Battery Remaining Useful Life Research Articles

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.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM

Predicting the Remaining Useful Life (RUL) of lithium-ion batteries is one of the core tasks in battery health management. This study aims to enhance the accuracy of RUL prediction by proposing a battery RUL prediction method that integrates source domain battery iteration transfer with Long Short-Term Memory (LSTM) neural networks. Firstly, multiple batteries with known degradation trends are utilized as source domain batteries, and combined with full-lifetime capacity degradation data to construct the Source Domain Battery Iterations Module (SIM) to obtain the optimal LSTM pre-training model. Secondly, the pre-trained model is transferred to the target domain and fine-tuned using the target domain training set. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction. The effectiveness of the algorithm is validated on three open-source datasets. Experimental results demonstrate that in scenarios where the source domain and target domain battery types are the same, the absolute error of RUL prediction is less than 2 cycles. Moreover, in cases where the battery types differ, except for the 20% prediction starting point, the RUL prediction error is less than 10 cycles, indicating a high level of prediction accuracy.

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

Remaining Useful Life Prediction of Lithium‐Ion Batteries Based on a Combination of Ensemble Empirical Mode Decomposition and Deep Belief Network–Long Short‐Term Memory

The prediction of remaining useful life (RUL) for lithium‐ion batteries is a critical component of electric vehicle battery management systems. However, during the aging process, batteries exhibit an overall declining trend in capacity curves, coupled with capacity regeneration and localized fluctuations. Directly modeling this degradation trend based on the original capacity curve proves challenging, leading to reduced accuracy in RUL prediction. This article introduces a hybrid method to enhance the precision of battery RUL prediction. Utilizing the ensemble empirical mode decomposition technique, the battery's capacity degradation sequence is decomposed into intrinsic mode functions (IMFs) with varying degrees of fluctuations, along with a residue that characterizes the battery's overall declining trend. Subsequently, deep belief networks and long short‐term memory networks are established to predict the residue and IMFs separately. The combined results from these models yield the final battery RUL prediction. Finally, the effectiveness of this approach is validated on the NASA battery dataset, with diverse training periods and prediction time steps. Experimental results demonstrate that the root mean square error of predictions for all four batteries remains below 2%.

The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model

Energy storage has a flexible regulatory effect, which is important for improving the consumption of new energy and sustainable development. The remaining useful life (RUL) forecasting of energy storage batteries is of significance for improving the economic benefit and safety of energy storage power stations. However, the low accuracy of the current RUL forecasting method remains a problem, especially the limited research on forecasting errors. In this paper, a method for forecasting the RUL of energy storage batteries using empirical mode decomposition (EMD) to correct long short-term memory (LSTM) forecasting errors is proposed. Firstly, the RUL forecasting model of energy storage batteries based on LSTM neural networks is constructed. The forecasting error of the LSTM model is obtained and compared with the real RUL. Secondly, the EMD method is used to decompose the forecasting error into many components. The time series of EMD components are forecasted by different LSTM models. The forecasting values of different time series are added to determine the corrected forecasting error and improve the forecasting accuracy. Finally, a simulation analysis shows that the proposed method can effectively improve the forecasting effect of the RUL of energy storage batteries.

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

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

Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.

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

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

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

With the wide utilization of lithium-ion batteries in the fields of electronic devices, electric vehicles, aviation, and aerospace, the prediction of remaining useful life (RUL) for lithium batteries is important. Considering the influence of the environment and manufacturing process, the degradation features differ between the historical batteries and the target ones, and such differences are called individual differences. Currently, lithium battery RUL prediction methods generally use the characteristics of a large group of historical samples to represent the target battery. However, these methods may be vulnerable to individual differences between historical batteries and target ones, which leads to poor accuracy. In order to solve the issue, this paper proposes a prediction method based on transfer learning that fully takes individual differences into consideration. It utilizes an extreme learning machine (ELM) twice. In the first stage, the relationship between the capacity degradation rate and the remaining capacity is constructed by an ELM to obtain the adjusting factor. Then, an ELM-based transfer learning method is used to establish the connection between the remaining capacity and the RUL. Finally, the prediction result is adjusted by the adjusting factor obtained in the first stage. Compared with existing typical data-driven models, the proposed method has better accuracy and efficiency.

A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries.

