Life Prediction Of Lithium-ion Batteries Research Articles

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.

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

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.

Transfer learning based remaining useful life prediction of lithium-ion battery considering capacity regeneration phenomenon

Lithium-ion battery (LIB) has been widely used in various energy storage systems, and the accurate remaining useful life (RUL) prediction for LIB is critical to ensure the normal operation of system. However, the capacity regeneration (CR) phenomenon caused by the non-working state of LIB will seriously affect the capacity degradation trajectory of LIB, thus leading to an inaccurate RUL prediction result. To solve this problem, this paper proposes a non-parametric CR detection algorithm to detect the CR phenomenon online and quantitatively correct the prediction errors caused by different CR phenomena. In addition, a data reconstruction algorithm and a parameter update scheme are proposed to solve the problem of unit-to-unit difference among LIBs. Finally, the long and short term memory neural network-based transfer learning model is applied to predict the RUL of target LIB. The effectiveness of the proposed model is verified by the real LIB dataset of NASA. Compared with some existing RUL prediction models considering CR phenomenon, the proposed model exhibits higher accuracy and better universality.

Remaining Useful Life Prediction of Lithium-Ion Battery Based on Adaptive Fractional Lévy Stable Motion with Capacity Regeneration and Random Fluctuation Phenomenon

The capacity regeneration phenomenon is often overlooked in terms of prediction of the remaining useful life (RUL) of LIBs for acceptable fitting between real and predicted results. In this study, we suggest a novel method for quantitative estimation of the associated uncertainty with the RUL, which is based on adaptive fractional Lévy stable motion (AfLSM) and integrated with the Mellin–Stieltjes transform and Monte Carlo simulation. The proposed degradation model exhibits flexibility for capturing long-range dependence, has a non-Gaussian distribution, and accurately describes heavy-tailed properties. Additionally, the nonlinear drift coefficients of the model can be adaptively updated on the basis of the degradation trajectory. The performance of the proposed RUL prediction model was verified by using the University of Maryland CALEC dataset. Our forecasting results demonstrate the high accuracy of the method and its superiority over other state-of-the-art methods.

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.

Capacity and remaining useful life prediction for lithium-ion batteries based on sequence decomposition and a deep-learning network

Capacity and remaining useful life prediction for lithium-ion batteries based on sequence decomposition and a deep-learning network

Remaining Useful Life Prediction of Lithium-ion Batteries Based on Optimized Prophet Model

In this paper, we propose an algorithm to optimize the Prophet model by combining the improved sparrow search algorithm. Firstly, the original Prophet model is theoretically analyzed and regression fitted in a realistic data set to verify the feasibility of the model for lithium-ion battery RUL prediction. Secondly, the parameters of the Prophet model are optimized by using the modified sparrow search algorithm to enhance its prediction accuracy. The simulation results show that the model proposed in this paper provides better performance in lithium-ion battery RUL prediction and outperforms the methods involved in this paper for comparison in terms of prediction accuracy.

Equivalent circuit simulated deep network architecture and transfer learning for remaining useful life prediction of lithium-ion batteries

Equivalent circuit simulated deep network architecture and transfer learning for remaining useful life prediction of lithium-ion batteries

A data reconstruction-based Monte Carlo method for remaining useful life prediction of lithium-ion battery with few historical samples

Lithium-ion battery (LIB) has been widely used in many energy storage systems, and the accurate remaining useful life (RUL) prediction of LIB is essential to ensure the safe operation of these systems. However, for the LIB installed in a new system, there are only few historical samples available for RUL prediction, which makes it difficult to build accurate RUL prediction model for LIB. Therefore, this paper proposes a data reconstruction-based Monte Carlo method to solve the problems caused by few historical samples. First, a Wiener process-based data reconstruction algorithm is used to reconstruct historical dataset, providing sufficient prior information for the Monte Carlo method. Then, Bayesian theory and a parameter update scheme are proposed to update the reconstructed dataset and the corresponding parameter distributions, so that the training data of RUL prediction model can be closer to the data of target LIB. Finally, long short-term memory neural network is applied for RUL prediction. The effectiveness of our model is verified by two real LIB datasets. Compared with some existing RUL prediction models of LIB, the proposed model has better generality and higher prediction accuracy under the condition of few historical samples.

A data-driven prediction model for the remaining useful life prediction of lithium-ion batteries

Accurate prediction of remaining useful life (RUL) can ensure the safety and reliability of power batteries during operation, reduce the failure rate and operating costs, and enhance user experience. However, battery degradation is a complex, nonlinear dynamic process that is difficult to fully comprehend and predicting RUL remains a significant challenge. To address this issue, the hybrid data-driven prediction model PCA-CNN-BiLSTM was proposed in this paper, which combines principal component analysis (PCA), convolutional neural network (CNN), and bi-directional long short-term memory (Bi-LSTM) network. PCA was applied to downscale and whiten the health factor (HF) to maximize the extraction of important features of lifespan decay, while reducing the correlation between features. The convolution kernel of the CNN was used to explore the local region feature information of the input information and search for the common patterns among the neighboring data. Additionally, the model parameters and computational efforts were reduced through pooling. Finally, battery RUL prediction was achieved using Bi-LSTM, which has the advantages of effectively enhancing model accuracy and reducing the risk of over-fitting by taking into account both past and future data. The performance of the proposed model was evaluated utilizing NASA and CALCE's battery datasets, and the results suggest that it exhibits a high level of accuracy across various datasets. Compared to other methods, the PCA-CNN-BiLSTM method has the best performance indicators for predicting battery RUL, including RMSE, MAE, MAPE, RULe and DOL. This indicates that the proposed model has better fitting performance, accuracy, robustness, and generalization ability.