A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery

Author:

Gao Dexin1,Liu Xin1ORCID,Zhu Zhenyu1,Yang Qing2

Affiliation:

1. College of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, China

2. College of Information Science and Technology, Qingdao University of Science & Technology, Qingdao, China

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.

Funder

Key Research and Development Projects of Shandong Province

Shandong Graduate Education Quality Improvement Plan Project

Project of Shandong Province Higher Educational Science and Technology Program

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Applied Mathematics,Control and Optimization,Instrumentation

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