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.
引用
收藏
页码：251 / 259
页数：8
相关论文
共 23 条
  • [11] YANG Z S, WANG Y H, KONG C Z., Prediction of remaining useful life of lithium- ion batteries based on GWO-SVR method［J］, Journal of power supply, 21, 2, pp. 154-162, (2023)
  • [12] YANG Y R, SHI Y H，, Et al., Remaining useful life prediction for lithium-ion battery based on CEEMDAN and SVR［J］, Journal of electronic measurement and instrumentation, 34, 12, pp. 197-205, (2020)
  • [13] ZHAO Q F, CAI Y P, WANG X J., Remaining useful life prediction of lithium battery under different discharge intervals［J］, China measurement & test, 49, 3, pp. 159-165, (2023)
  • [14] WANG S L, JIN S Y，, BAI D K，, Et al., A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries［J］, Energy reports, 7, pp. 5562-5574, (2021)
  • [15] PANG X Q, WANG Z Q, ZENG J C，, Et al., Prediction for the remaining useful life of lithium- ion battery based on PCA- NARX［J］, Transactions of Beijing Institute of Technology, 39, 4, pp. 406-412, (2019)
  • [16] YE L F, SHI Y B, HUANG Y Y., Lithium battery life prediction based on BiGRU network［J］, Chinese journal of power sources, 45, 5, pp. 598-601, (2021)
  • [17] LIANG H F, YUAN P, GAO Y J., Remaining useful life prediction of lithium-ion battery based on CNN-Bi-LSTM network［J］, Electric power automation equipment, 41, 10, pp. 213-219, (2021)
  • [18] HE B C, YANG X M, WANG J S，, Et al., Prediction of remaining useful life of lithium-ion batteries based on PCA-GPR［J］, Acta energiae solaris sinica, 43, 5, pp. 484-491, (2022)
  • [19] HINTON G E, TEH Y W., A fast learning algorithm for deep belief nets［J］, Neural computation, 18, 7, pp. 1527-1554, (2006)
  • [20] LU S L., Research on damage identification of existing steel truss bridge based on deep learning［D］
123