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Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies; a feasible solution that can analyze 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 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 analyzed 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 (FI) and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production FI and correlations by three various quantitative indicators, including the unbiased FI, gain improvement FI, and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.

Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification