Life Prediction Of Lithium-ion Batteries Research Articles

Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model

The gradual decrease capacity serves as a pivotal health indicator, reflecting the condition of lithium-ion batteries. Accurate forecasting of capacity can ascertain the remaining lifespan of these batteries at any given cycle, which is crucial for managing batteries in electric vehicles. This paper proposes an Autoregression with Exogenous Variables (AREV) model, which continually updates itself through a sliding window, offering predictions of battery state of health and remaining useful life, which extends battery prognostics at a fixed operating condition to different operating conditions. In addition, unlike most models that require multiple battery data of the same type for training, the proposed model only requires the use of fragmented data of the target battery with length around 30-50 cycles for capacity prediction and determines battery life based on battery failure thresholds. The above two points enable this model to be updated online without the need for any offline training. Finally, four different types of battery dataset , with different chemical substances and different charge and discharge conditions (especially dataset that follows random walk discharging profile to stimulate the real power consumption process) , are applied to verify the effectiveness and robustness of proposed RUL prediction approach. It shows that the proposed model can accurately predicting future capacity values. Timely warning signals can be issued before the end of life of battery, thereby ensuring the safe driving of electric vehicles and timely battery replacement.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Simplified Electrochemical Model and TSO-TCN

Accurate prediction of the remaining useful life (RUL) of lithium-ion battery is critical in practical applications, but is challenging due to the presence of multiple aging pathways and nonlinear degradation mechanisms. In this paper, a method for RUL prediction is proposed combined with battery capacity aging mechanism based on transient search optimization (TSO)-temporal convolutional network (TCN) algorithm. First, the particle swarm optimization algorithm is used to derive three health indicators directly related to capacity loss from a simplified electrochemical model. Then, the TCN parameters are optimized with transient search algorithm to obtain the optimal prediction model. Finally, the RUL prediction are compared with other typical algorithms, and the results show that the proposed method can accurately predict the RUL of lithium-ion battery, and the life prediction error is within 10 cycles. Compared to TCN, the prediction results remain accurate even with less training data, and the error metrics are reduced by about 50% with the maximum error only 7 cycles from the 250th charge/discharge cycle.

Remaining Useful Life Prediction of Lithium-Ion Batteries With Limited Degradation History Using Random Forest

Remaining Useful Life Prediction of Lithium-Ion Batteries With Limited Degradation History Using Random Forest

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

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

Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model

Aiming at the problem that the cycle life of the battery is hard to be accurately estimated, a segmented capacity degradation model is established based on the trend of capacity degradation rate. By applying a genetic algorithm (GA) to optimize the model parameters, GA-Weibull and GA-SVR (support vector regression) degradation models and their corresponding segmented models are established, and the battery life is calculated. Furthermore, a method taking the RMSE as the target is put forward to calculate the knee-point of capacity degradation. Finally, the life is predicted by segmented models. The results show that compared with unsegmented models, describing the process of capacity degradation by segmented models reduces the error notably, makes the determination coefficient closer to 1, and improves the model accuracy effectively. Later, the proposed method makes the fitting error of the segmented model smaller than that of Bacon-Watts method, thus obtaining a more accurate knee-point. In addition, it is found that there exists linear relationship between the estimated knee-point and the battery life. Finally, the prediction results based on segmented model show that the prediction accuracy of segmented GA-SVR model is higher, and the MAPE is only 4.32%. The relevant results can provide some guidance for the reliability evaluation and product quality management of lithium-ion batteries, which is conducive to establishing more accurate models for battery life prediction.

A novel LFSSA-LSTM algorithm for the remaining useful life prediction of lithium-ion batteries in whole life cycle

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential to improve the safety of battery systems and optimize battery management strategies. Therefore, a new algorithm combining Sparrow search algorithm (SSA) improved by the Levy flight method and long short-term memory neural network (LSTM) is proposed in this paper. By extracting the National Aeronautics and Space Administration (NASA) battery aging datasets, the battery correlated health characteristics are obtained, and the deterioration trend of battery performance is effectively described. The sparrow search algorithm improved by the Levy flight method enhances the ability of the algorithm to jump out of the optimal solution. By using the improved algorithm, the hyper-parameter optimization problem of the long short-term memory network is solved, and the accuracy of the algorithm is improved. Finally, the probability density function is used to predict the remaining useful life of lithium-ion batteries, which effectively prevents the uncertainty caused by a single prediction result. Compared with long short-term memory neural network and the one optimized by a traditional sparrow search algorithm, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed algorithm are kept at 1.77 %, 2.24 %, and 1.25 %, respectively. The results indicate that the algorithm has better accuracy and fitting performance.

Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model

Remaining useful life prediction of lithium-ion batteries based on performance degradation mechanism analysis and improved Deep Extreme Learning Machine model

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Neural Network and Adaptive Unscented Kalman Filter

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Neural Network and Adaptive Unscented Kalman Filter

A novel multiple kernel extreme learning machine model for remaining useful life prediction of lithium-ion batteries

The safety and decommissioning of lithium-ion batteries (LIBs) are closely related to the remaining useful life (RUL). Accurate life prediction of LIBs can prevent safety issues and recycling at the end of life. This paper proposes a method based on the multiple kernel extreme learning machine (MKELM) to improve the stability and accuracy of LIBs life prediction. According to the measured cyclic aging data of the 18650 battery, in-depth analysis of charging curves, extract the equal voltage rise charging time, the constant voltage charging time and the equal time voltage drop in the static state as the health factors, using MKELM to predict the remaining cycle life of LIBs, and using the whale algorithm to optimize the nuclear parameters of MKELM. The proposed method is simulated and verified by the actual LIBs data. When the training set is the first 50% of the data, the whale optimization algorithm-MKELM (WOA-MKELM) algorithm predicts the average error cycle number of the three batteries is 15.6 cycles. After adding 10% of the training set, the average number of forecast error cycles reduced to 5 cycles, and the results show that the method has high stability and accuracy.

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.

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.

Remaining useful life prediction of lithium-ion batteries combined with SVD-SDAE and support vector quantile regression

Lithium-ion batteries are important energy storage materials, and the prediction of their remaining useful life has practical importance. Since traditional feature extraction methods depend on parameter settings and have poor adaptability, singular value decomposition was used to extract 15 health indicators from the degradation data of lithium-ion batteries. To eliminate redundancy among the extracted health indicators, Spearman correlation analysis was subsequently used to determine the most appropriate health indicators. On this basis, the selected health indicators were processed by the denoising stack autoencoder, and a fusion health indicator was obtained. Finally, the support vector quantile regression model was used to predict the battery capacity interval by the fusion health indicator. The National Aeronautics and Space Administration battery dataset and Massachusetts Institute of Technology battery dataset were used to verify the validity and generalizability of our proposed model, and our proposed model was compared with the existing four classical prediction models. The experimental results showed that our proposed prediction model had higher prediction accuracy and better robustness than the other models and could effectively improve the prediction effect of the remaining useful life of batteries. The mean value of the root mean square error of the predicted results using our proposed model remained within 1.3%, and the mean value of the coefficient of determination was above 0.97.

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.

Remaining Useful Life Prediction of Lithium-Ion Batteries: A Temporal and Differential Guided Dual Attention Neural Network

Remaining Useful Life Prediction of Lithium-Ion Batteries: A Temporal and Differential Guided Dual Attention Neural Network

Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter

Accurate prediction of capacity and remaining useful life (RUL) for lithium-ion batteries (LIBs) is crucial for ensuring safe and reliable operation of electric vehicles. However, the battery capacity degradation and external environmental disturbances make it still challenging to achieve this goal. In this article, an accurate capacity and RUL prediction method is proposed by combining improved particle swarm optimization (IPSO) with particle filter (PF) algorithms. First, the parameters of particle swarm optimization (PSO) algorithm are adjusted by adaptive weights to avoid the problem of local optimal solution. Subsequently, the optimal particle searched by IPSO is updated continuously by the PF algorithm to achieve a more accurate posterior estimation. Finally, the proposed IPSO-PF method is verified by two independent and public datasets of NASA and CALCE batteries. The results validate that the proposed method has high precision and generalizability in predicting the capacity and RUL of LIBs even at various charging rates and battery types.

AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries

For Lithium-ion (Li-ion) batteries, problems such as material aging and capacity decay lead to battery performance degradation or even catastrophic events. Predicting Remaining Useful Life (RUL) is an effective way to indicate the health of Li-ion batteries, which helps to improve the reliability and safety of battery-powered systems. We propose a novel neural network, AttMoE, which combines an attention mechanism with Mixture of Experts (MoE), to capture the capacity fade trend for battery RUL prediction. When facing the problem that raw data collected from sensors are always full of noise, AttMoE uses a dropout mask to denoise the raw data. For RUL prediction, one key idea is that the attention mechanism captures the long-term dependencies between elements in a sequence and more attention is paid to the important features that contain more degradation information; another key idea is that MoE uses many experts to increase model capacity to achieve better representations. Finally, we conducted experiments using two public data sets to show that AttMoE is effective in RUL prediction and achieves up to 10%–20% improvement in terms of Relative Error (RE). Our projects are all open source and are available at https://github.com/XiuzeZhou/RUL.

A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments

Accurate remaining useful life (RUL) estimation is crucial for the normal and safe operations of lithium-ion batteries (LIBs). Traditionally, every cycle’s maximum discharging capacity should be measured and then serve as a model input to predict iteratively the degradation trajectory. Unfortunately, full discharge stages are not always present in practice. Herein, this study presents a hybrid approach consisting of signal decomposition and deep learning to overcome the above limitations. Firstly, for the collected discharging fragments, the convolutional neural networks model predicts every cycle’s maximum discharging capacity which combines to form a predicted capacity degradation curve before the start point of RUL prediction. Then, via empirical mode decomposition, this curve’s global degradation trend is extracted and serves as the subsequent model input. Finally, the entire degradation trajectory and RUL value could be inferred based on the well-trained gated recurrent unit-fully connected model. The superior prediction performance of the proposed method is verified on two open battery datasets. All the estimation errors can be maintained within 7.0% based on the discharging fragment of the ∼20% capacity ratio ranges from 40% to 60% of the degradation data. This result illustrates the promising accuracy and robustness of the developed LIBs RUL estimation method, especially for not full discharge process in practice.

Cycle life and influencing factors of cathode materials for lithium-ion batteries--a case study of lithium-cobalt oxides

Abstract Lithium-cobalt oxide has become a new generation of highly promising anode materials for lithium-ion batteries due to its low price, environmental friendliness, high platform voltage, and high theoretical capacity. In this paper, the working characteristics and related parameters of lithium-ion batteries are sorted out, and the influence factors of the decline mechanism of lithium-ion batteries are investigated from the perspective of chemical composition. Regarding the cycle life of lithium-ion battery cathode materials, this paper establishes a cycle life prediction model for lithium-ion batteries based on the LSTM model. It optimizes the hyperparameters of the model using the PSO algorithm. In addition, this paper also prepared lithium cobalt oxide cathode materials and carried out a validation analysis of the factors affecting their electrochemical performance. It is found that the cycle life prediction of lithium-ion battery based on LSTM has an RMSE of 3.27%, and the capacity of lithium cobalt oxide soft pack full battery decays from 249.81mAh to 137.04mAh at 26°C. The cycle life of its lithium cobalt oxide lithium-ion battery is around 250 cycles, and the average decay is 0.445mAh during one charge/discharge. Different charge/discharge cycles and diversity will have a significant effect on the cycle life of lithium-ion battery cathode materials, and it is necessary to pay attention to the parameter changes in the preparation of lithium cobalt oxide cathode materials in order to increase the cycle life of lithium-ion batteries.

A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved. To address this challenge, this paper proposes a novel deep learning model, the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features. Additionally, we devise an ensemble predictor based on a Mixture-of-Experts (MoE) architecture to generate reliable RUL predictions. The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods, providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process. Our code and dataset are available at the website of github.

Remaining useful life prediction for lithium-ion batteries with an improved grey particle filter model

Accurate prediction of remaining useful life is of great value for the maintenance and replacement of electric vehicles lithium-ion batteries. This paper aims to present a grey particle filter model for improving remaining useful life forecast accuracy. Firstly, a grey particle filter model with recursive least square parameter estimation is built, and the proposed model’s parameters are trained. Secondly, RUL is predicted by using the parameters and proposed model. Finally, NASA lithium-ion battery open data set was used for verification. The model was evaluated from two perspectives of RUL accuracy and mean absolute percentage error. Predictions are also made for lithium-ion batteries under conditions of elevated temperature. The findings demonstrate that the proposed model outperforms the other models.