The process step of drying represents one of the most energyintensive steps in the production of lithium-ion batteries (LIBs). [1,2] According to Liu et al., the energy consumption from coating and drying, including solvent recovery, amounts to 46.84% of the total lithium-ion battery production. [3] The starting point for drying battery electrodes on an industrial scale is a wet film of particulate solvent dispersions, which are applied to a current collector foil by slot-die coating. Conventional convective drying removes the solvent from the wet film and solidifies the layer as the drying time progresses (Figure 1). According to the state-of-the-art, electrodes are produced at a web speed of 25-80 m min À1 . [2,[4][5][6][7][8] The deviations in the coating/drying speeds are related to the constant improvement of the system technologies and the different coating thicknesses and types. For example, coatings can be applied intermittently or continuously. [9][10][11] Furthermore, double-sided coatings can be applied simultaneously or sequentially, meaning that the first side is dried first and then the second side. [4,6,7,12] In addition to the actual application methods, parameters such as mass loading and solids content determine the achievable drying rate. Pfleging et al. report typical coating speeds for thick electrodes of 30 m min À1 , whereas Mauler et al. assume that processing speeds of up to 100 m min À1 are realistic in the future. [6,7] However, the influence of the drying intensity on the structure of the electrode must be taken into account. Increasing the drying rate leads to a segregation of binder and carbon black particles contained in the formulation. [13,14] This results in a decrease in binder and carbon black at the interface between the current collector foil and the coating. Jaiser et al. divide the drying process into two different phases. During the first phase, the evaporation of the solvent leads to a shrinkage of the coated layer, the second starts once this shrinkage stops and the solvent is evaporated by capillary transport. The latter phase is mainly responsible for the binder migration. [15] For this reason, drying must be very gentle during this phase. Reducing the drying rate of anode slurries during this stage from 1.19 to 0.52 g m 2 s À1 leads to a 50% increase in adhesive strength. [15] Clearly, an elevation of the drying intensity and the associated production speed is not possible compromising electrode quality. [13,14] Regardless of the electrode parameters, however, throughput can be increased by lengthening the dryer. The difficulty here lies in handling very thin foils, which tend to wrinkle with an increase in dryer length and place new demands on the web tension controls. [4] To be able to increase production speeds and, thus, maximize throughputs, a carefully chosen
The manufacturing of lithium‐ion battery (LIB) cells has been identified as a hotspot addressing growing price competition and the environmental and economic pressures on technologies along the value creation chain. Effective quality management strategies (QMS) are essential to meet these rising challenges. Herein, data‐driven technologies have fostered an improved understanding of existing process–structure–properties and their process chain covering interdependencies. However, there are often limited in their capability due to the lack of availability or in‐depth information about the manufactured product, existing incidences (i.e., product faults), and the possibility to deploy inline QMS due to insufficient knowledge about the history and condition of the identified products. Against this background, this work describes the implementation of a traceability system as part of QMS for battery cell production and presents a developed framework to overcome challenges from an LIB production perspective for traditional traceability approaches. In addition, this article discusses the implementation of technologies, focusing on the identification and traceability on electrode‐sheet level through continuous and feature‐based approaches. With those technologies implemented, the deployment of quality gates within the electrode production is now possible to not only improve quality inline, but also discover new possibilities for future electrode balancing to reduce manufacturing costs and the overall environmental impact.
The rising demand for lithium-ion batteries (LIBs) significantly changed the field of research in the past years. [1] Due to the exponentially increasing market volume and high raw material costs, resource efficient processing is more important than ever since almost 70% of the battery costs are due to the raw material used. [2] Resource efficient processing requires continuous product quality monitoring along the process chain to identify scrap material as soon as possible and enable an intelligent, data-driven process control to adjust the following process steps. [3,4] So far, few studies have been known that focus on the intermediate product (IMP) analysis in electrode production. [5][6][7] Along the process chain, the material passes three different IMP steps: powder, slurry, and electrode. Each IP has its own distinct characteristics and different methods to determine the IP quality. For example, the slurry is often characterized by its particle size and viscosity, while electrodes usually are characterized regarding conductivity, mechanical properties, or porosity. However, only a few publications specifically investigate those methods in depth. Even though many publications use the measured values to investigate processes or materials, none of them concludes quality assessment parameters. The main challenges with postulating these so-called "quality gates," which define the IP quality, are the huge variety of parameters, the high complexity of the subsequent process steps, and so-called process-structure-property relations. These relations are even more complicated to identify and to predict since every IMP has its own characteristics that cannot be carried onto later IMPs. The information regarding slurry rheology may influence edge formation or coating quality, yet these characteristics are measured differently and cannot be compared easily.Due to the high complexity, Kornas et al. [8] proposed a multivariate key performance indicator (KPI)-based method for quality control of the process chain. This system was developed considering the entire cell production process and introduces a KPI system with a hierarchical structure to take into account the interactions between process and product characteristics.However, the idea behind intelligent processing is a realization of autonomous control systems. These control systems require specific target values in product and process properties (quality gates). Based on these quality gates, simulations can compare input parameters and adjust the process if needed. Furthermore, short measurement times are necessary for the implementation of closed-loop controls and rapid intervention in the event of any deviations from the quality gates. Therefore, inline measurements offer an enormous advantage over standard offline quality measurements in the laboratory.
In order to reduce the cost of lithium-ion batteries, production scrap has to be minimized. The reliable detection of electrode defects allows for a quality control and fast operator reaction in ideal closed control loops and a well-founded decision regarding whether a piece of electrode is scrap. A widely used inline system for defect detection is an optical detection system based on line scan cameras and specialized lighting. The cameras scan the electrode, and brightness differences on the surface are detected and processed inline. The characteristics of the defect image are used for automated classification of the defects based on image features. Furthermore, the detailed detection of defects allows for the identification of causes. This paper describes the working principle of such an inline detection system, the catalog of typical defects, and the image features used to classify them automatically. Furthermore, we propose and discuss causes and effects of the different defect types on the basis of the literature and expert experience. In combination with tracking and tracing, this enables the manufacturer to reduce scrap by detecting defects early in the production chain.
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