Transfer Learning Framework and Use Cases for Battery Manufacturing Systems

The integration of machine learning (ML) into battery manufacturing systems has led to substantial improvements in various areas such as battery performance, quality control, and predictive maintenance. However, in industrial settings, it is often not feasible to acquire a large amount of data required for conventional ML approaches. Adaptation of new process technology, reconfiguration of cell design and up-scaling of processes are the main reasons for the value decrease of the previous data. Therefore, a swift adaptation to production dynamics gains more attention in the field of battery manufacturing. To tackle this problem, this paper introduces transfer learning in battery manufacturing systems to investigate the relationship between production parameters faster and more accurately with less data requirement. A novel framework termed the "Transfer Learning Cube" is demonstrated to explore the feasibility and efficacy of transfer learning for multiple use cases across three key dimensions: production scale, manufacturing process, and battery cell design. Specifically, two industrial case studies were investigated to test the “Transfer Learning Cube” by analyzing their concepts, obstacles, needs, and solutions. This framework underlines the significant potential of transfer learning in battery manufacturing systems. Despite limited data availability, transfer learning enables the rapid setup of battery production lines equipped with new processing methods and enhances the efficiency of scaling up production lines.