Abnormal Battery On-line Detection Method Based on Dynamic Time Warping and Improved Variational Auto-Encoder

被引：0

作者：

Guo T. ^{[1
]}

He J. ^{[1
]}

Shen S. ^{[1
]}

Wang X. ^{[1
]}

Zhang B. ^{[1
]}

机构：

[1] School of Automation, Central South University, Changsha

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 02期

关键词：

Anomaly detection; Bayesian optimization; Dynamic Time Warping(DTW); Lithium battery; Long Short-Term Memory(LSTM); Variational Auto-Encoder(VAE);

D O I：

10.11999/JEIT230084

中图分类号：

学科分类号：

摘要：

In the process of battery production, the traditional detection accuracy of abnormal batteries is poor, and the offline anomaly detection method after production is inefficient. To solve these problems, a lithium battery anomaly online detection method integrating Long Short-Term Memory Variational AutoEncoder and Dynamic Time Warping evaluation (VAE-LSTM-DTW) is proposed, which realizes the online detection of abnormal battery conditions and prevents the time and energy wastage caused by offlize anomaly detection. Firstly, the Long Short-Term Memory (LSTM) is introduced into the Variational Auto-Encoder (VAE) model to train the battery time series reconstruction model. Secondly, in battery anomaly detection, the Dynamic Time Warping value (DTW) is introduced into the evaluation index, and the optimal detection threshold is obtained based on Bayesian optimization, and the dynamic warping value of each single battery reconstruction data is abnormally identified. The experimental results indicate that, compared with the traditional anomaly detection methods in this field, the VAE-LSTM-DTW model has superior performance, the accuracy rate and F1-score have been greatly improved, and it has high effectiveness and practicability. © 2024 Science Press. All rights reserved.

引用

页码：738 / 747

页数：9

共 20 条

[1] XIAO Jianfu, SUN Rui, MIN Jie, Et al., Fault detection of lithium-ion power battery system[J], Chinese Journal of Power Sources, 45, 6, pp. 736-739, (2021)
[2] MA Suliang, WU Yiwen, LI Jianlin, Et al., Anomaly detection for battery data based on genetic algorithm under cluster analysis framework[J], Power System Technology, 47, 2, pp. 859-867, (2023)
[3] JIN Ruochen, WEI Bo, LUO Yongmei, Et al., Blockchainbased data collection with efficient anomaly detection for estimating battery state-of-health[J], IEEE Sensors Journal, 21, 12, pp. 13455-13465, (2021)
[4] SAXENA S, KANG M, XING Y J, Et al., Anomaly detection during lithium-ion battery qualification testing[C], 2018 IEEE International Conference on Prognostics and Health Management, pp. 1-6, (2018)
[5] DONG Shuqin, ZHANG Bin, Network traffic anomaly detection method based on deep features learning[J], Journal of Electronics & Information Technology, 42, 3, pp. 695-703, (2020)
[6] KINGMA D P, WELLING M., Auto-encoding variational Bayes, The 2nd International Conference on Learning Representations, (2014)
[7] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, Et al., Generative adversarial networks[J], Communications of the ACM, 63, 11, pp. 139-144, (2020)
[8] AN J, CHO S., Variational autoencoder based anomaly detection using reconstruction probability[J], Special Lecture on IE, 2, 1, pp. 1-18, (2015)
[9] QIN Wanting, LAO Songyang, TANG Jun, Et al., Hurricane trajectory outlier detection method based on variational auto-encode[J], Journal of System Simulation, 33, 9, pp. 2191-2201, (2021)
[10] CHANG Jiliang, XIE Lei, ZHAO Jianwei, Et al., An anomaly detection algorithm for ship trajectory data based on VAE-LSTM model[J], Journal of Transport Information and Safety, 38, 6, pp. 1-8, (2020)

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