CC BY 4.0Xu, XukuanXukuanXuStier, SimonSimonStierGronbach, AndreasAndreasGronbachMoeckel, MichaelMichaelMoeckel2025-10-022025-10-022025https://publica.fraunhofer.de/handle/publica/496627https://doi.org/10.24406/publica-559010.3390/batteries1108028510.24406/publica-5590Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing.enlithium-ion batterymachine learningquality managementvirtual quality gatesNew product developmentjournal article