A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening | |
Liu, Chengbao1,2; Tan, Jie2; Wang, Xuelei2 | |
刊名 | JOURNAL OF INTELLIGENT MANUFACTURING |
2020-04-01 | |
卷号 | 31期号:4页码:833-845 |
关键词 | Multi-source data fusion Imbalanced learning Convolutional auto-encoder Generative adversarial networks Inconsistent lithium-ion cell screening |
ISSN号 | 0956-5515 |
DOI | 10.1007/s10845-019-01480-1 |
通讯作者 | Tan, Jie(tan.jie@tom.com) |
英文摘要 | 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. |
WOS关键词 | BATTERY PACK ; SMOTE |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | SPRINGER |
WOS记录号 | WOS:000523031700003 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/38785] |
专题 | 综合信息系统研究中心_工业智能技术与系统 |
通讯作者 | Tan, Jie |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
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. |
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