memory (LSTM) network model was adopted for predicting the RUL of lithium-ion battery. The proposed method was verified through the comparison of different methods, and the results show that it can realize highly precise and stable capacity estimation and RUL prediction under working conditions.

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.

An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration

Accurate prediction of remaining useful life (RUL) is crucial to the safety and reliability of the lithium-ion battery management system (BMS). However, the performance of a lithium-ion battery deteriorates nonlinearly and is heavily affected by capacity-regeneration phenomena during practical usage, which makes battery RUL prediction challenging. In this paper, a rest-time-based regeneration-phenomena-detection module is proposed and incorporated into the Coulombic efficiency-based degradation model. The model is estimated with the particle filter method to deal with the nonlinear uncertainty during the degradation and regeneration process. The discrete regeneration-detection results should be reflected by the model state instead of the model parameters during the particle filter-estimation process. To decouple the model state and model parameters during the estimation process, a dual-particle filtering estimation framework is proposed to update the model parameters and model state, respectively. A kernel smoothing method is adopted to further smooth the evolution of the model parameters, and the regeneration effects are imposed on the model states during the updating. Our proposed model and the dual-estimation framework were verified with the NASA battery datasets. The experimental results demonstrate that our proposed method is capable of modeling capacity-regeneration phenomena and provides a good RUL-prediction performance for lithium-ion batteries.

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.