A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening

doi:10.1007/s10845-019-01480-1

CASIA OpenIR > 中科院工业视觉智能装备工程实验室 > 工业智能技术与系统

	A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening
	Liu, Chengbao1,2 ; Tan, Jie1 ; Wang, Xuelei1
发表期刊	JOURNAL OF INTELLIGENT MANUFACTURING
ISSN	0956-5515
	2020-04-01
卷号	31 期号:4 页码:833-845
通讯作者	Tan, Jie(tan.jie@tom.com)
摘要	Because the data generated in the complex industrial manufacturing processes is multi-sourced and heterogeneous, it brings a challenge for addressing decision-making optimization problems embedded in the whole manufacturing processes. Especially, for inconsistent lithium-ion cell screening as such a special problem, it is a tough issue to fuse data from multiple sources in a lithium-ion cell manufacturing process to screen cells for relieving the inconsistency among cells in a battery pack with multiple cells configured in series, parallel, and series-parallel. This paper proposes a data-driven decision-making optimization approach (DDDMO) for inconsistent lithium-ion cell screening, which takes into account three dynamic characteristic curves of cells, thus ensuring that the screened cells have consistent electrochemical characteristics. The DDDMO method uses the convolutional auto-encoder to extract features from different characteristics curves of lithium-ion cells through multi-channels and then the features in different channels are combined into fusion features to build a feature base. It also proposes an effective sample generation approach for imbalanced learning using the conditional generative adversarial networks to enhance the feature base, thereby efficiently training a classifier for inconsistent lithium-ion cell screening. Finally, industrial applications verify the effectiveness of the proposed approach. The results show that the missing rate of inconsistent lithium-ion cells drops by an average of 93.74% compared to the screening performance in the single dynamic characteristic of cells, and the DDDMO approach has greater accuracy for screening cells at lower time costs than the existing methods.
关键词	Multi-source data fusion Imbalanced learning Convolutional auto-encoder Generative adversarial networks Inconsistent lithium-ion cell screening
DOI	10.1007/s10845-019-01480-1
关键词[WOS]	BATTERY PACK ; SMOTE
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Manufacturing
WOS记录号	WOS:000523031700003
出版者	SPRINGER
七大方向——子方向分类	人工智能+制造
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38786
专题	中科院工业视觉智能装备工程实验室_工业智能技术与系统
通讯作者	Tan, Jie
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu, Chengbao,Tan, Jie,Wang, Xuelei. A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening[J]. JOURNAL OF INTELLIGENT MANUFACTURING,2020,31(4):833-845.
APA	Liu, Chengbao,Tan, Jie,&Wang, Xuelei.(2020).A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening.JOURNAL OF INTELLIGENT MANUFACTURING,31(4),833-845.
MLA	Liu, Chengbao,et al."A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening".JOURNAL OF INTELLIGENT MANUFACTURING 31.4(2020):833-845.