As a crucial energy storage for the spacecraft power system, lithium-ion batteries degradation mechanisms are complex and involved with external environmental perturbations. Hence, effective remaining useful life (RUL) prediction and model reliability assessment confronts considerable obstacles. This article develops a new RUL prediction method for spacecraft lithium-ion batteries, where a hybrid data preprocessing-based deep learning model is proposed. First, to improve the correlation between battery capacity and features, the empirically selected high-dimensional features are linearized by using the Box-Cox transformation and then denoised via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, the principal component analysis (PCA) algorithm is employed to perform feature dimensionality reduction, and the output of PCA is further processed by the sliding window technique. Third, a multiscale hierarchical attention bi-directional long short-term memory (MHA-BiLSTM) model is constructed to estimate the capacity in future cycles. Specifically, the MHA-BiLSTM model can predict the RUL of lithium-ion batteries by considering the correlation and significance of each cycle's information during the degradation process on different scales. Finally, the proposed method is validated based on multiple types of experiments under two lithium-ion battery datasets, demonstrating its superior performance in terms of feature extraction and multidimensional time series prediction.

Dual time-scale state-coupled co-estimation of state of charge, state of health and remaining useful life for lithium-ion batteries via Deep Inter and Intra-Cycle Attention Network

The reliable and safe battery operations demand accurate estimation of battery states in the battery management system (BMS). Due to the complex electrochemical process, it is challenging to directly measure SOH and RUL of batteries for online applications. Moreover, the battery aging can strongly influence SOC estimation. The paper proposes a SOC, SOH, and RUL co-estimation method based on Deep Inter and Intra-Cycle Attention Network (DIICAN). From BMS data, the dual time-scale inter-cycle and intra-cycle features are automatically extracted with neural networks. The state degradation attention (SDA) unit is employed to recognize the state evolution pattern and evaluate battery degradation degree based on inter-cycle features. In addition, the extracted inter-cycle and intra-cycle features are simultaneously coupled for the SOC estimation to calibrate the capacity degradation. The mean absolute error (MAE) and root mean square error (RMSE) of the state of health (SOH) estimation are 0.30% and 0.43%, respectively. For the remaining useful life (RUL) estimation, the MAE is 178.5, and the RMSE is 230.3. With state coupling, the MAE of the state of charge (SOC) estimation decreases from 2.36% to 1.00%, and the RMSE decreases from 3.27% to 1.36% over the entire lifespan. In summary, the proposed method can not only accurately co-estimate SOH and RUL, but improve SOC estimation accuracy over the entire battery life.

Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation

Accurate remaining useful life (RUL) prediction technique matters in lithium-ion battery use, optimization, and replacement. This article presents an RUL prediction method combining the sliding window (SW) technique and Box-Cox transformation (BCT). This method achieves online RUL prediction with acceptable accuracy, is independent of offline training data, and only brings a low computational burden. The SW technique is employed for gathering a certain amount of capacity data, which is subsequently transformed using BCT-related techniques to construct a capacity degradation model. The identified model is then extrapolated to predict battery RUL, and the prediction uncertainties are calculated through Monte Carlo (MC) simulation. A simple implementation shows that this hybrid method outperforms the history-based polynomial and BCT methods in battery RUL prediction. Given the segmented capacity degradation trend, a constraint can be imposed on the model parameter for optimization purposes. Experimental results demonstrate that the optimized method obtains lower root-mean-square errors (RMSEs) of RUL predictions during the last 20 % of the battery lifetime than the original one, and the precise RULs are predicted with standard deviations mainly within [1, 10] cycles.

A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles

Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learning model that seamlessly integrates convolutional neural network (CNN) and gated recurrent unit (GRU) architectures. Our primary goal is to significantly improve the accuracy of RUL predictions for LIBs. Our model excels in its predictive capabilities by skillfully extracting intricate features from a diverse array of data sources, including voltage (V), current (I), temperature (T), and capacity. Within this novel architectural design, parallel CNN layers are meticulously crafted to process each input feature individually. This approach enables the extraction of highly pertinent information from multi-channel charging profiles. We subjected our model to rigorous evaluations across three distinct scenarios to validate its effectiveness. When compared to LSTM, GRU, and CNN-LSTM models, our CNN-GRU model showcases a remarkable reduction in root mean square error, mean square error, mean absolute error, and mean absolute percentage error. These results affirm the superior predictive capabilities of our CNN-GRU model, which effectively harnesses the strengths of both CNNs and GRU networks to achieve superior prediction accuracy. This study draws upon NASA data to underscore the outstanding predictive performance of the CNN-GRU model in estimating the RUL of LIBs.