Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (4): 41-50.doi: 10.11985/2022.04.006

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Capacity Prediction of Lithium-ion Batteries Based on Multi-task LSTM with Attention Mechanism

LU Nan1(), OUYANG Quan1(), HUANG Lianghui2(), WANG Zhisheng1()   

  1. 1. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106
    2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023
  • Received:2022-05-15 Revised:2022-06-24 Online:2022-12-25 Published:2023-02-03
  • Contact: OUYANG Quan, E-mail:ouyangquan@nuaa.edu.cn

Abstract:

Accurate capacity prediction of lithium-ion batteries can effectively reduce the risk and loss caused by battery failure. The time series prediction model based on neural network is a very common method in the field of battery capacity prediction. However, most of the predictions of the past models only considered the future target points, but did not consider the auxiliary role of the information near the target points. A capacity prediction method of lithium-ion batteries based on multi-task LSTM with attention mechanism is proposed to realize the complementation of information at different times in the future and improve the prediction accuracy. The hard parameter sharing method is used to establish the connection among multiple tasks, and the convolutional neural network is used to extract features at different levels of abstraction. Comparing with the traditional neural network, and the experimental results show that the proposed MT-LSTM model has higher prediction accuracy. In addition, comparison experiments are designed for the multi-task learning and the attention mechanism to verify the positive effects of these two methods on the prediction accuracy of battery capacity.

Key words: Battery capacity prediction, long short-term memory neural network, attention mechanism, multi-task learning

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