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

作     者：Liu, Chengbao Tan, Jie Wang, Xuelei 

作者机构：Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China 

出 版 物：《JOURNAL OF INTELLIGENT MANUFACTURING》 (智能化制造业杂志)

年 卷 期：2020年第31卷第4期

页      面：833-845页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Intelligent Manufacturing Comprehensive Standardization National Natural Science Foundation of China, NSFC, (U1701262, U1801263) Ministry of Industry and Information Technology of the People's Republic of China, MIIT 

主　　题：Multi-source data fusion Imbalanced learning Convolutional auto-encoder Generative adversarial networks Inconsistent lithium-ion cell screening 

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.