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A data-driven decision-making optimization approach for inconsistent lithium-ion cell screening
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DOI：10.1007/s10845-019-01480-1.png)
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Abstract
En 中文
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.
Keywords:
Multi-source data fusion
Imbalanced learning
Convolutional auto-encoder
Generative adversarial networks
Inconsistent lithium-ion cell screening
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