Efficient Analysis of Interdependencies in Electrode Manufacturing Through Joint Application of Design of Experiments and Explainable Machine Learning

Battery cell production is a key contributor to achieving a net-zero future. A comprehensive understanding of the various process steps and their interdependencies is essential for speeding up the commercialization of newly developed materials and optimizing production processes. While several approaches have been employed to analyze and understand the complexity of the process chain and its interdependencies - ranging from expert- and simulation-based to data-driven - the latter holds significant potential for real-time application. This is particularly relevant for inline process control and optimization. To streamline the development and implementation of data-driven models, a holistic framework that encompasses all necessary steps - from identification of relevant parameters and generation of data to development of models - is imperative. This article aims to address this objective by presenting a comprehensive and systematic methodology, demonstrated for efficient cross-process analysis in electrode manufacturing. Through the combined utilization of design of experiments methods, data-driven models, and explainable machine learning methods, the interdependencies between production parameters and the physical, mechanical, and electrochemical characteristics of the electrodes were uncovered. These actionable insights are essential for enabling informed decision-making, facilitating the selection of appropriate process parameters, and ultimately optimizing the production process. Explainable machine learning (XML) methods were used in combination with the Design of Experiments (DoE) method to generate a dataset and conduct a comprehensive analysis of the interdependencies between manufacturing parameters, and physical, mechanical, and electrochemical properties of lithium-ion electrode batteries.image

Automated summary

Battery cell production plays a crucial role in achieving a net-zero future. Understanding the interdependencies of various process steps is vital for accelerating commercialization and optimizing production processes. This article presents a comprehensive methodology that combines design of experiments, data-driven models, and explainable machine learning methods to uncover the relationships between production parameters and the characteristics of lithium-ion electrode batteries.