REMAINING USEFUL LIFE PREDICTION OF LITHIUM-ION BATTERIES BASED ON DIFFERENTIAL VOLTAGE AND ICS-ELMAN NEURAL NETWORKS

被引：0
|
作者：
Li L. [1 ,2 ]
Zhu L. [1 ]
Li S. [1 ]
Liu H. [3 ]
Wang Y. [3 ]
Zhao J. [3 ]
机构：
[1] School of Artificial Intelligence, Hebei University of Technology, Tianjin
[2] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[3] State Grid Jibei Zhangjiakou Wind and Solar Energy Storage and Transportation New Energy Co.，Ltd., Zhangjiakou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 12期
关键词：
curves of differential voltage; Elman neural networks; improved cuckoo search algorithm; lithium- ion batteries; remaining useful life;
D O I：
10.19912/j.0254-0096.tynxb.2022-1357
中图分类号：
学科分类号：
摘要：
Lithium-ion batteries are widely used in equipment supporting new energy grid connection，and their remaining useful life （RUL）prediction is very important for equipment operation and maintenance management. This paper presents a method for predicting the remaining service life of lithium-ion batteries based on differential voltage and improved cuckoo search algorithm（ICS）-Elman neural network. Firstly，the internal electrochemical reaction and external data characteristics of the battery were analyzed，and the differential voltage curve combined with the internal and external characteristics of the battery was selected as the feature extraction object，and the relevant features were selected from the charge differential voltage curve and discharge differential voltage curve. Considering the phenomenon of battery capacity regeneration，a battery capacity prediction model based on Elman neural networks is established. In order to improve the prediction accuracy，the improved cuckoo search algorithm is used to optimize the initial weights and thresholds of the network. The cuckoo search is improved by three methods：improving the probability formula，increasing the diffusion factor and chaos initialization to form the ICS-Elman prediction method. Finally，the ICS-Elman method is validated by using NASA dataset and self-test dataset. The results show that the ICS-Elman method can predict the RUL of lithium-ion battery more accurately and effectively compared with the CS-Elman and Elman models. © 2023 Science Press. All rights reserved.
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页码：433 / 443
页数：10
相关论文
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