This deliverable presents a comprehensive methodology for the identification and quantification of uncertainties in modeling and simulation of battery manufacturing processes. The concept is generic and can be applied to practically any simulation technique. The objective is to improve the reliability, reproducibility, and transparency of modeling and simulation of battery manufacturing processes. It was tested and validated using two specific simulation techniques, namely molecular simulation using classical force fields and equations of state (EOS). The report covers a description of the developed uncertainty quantification methodology – first giving an overview; then, giving details and demonstrating the application to exemplary modeling techniques. The two examples differ significantly demonstrating the robustness of the uncertainty quantification methodology. The increasing complexity of digital manufacturing processes, combined with the integration of models, simulations, and experimental data, necessitates a rigorous approach to uncertainty quantification (UQ) and reliability assessment. Uncertainty Quantification (UQ) is a cornerstone of the BatCAT project's strategy to develop reliable, transparent, and actionable digital twins for battery cell manufacturing. D3.1 addresses this challenge by developing a formal methodology for the representation, propagation, and management of uncertainty across the manufacturing data space and supporting provenance tracking. Accordingly, the objectives of deliverable D3.1 are to: Develop a formal model and methodology for representing uncertainty and reliability. Establish mechanisms for uncertainty propagation. Support evidence aggregation from heterogeneous sources. Enable documentation of uncertainties in models, simulations, and experimental data.