Empowering lithium-ion battery manufacturing with big data: Current status, challenges, and future

With the rapid development of new energy vehicles and electrochemical energy storage, the demand for lithium-ion batteries has witnessed a significant surge. The expansion of the battery manufacturing scale necessitates an increased focus on manufacturing quality and efficiency. However, the complexity of the lithium-ion battery manufacturing process, coupled with numerous process parameters, poses challenges for quality management and control. In recent years, the utilization of big data and artificial intelligence methods for optimizing existing manufacturing processes has gained considerable attention. This paper provides a comprehensive summary of the data generated throughout the manufacturing process of lithium-ion batteries, focusing on the electrode manufacturing, cell assembly, and cell finishing stages. A thorough review of research pertaining to performance prediction, process optimization, and defect detection based on these data is presented. Furthermore, the study identifies the existing research limitations and outlines future research directions for harnessing the potential of big data in battery manufacturing. This study provides theoretical and methodological references for further reducing production costs, increasing production capacity, and improving quality in lithium-ion battery manufacturing.