PREDICTION OF REMAINING USEFUL LIFE OF LITHIUM BATTERIES BASED ON CS-DBN

被引：0
|
作者：
Liang J. [1 ,2 ]
He X. [1 ,2 ]
Xiao H. [1 ,2 ]
机构：
[1] Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan
[2] College of Science, Wuhan University of Science and Technology, Wuhan
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 03期
关键词：
cuckoo algorithm; deep belief network; health indicator; lithium-ion batteries; random forest; remaining useful life;
D O I：
10.19912/j.0254-0096.tynxb.2022-1923
中图分类号：
学科分类号：
摘要：
In order to predict the remaining service life of lithium batteries more accurately，a prediction model based on cuckoo algorithm（CS）and deep belief network（DBN）is proposed in this paper. Firstly，16 health indicators（HI）that affect the RUL of lithium batteries are introduced，and nine HIs that are more important for the RUL through random forest（RF）are selected. Then the CS is used to optimize the parameters of the hidden layer in the deep belief network model，and the optimal deep belief network prediction model is established through optimization. Finally，the battery data collected by the University of Maryland（CALCE）is used for the experiment. The results show that the goodness of fit of the CS-DBN model proposed in this paper is up to 98%，and compared with the prediction results of other models，it has smaller error，which verifies the effectiveness of the proposed method. © 2024 Science Press. All rights reserved.
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页码：251 / 259
页数：8
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  • [21] LI X Y., Recognition of one- dimensional range profile of radar target based on deep learning feature selection［D］, (2020)
  • [22] MOHAMED A R, DAHL G E，, HINTON G., Deep belief networks for phone recognition［J］, Proc NIPS, 1, 9, pp. 3-9, (2009)
  • [23] HOU Q L, CAO L, SHAN T M，, Et al., Remaining useful life prediction of aviation lithium battery based on indirect health index and echo state network［J］, Measurement & control technology, 41, 7, pp. 57-63, (2022)
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