Remaining Useful Life Prediction for Lithium-Ion Batteries With a Hybrid Model Based on TCN-GRU-DNN and Dual Attention Mechanism

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 article, a hybrid model based on temporal convolutional network (TCN) -gated recurrent unit (GRU) -deep neural network (DNN) and dual attention mechanism is proposed for enhancing the RUL prediction accuracy of lithium-ion batteries. First, the TCN with a feature attention mechanism is applied to form an encoder module to capture the battery capacity regeneration phenomenon, and then, a 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 DNN. We conducted experiments on two datasets 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 (R^2). The experimental results demonstrate that the proposed model can achieve a more accurate prediction for RUL on lithium-ion batteries, in which RMSE does not exceed 2. 407% in the NASA dataset and does not exceed 0. 897% in the CALCE lithium-ion battery dataset.