Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.

Automated summary

Using digital twin models in real-time information exchange and parameter correction can optimize the characteristics of the pilot line in lithium-ion battery manufacturing. This article discusses the parameters involved in each step of LIB manufacturing, presents available computational modeling approaches, and explores software implementation details. The importance of these parameters in terms of modeling set-up and validation, measurement accuracy, and rapidity is analyzed. Furthermore, the challenges and opportunities of integrating digital twin pilot lines are presented. The challenges of managing the data produced by these models are also discussed.