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

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.

Study on Lifetime Decline Prediction of Lithium-Ion Capacitors

“…The suggested approach not only captures the capacity renewal phenomenon well, but also has good forecast accuracy and is less affected by various prediction starting points. Furthermore, to fully exploit the time-series properties of the lithium-ion battery data, Gao et al [40] combined a convolutional neural network (CNN) and a bidirectional long and short-term memory (BiLSTM) neural network to extract deep features of battery capacity data, and they chose the memory function of the BiLSTM neural network to retain key information in the data and predict the trend of variations in the RUL of batteries. It is crucial to remember that the aging battery data drastically restricts the precision and stability of the data-driven algorithm.…”

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the 1D CNN-BLSTM Neural Network

“…Accurately predicting the RUL is crucial for ensuring the reliable operation of batteries throughout their entire energy storage lifespan. The capacity is broadly regarded as a sign of health, which is used to evaluate the remaining cycle life of a battery [6,7]. Lithium-ion batteries are dynamic and ever-changing electrochemical systems with nonlinear characteristics and complex internal mechanisms, which exposes great challenges for predicting maximum remaining capacity and minimizing the declining trend.…”

Preventive Maintenance Method of Brake Pads According to CNN-LSTM Model

“…However, There are few studies on preventive maintenance of brake disc damage monitoring by CNN-LSTM hybrid algorithm. Gao Dexin et al [7] used CNN-LSTM combination to monitor the performance of automotive lithium-ion batteries, and the research showed that the efficiency of CNN-LSTM algorithm were superior to other algorithms, indicating that the method was also suitable for preventive maintenance of brake pads.…”