Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies,Batteries & Supercaps

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Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies
Batteries & Supercaps ( IF 5.1 ) Pub Date : 2019-11-22 , DOI: 10.1002/batt.201900135
Ricardo Pinto Cunha _{1,

2} , Teo Lombardo _{1,

2} , Emiliano N. Primo _{1,

2} , Alejandro A. Franco _{1,

2,

3,

4}

Affiliation

The number of parameters involved in lithium‐ion battery electrode manufacturing and the complexity of the physicochemical interactions throughout the associated processes make highly complex to find interdependencies between the final electrode characteristics and the fabrication parameters. In this work, we have analyzed three different machine‐learning algorithms (decision tree, support vector machine, and deep neural network) in order to find the best one to uncover the interdependencies between the slurry manufacturing parameters and the final properties of NMC‐based cathodes. The results revealed that the support vector machine method shows high accuracy and the possibility to predict the influence of manufacturing parameters on themass loading and porosity of the electrodes in a straightforward graphical way. Furthermore, we report for the first time this new approach and a case study that, by comparing the trends observed experimentally and from the model, demonstrates the validity and the quality of the proposed approach.

中文翻译：

NMC阴极制造参数相互依赖性的人工智能研究

锂离子电池电极制造中涉及的参数数量以及整个相关过程中物理化学相互作用的复杂性，使得寻找最终电极特性与制造参数之间的相互依赖性变得非常复杂。在这项工作中，我们分析了三种不同的机器学习算法（决策树，支持向量机和深度神经网络），以便找到最好的算法来揭示浆料制造参数与基于NMC的最终特性之间的相互依赖性。阴极。结果表明，支持向量机方法显示出很高的准确性，并且有可能以直观的图形方式预测制造参数对电极的质量负载和孔隙率的影响。此外，

更新日期：2019-11-22

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