A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery. (January 2023)

• Record Type: Journal Article
• Authors: Gao, Dexin; Liu, Xin; Zhu, Zhenyu; Yang, Qing
• Is Part Of: Measurement and control. Volume 56:Number 1/2(2023)
• Page Start: 371
• Page End: 383
• Publication Date: 2023-01
• Subjects: Lithium-ion battery -- remaining useful life -- one dimensional convolutional neural network -- bidirectional long short-term memory; Automatic control -- Periodicals; Engineering instruments -- Periodicals; Production engineering -- Periodicals; 629.8
• DOI: 10.1177/00202940221103622
• Languages: English
• ISSNs: 0020-2940

Abstract

For accelerating the technology development and facilitating the reliable operation of lithium-ion batteries, accurate prediction for battery remaining useful life (RUL) are both critical. In this paper, a 1D CNN-BiLSTM method is proposed to extract the RUL prediction of lithium-ion battery of Electric Vehicles (EVs). By using one dimensional convolutional neural network (1D CNN) and bidirectional long short-term memory (BiLSTM) neural network simultaneously, selecting the ELU activation function to apply to the convolutional layer, a hybrid neural network is proposed to improve the accuracy and stability of lithium-ion battery RUL prediction. The 1D CNN is used to fully mine the deep features of lithium-ion SOH data, while the BiLSTM is adopted to study the deep features in two directions, and the RUL prediction of lithium-ion battery is output through dense layer. To verify the effectiveness of the proposed method, the battery data of the National Aeronautics and Space Administration (NASA) are utilized to make some comparisons among the RNN model, LSTM model, BiLSTM model and hybrid neural network model. The results show that the hybrid one has higher generalization ability and prediction accuracy than the others.