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Lithium-Ion Cell Screening W th Convolutional Neural Networks Based on Two-Step Time-Series Clustering and Hybrid Resampling for Imbalanced Data
Liu, Chengbao1,2; Tan, Jie1; Shi, Heyuan3; Wang, Xuelei1
发表期刊IEEE ACCESS
ISSN2169-3536
2018
卷号6页码:59001-59014
通讯作者Tan, Jie(tan.jie@tom.com)
摘要Due to the material variations of lithium-ion cells and fluctuations in their manufacturing precision, differences exist in electrochemical characteristics of cells, which inevitably lead to a reduction in the available capacity and premature failure of a battery pack with multiple cells configured in series, parallel, and series parallel. Screening cells that have similar electrochemical characteristics to overcome the inconsistency among cells in a battery pack is a challenging problem. This paper proposes an approach for lithium-ion cell screening using convolutional neural networks (CNNs) based on two-step time-series clustering (TTSC) and hybrid resampling for imbalanced data, which takes into account the dynamic characteristics of lithium-ion cells, thus ensuring that the screened cells have similar electrochemical characteristics. In this approach, we propose the TTSC to label the raw samples and propose the hybrid resampling method to solve the sample imbalance issue, thereby obtaining labeled and balanced datasets and establishing the CNN model for online cell screening. Finally, industrial applications verify the effectiveness of the proposed approach and the inconsistency rate of the screened cells drops by 91.08%.
关键词Lithium-ion cell screening time-series clustering resampling convolutional neural networks
DOI10.1109/ACCESS.2018.2875514
关键词[WOS]OF-CHARGE ESTIMATION ; BATTERY PACKS ; ELECTRIC VEHICLES ; CLASSIFICATION ; MECHANISM ; DISCHARGE ; SMOTE ; LIFE
收录类别SCI
语种英语
资助项目National Nature Science Foundation of China[U1701262] ; Intelligent Manufacturing New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China[2016ZXFM06005]
项目资助者National Nature Science Foundation of China ; Intelligent Manufacturing New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000449646300001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22582
专题综合信息系统研究中心
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
3.Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714
Liu, Chengbao,Tan, Jie,Shi, Heyuan,et al. Lithium-Ion Cell Screening W th Convolutional Neural Networks Based on Two-Step Time-Series Clustering and Hybrid Resampling for Imbalanced Data[J]. IEEE ACCESS,2018,6:59001-59014.
APA Liu, Chengbao,Tan, Jie,Shi, Heyuan,&Wang, Xuelei.(2018).Lithium-Ion Cell Screening W th Convolutional Neural Networks Based on Two-Step Time-Series Clustering and Hybrid Resampling for Imbalanced Data.IEEE ACCESS,6,59001-59014.
MLA Liu, Chengbao,et al."Lithium-Ion Cell Screening W th Convolutional Neural Networks Based on Two-Step Time-Series Clustering and Hybrid Resampling for Imbalanced Data".IEEE ACCESS 6(2018):59001-59014.
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