Title: A Hierarchical Digital Twin Framework for Predictive Quality and Lifecycle Traceability in High-Volume Lithium-Ion Battery Cell Production Abstract: This paper introduces a structured information model for implementing digital twins across multi-stage lithium-ion battery cell manufacturing processes to enhance predictive quality and enable full lifecycle traceability. Focusing on the transition toward giga-scale production, the proposed framework employs a modular architecture of sub-twins—representing intermediate products such as slurry, electrode coils, and cell stacks—integrated with plant-level digital twins via standardized interfaces. Built on Industry 4.0 principles, the model incorporates geometric, material, and process data to support AI-driven simulations for real-time quality prediction, such as coating thickness uniformity, and facilitates adaptive closed-loop process control. By reducing scrap rates, improving yield, and enabling data continuity from raw materials to end-of-life, this approach advances the sustainability, scalability, and economic viability of battery manufacturing systems critical to electrified transportation and grid-scale energy storage.