The instability of lithium-ion batteries may result in system operation failure and cause safety accidents, thus predicting the remaining useful life (RUL) accurately is helpful for reducing the risk of battery failure and extending its useful life. In this paper, a hybrid model based on TCN-GRU-DNN and dual attention mechanism is proposed for enhancing the RUL prediction accuracy of lithium-ion batteries. Firstly, the Temporal Convolutional Network (TCN) with a feature attention mechanism is applied to form an encoder module to capture the battery capacity regeneration phenomenon, and then a Gated Recurrent Unit (GRU) with a temporal attention mechanism is denoted as a decoder module for better characterizing the decay trend of the capacity series. Finally, the final prediction results are output through a Deep Neural Network (DNN). We conducted experiments on two data sets NASA and CALCE. The prediction errors are presented in subsequent experiments under different evaluation standards such as absolute error (AE), root mean square error (RMSE), mean absolute error (MAE), and R-squared error (R²). The experimental results demonstrate that the proposed model can achieve a more accurate prediction for RUL on lithium-ion batteries, with RMSE does not exceed 2.407% in the NASA dataset and does not exceed 0.897% in the CALCE lithium-ion battery dataset respectively.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries enables their timely replacement and ensures the proper operation of equipment. This study presents a novel hybrid approach for predicting nonlinear and nonsmooth battery capacity sequences. To develop this approach, first, the original battery capacity sequence was adaptively decomposed through Northern Goshawk Optimization (NGO)-Variational Mode Decomposition (VMD). NGO-VMD could efficiently extract useful information at different scales and could considerably reduce the complexity of the battery capacity sequence. Second, the decomposed sequences were grouped into high- and low-frequency components on the basis of the over-zero rate. A convolutional neural network-bidirectional long short-term memory model was then constructed to predict the low-frequency components, and a temporal convolutional network-attention mechanism-deep neural network model was developed to predict the high-frequency components. In addition, a tensor-based transfer learning approach was employed to predict the low-frequency components of capacity sequences from same-type batteries. The RUL prediction errors of the proposed approach did not exceed 2 cycles, which was fewer than those of other comparable approaches. Accordingly, the proposed approach has favorable generalizability and robustness.

The prediction of the remaining useful life (RUL) of widely used lithium-ion batteries (LIBs) is of great importance. Existing techniques struggle to balance prediction accuracy with execution time. To achieve accurate RUL prediction quickly, a hybrid RUL prediction method for LIBs has been developed. This method first employs a channel-wise deep residual shrinkage network to adaptively extract features from input data enhancing important information features and suppressing ineffective ones based on the significance of the feature information. Subsequently, a bidirectional gated recurrent unit is used to extract bidirectional temporal features from the processed data, and an attention mechanism is introduced to maximize the extraction of significant temporal mutual information. Finally, a fully connected layer transfer strategy is applied to transition the model from offline training to online prediction, which avoids unstable predictions due to random model initialization and significantly improves the model’s computational efficiency. The simulation results show that the root mean square error of the proposed method did not exceed 1.77% and the mean absolute error did not exceed 1.44% on the NASA dataset. Consequently, the proposed method can achieve accurate online RUL prediction accuracy for LIBs.

With the rapid development of the new energy vehicle industry, lithium-ion batteries (LIBs) have become widely used, therefore, an accurate prediction of its remaining useful life (RUL) is essential. However, LIBs exhibit a capacity regeneration (CR) phenomenon during degradation, resulting in a volatile and nonlinear capacity degradation curve. This challenges the prediction model’s adaptability and accuracy in predicting the battery’s RUL. To address this challenge, we propose a method that combines sequence decomposition with deep learning to predict the RUL of LIBs. First, the battery capacity sequence is adaptively decomposed using time-varying filtered empirical mode decomposition. The resulting components are reconstructed into high-frequency and low-frequency sequences based on the over-zero rate, significantly reducing the time series complexity and mitigating the impact of the CR on predictions. Second, we designed the beluga whale optimization algorithm to optimize the combined ordered neurons long short-term memory and convolutional neural network, as well as the AdaBoost-based relevance vector machine, for predicting the low-frequency and high-frequency components, respectively. This approach aims to enhance prediction accuracy. Finally, the predictions for the low-frequency and high-frequency components are combined to yield the final prediction result. To test the model’s generalization and robustness, we conducted experiments on the NASA and CALCE datasets. We evaluated the model using metrics such as root mean squared error, mean absolute error, absolute error, prediction interval coverage probability, and prediction interval normalized average width, and the results demonstrated superior performance compared to other models.