Prediction of Remaining Useful Life of Lithium-Ion Battery Based on Adaptive Data Preprocessing and Long Short-Term Memory Network

被引：0
|
作者：
Huang K. [1 ]
Ding H. [1 ]
Guo Y. [2 ]
Tian H. [1 ]
机构：
[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[2] School of Artificial Intelligence, Hebei University of Technology, Tianjin
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 15期
关键词：
adaptive bi-exponential model smooth method; Lithium-ion battery; long short-term memory network; remaining useful life; the complete ensemble empirical mode decomposition with adaptive noise;
D O I：
10.19595/j.cnki.1000-6753.tces.210860
中图分类号：
学科分类号：
摘要：
The remaining useful life (RUL) of lithium-ion battery can evaluate the reliability of battery, which is an important parameter of battery health management. Accurate prediction of RUL of battery can effectively improve the safety of equipment and reduce the working risk. In this paper, a RUL prediction framework combined with the adaptive data preprocessing method and long-term and short-term memory neural network (LSTM) was proposed. Selecting capacity as the health factor, in the data preprocessing stage, the adaptive double exponential model smoothing method was used to reduce the negative effect of capacity recovery and the adaptive white noise integrated empirical mode decomposition (CEEMDAN) is used to suppress the noise. In the model constructing stage, the LSTM model was built for RUL prediction by training the preprocessed data. The NASA and CALCE open source data were selected to verify the performance of the proposed method. The experimental results show that it has good robustness and can provide RUL prediction results with high precision. © 2022 Chinese Machine Press. All rights reserved.
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页码：3753 / 3766
页数：13
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