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Electrode Production and Experimental Data
Abstract: As a typical mechatronics system, the battery manufacturing chain becomes a hot research topic because it directly determines electrode quality, further affecting manufac... View more
As a typical mechatronics system, the battery manufacturing chain becomes a hot research topic because it directly determines electrode quality, further affecting manufactured battery performance. Due to the complexity of battery manufacturing, an effective sensitivity analysis solution that could quantify variable importance or correlations and explore impact variables toward resulting the electrode quality is urgently needed. This article scrutinizes the effects of component parameters from the mixing stage on the manufactured results of Li-ion battery electrode via classification modeling. Specifically, an effective RUBoost-based ensemble learning framework is proposed to compensate for class imbalance issue and well classify three key quality indicators including the electronic conductivity, thickness, and half-cell capacity for both LiFePO
_4
- and Li
_4
Ti
_5
O
_{12}
-based electrode. Experimental results reveal that the proposed models could well handle the class imbalance issues and accurately classify/predict the qualities of the manufactured electrode. Moreover, the importance weights of variables and the correlations of variable pairs could be effectively quantified. Due to the superiority in terms of accuracy, interpretability, and data-driven nature, the proposed ensemble learning approach could not only help to conduct reliable multiclassification of manufactured electrode but also benefit smarter battery manufacturing.
Published in: IEEE/ASME Transactions on Mechatronics ( Volume: 27 , Issue: 5 , October 2022 )
Date of Publication: 18 October 2021
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Lithium-ion (Li-ion) battery represents one of the promising energy storage solutions for many applications such as electrical vehicle, owing to its high energy density, reliable service life [1], etc. However, the wider applications of Li-ion batteries are limited by their cost, reliability, safety, energy density, and life. As a typical mechatronics system, the battery production chain plays a vital and direct role in affecting the qualities of intermediate products, which, in turn, further determines final battery performance [2]. Therefore, it is crucial to monitor and analyze battery intermediate manufacturing processes in the pursuit of a smarter manufacturing chain [3], [4].
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
2021 IEEE Sustainable Power and Energy Conference (iSPEC)
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Анал с немолодыми девками
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Худая телка с очень большой грудью мастурбирует в пенной ванной