Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification
This work addresses the need for a systematic solution to reduce model dimension and conduct sensitivity analysis in battery manufacturing, though it is incremental as it applies an existing method (random forest) to a new domain-specific dataset.
The paper tackles the problem of analyzing feature interdependencies in lithium-ion battery manufacturing by proposing a random forest-based classification framework, which achieves reliable classification of electrode properties and effectively quantifies feature importance and correlations using three quantitative indicators.
Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag (OOB) predictions, Gini changes as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analysed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production feature importance and correlations by three various quantitative indicators including the unbiased feature importance (FI), gain improvement FI and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.