Interdependencies in Electrode Manufacturing: A Comprehensive Study Based on Design Augmentation and Explainable Machine Learning

Abstract

Electrode manufacturing, as the core of battery cell production, is a complex process chain with a large number of interrelated parameters. An in‐depth understanding of the processes, their relevant parameters, and the resulting effects on intermediate and final product properties can accelerate the transition toward quality‐oriented, efficient battery cell production. Given the complexity of the process chain, data‐driven models have emerged as promising solutions for analyzing the existing interdependencies. The accuracy and effectiveness of these models significantly depend on the quality and comprehensiveness of the underlying data. With a low‐quality dataset, there is an increased risk of drawing inaccurate conclusions or generating misleading results. This article aimed to demonstrate a use case for the evaluation and enhancement of historical datasets to provide a statistically robust foundation for the development of machine learning models. The study was based on pilot‐scale anode manufacturing and covered variations in the coating, drying, and calendering processes. The key intermediate product and process parameters were used to predict two primary target variables: adhesion strength and discharge capacity at different C‑rates. To gain a better understanding of the analyzed interdependencies, explainable machine learning methods were adopted.