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Volume 9
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Article

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

by
Hekun Zhang
1,
Xiangdong Kong
2,*,
Yuebo Yuan
2,
Jianfeng Hua
1,
Xuebing Han
2,
Languang Lu
2,
Yihui Li
3,4,
Xiaoyi Zhou
3,4 and
Minggao Ouyang
2
1
Sichuan New Energy Vehicle Innovation Center Co., Ltd., Yibin 644000, China
2
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
3
SVOLT Energy Technology Co., Ltd., Changzhou 213200, China
4
Dr. Octopus Intelligent Technology (Shanghai) Co., Ltd., Shanghai 201800, China
*
Author to whom correspondence should be addressed.
Batteries 2023, 9(7), 346; https://doi.org/10.3390/batteries9070346
Submission received: 18 May 2023 / Revised: 17 June 2023 / Accepted: 21 June 2023 / Published: 27 June 2023
(This article belongs to the Special Issue Battery Energy Storage Management by Integrating Omni-Channel Information: Battery Physics, Machine Learning, Force/Thermal/Electrical/Gas Sensors)
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Abstract

During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.
Keywords: lithium-ion cell; foreign matter defect; K-value test; machine learning; local outlier factor lithium-ion cell; foreign matter defect; K-value test; machine learning; local outlier factor

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MDPI and ACS Style

Zhang, H.; Kong, X.; Yuan, Y.; Hua, J.; Han, X.; Lu, L.; Li, Y.; Zhou, X.; Ouyang, M. A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing. Batteries 2023, 9, 346. https://doi.org/10.3390/batteries9070346

AMA Style

Zhang H, Kong X, Yuan Y, Hua J, Han X, Lu L, Li Y, Zhou X, Ouyang M. A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing. Batteries. 2023; 9(7):346. https://doi.org/10.3390/batteries9070346

Chicago/Turabian Style

Zhang, Hekun, Xiangdong Kong, Yuebo Yuan, Jianfeng Hua, Xuebing Han, Languang Lu, Yihui Li, Xiaoyi Zhou, and Minggao Ouyang. 2023. "A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing" Batteries 9, no. 7: 346. https://doi.org/10.3390/batteries9070346

APA Style

Zhang, H., Kong, X., Yuan, Y., Hua, J., Han, X., Lu, L., Li, Y., Zhou, X., & Ouyang, M. (2023). A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing. Batteries, 9(7), 346. https://doi.org/10.3390/batteries9070346

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