Electrode architectures significantly influence the electrochemical performance, flexibility, and applications of lithium‐ion batteries (LiBs). However, the conventional bar coating for fabricating electrodes limits the addition of customized architecture or patterns. In this study, as a novel approach, patterns are integrated into electrodes through large‐scale roll‐to‐roll (R2R) flexographic printing. Additionally, flexible, recyclable, and biodegradable paper are innovatively used as a printing substrate during printing LiBs manufacturing, which exhibited superior printability. Moreover, the paper is modified with a thin‐layer Al2O3 to function as the separator in the printed LiB. The Al2O3‐coated paper enables an admirable wettability for printing, excellent mechanical properties for high‐speed R2R manufacturing, and outstanding thermal stability for the safe and stable operation of LiBs. The assembled paper cells exhibit nearly 100% discharge capacity retention after 1000 cycles at 3 C and outstanding rate performance. This work inspires future large‐scale microbatteries manufacturing integrated with high‐resolution architecture designs.

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Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states.

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