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Batteries & Supercaps www.batteries-supercaps.org Review doi.org/10.1002/batt.202200224 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-theart, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization.

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The interplay between the internal mechanical properties and external mechanical conditions of a battery cell, e. g., Young's modulus and thickness change, has a crucial impact on the cell performance and lifetime, and thus, needs to be fully understood. In this work, 12 Ah lithium‐ion battery pouch cells were studied during cycling and aging by non‐invasive operando ultrasonic and dilation measurements. The effective Young's modulus increases and the thickness varies the most within a single cycle during the graphite transition from stage 1L to 4, at the beginning of the 2 to 1 stage transition and at the phase transition of the nickel‐rich NCM from H2 to H3. After 1000 cycles of aging, the overall effective Young's modulus of the lithium‐ion battery decreases by ∼11 %–12 % and the cell thickness increases irreversibly by ∼3 %–4 %, which is mostly related to a thicker and possibly softer, more porous solid electrolyte interphase layer.

With a growing demand for cheap and reliable lithium-ion batteries, manufacturing processes are being optimized continuously to reduce energy consumption and costs. However, one of the main contributors to the manufacturing cost [1,2] ─the wetting of the porous structure within the battery cell-still needs a gain in process knowledge to enable further acceleration and improvement. [3] After filling the battery cell with electrolyte, wetting is the next step in the process chain and involves a rest period under controlled conditions. [4,5] Unfortunately, there is uncertainty about the degree of wetting achieved and thus a safety margin is often used, which adds to the total time for this step.The wetting process is usually performed at elevated temperatures and may, depending on cell geometry and type, even involve additional steps of electrolyte dosing. To ensure a high battery cell quality, reliable performance, and safety, the wetting has to be completed before the subsequent formation step is performed. Otherwise, a nonhomogeneous deposition of the initial solid-electrolyte interface (SEI) is expected, [4,6] causing an accelerated aging of the battery cells. [7][8][9] Therefore, best practice solutions are in place to ensure a sufficiently long wetting time. These solutions are based on experience with the production of battery cells with similar specifications and postmortem studies to visually inspect the wetting degree and the uniformity of the formation process. An in-line testing method to be able to test the wetting degree of each battery cell or to determine the best parameters for sufficient wetting of a battery cell type in preproduction development is highly desirable. [10] Such a method would enable the process time to be shortened to a minimum without the need for a safety margin. Monitoring and Testing of the Wetting ProcessThere is currently no imaging or testing method, applicable on an industrial scale, able to spatially resolve the electrolyte distribution within a battery cell. Electrochemical impedance