Data-driven insights into lithium-ion battery manufacturing using the linear model to analyze the manufacturing process and predict cell quality

The rising demand for lithium-ion batteries leads to different challenges in production, necessitating cost reduction, improved quality and sustainability. This paper introduces a data-driven approach using machine learning to analyze the manufacturing process. This concept examines the relationships among process parameters and cell quality, employing machine learning models to discern critical factors. Machine learning enables the identification of optimization potentials, quality forecasting based on process parameters, and early detection of process errors. The paper aims to use the linear model to elevate cell quality and advance understanding of the complex manufacturing process through data.