From Machinery to Insights: A Comprehensive Data Acquisition Approach for Battery Cell Production

To ensure the widespread use of sustainably produced battery cells, further progress in research is needed. The transition to automated data acquisition is complicated by the technical complexity of industrial data acquisition. Existing software solutions also fall short in meeting usability, reproducibility, extensibility, and cost-effectiveness requirements for research-scale battery production lines. To address these gaps, this paper presents and evaluates a comprehensive data acquisition and collection solution for research-scale battery production lines. It offers a systematic overview of the industrial data acquisition process, focusing on gathering data from various existing machinery and utilizing the industry standard OPC UA protocol. Given the lack of existing solutions that meet the specified requirements, the paper introduces the "ProductionPilot" software as a solution. "ProductionPilot" is designed to provide an extensible platform with a user-friendly web interface. It enables users to select, structure, monitor, and export live production data delivered via OPC UA. The effectiveness of the proposed system is validated at the CELLFAB battery production research facility at eLab of RWTH Aachen university, demonstrating its capability for long-term data acquisition and the generation of digital shadows. By addressing the limitations of current data collection methods and providing a comprehensive solution, this research aims to facilitate the broader adoption of lithium-ion batteries in renewable energy applications

Digital Twin in the Battery Production Context for the Realization of Industry 4.0 Applications

Due to the worsening climate change drastic changes in the transportation sector are necessary. Crucial factors for sustainable energy supply are reliable and economical energy storage systems. Associated with that is the development of gigafactories with a capacity of up to 1000 GWh in 2030 in Europe (currently 25 GWh) for the production of battery cells especially for the automotive sector, which is one of the largest emitters of greenhouse gases in Europe. In addition to the required investments, high scrap rates due to unknown interdependencies within the process chain represent a central challenge within battery cell production. Another key challenge in series production is the product tracking along the value chain, which consists of continuous, batch and discrete processes. Because of it complexity the battery cell production industry is predestined for Industry 4.0 applications in order to meet the current challenges and to make battery cell production more efficient and sustainable. Digital twins and the use of AI algorithms enable the identification of previously unknown cause-effect relationships and thus a product improvement and increased efficiency. In this paper, the digital twin of a battery cell production will be developed. For this purpose, general requirements for the field of battery cell production are first determined and relevant parameters from the literature as well as from a production pilot line are defined. Based on the requirements and the selected parameters a corresponding structure for the digital twin in battery cell production is built and explained in this contribution. This provides the basis for measures to optimize production, such as predictive quality

Synthesis of Artificial Coating Images and Parameter Data Sets in Electrode Manufacturing

Driven by continuous cost pressure and increasing market requirements, the optimisation of the lithium-ion battery production is focus of attention. In order to save time and costs, machine learning (ML) represent a promising tool. ML methods are able to analyse highly complex correlations and abstract data sets. But a considerable amount of training data is needed. Since data is not always available to the required extent, approaches for synthesising artificial data were investigated. In this study, the quality and corresponding measurement parameters in electrode production were assessed and selected. Based on this selection, coating trials have been conducted and the corresponding data set collected. The data set forms the basis for synthesis of artificial coating images and parameters. The selection and design of the synthesis models was divided into two sub-steps. First, the synthesis of artificial coating images was investigated. This was followed by the consideration of a procedure for the synthesis of structured data sets. A promising method for data synthesis of (coating) images are Generative Adversarial Networks (GAN). The basic idea of GANs is to oppose two models: a discriminator and a generator. The generator generates artificial data samples that match the input of the training dataset. Afterwards those data samples (both input and artificial data) are introduced to the discriminator. The discriminator's function is to identify whether the data presented originates from the training dataset or whether it is a counterfeit (artificial data) of the generator. The requirements for the synthesis of tabular data sets correspond in principle to those for a multivariate regression analysis. The combination of the models resulted in a method that allows the prediction of the corresponding measured quality values for arbitrarily selected process parameters, as well as the visualisation of the associated coating result in the form of an artificial image

Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

The increasing global demand for high-quality and low-cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long-term reduction of reject rates, shortening of the production ramp-up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You-Only-Look-Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig

Experiencing the Real Presence of Christ in the Eucharist

We present a new understanding of Christ’s real presence in the Eucharist on the model of Stump’s account of God’s omnipresence and Green and Quan’s account of experiencing God in Scripture. On this understanding, Christ is derivatively, rather than fundamentally, located in the consecrated bread and wine, such that Christ is present to the believer through the consecrated bread and wine, thereby making available to the believer a second-person experience of Christ, where the consecrated bread and wine are the way in which she shares attention with him. The consecrated bread and wine are then, in a sense, icons of Christ

Verification of the Potential Yield of the Shallow Dolomite Aquifer in DuPage County, Illinois

published or submitted for publicationis peer reviewedOpe

Accuracy of admissible heuristic functions in selected planning domains

The efficiency of optimal planning algorithms based on heuristic search crucially depends on the accuracy of the heuristic function used to guide the search. Often, we are interested in domain-independent heuristics for planning. In assessing the limitations of domain-independent heuristic planning, it appears interesting to analyse the (in)accuracy of common domain-independent planning heuristics in the IPC benchmark domains. For a selection of these domains, we analytically investigate the accuracy of the h + heuristic, the h k family of heuristics, and certain (additive) pattern database heuristics, compared to the optimal heuristic h ∗. Whereas h + and additive pattern database heuristics usually return cost estimates proportional to the true cost, non-additive h k and non-additive pattern-database heuristics can yield results underestimating the true cost by arbitrarily large factors

Concept for Digital Product Twins in Battery Cell Production

This paper presents an approach for the design and derivation for establishing a digital product twin for battery cells. A digital product twin is a virtual replica of a physical battery cell and can be used to predict and optimize quality properties and performance in real-time. The study focuses on pouch cell manufacturing and aims to map the large amount and variety of process information down to purchased parts and interim products. The approach for this study was to collect and analyze data from the physical production process and use this information to structure a digital battery product twin based on its product architecture. The main findings of this study indicate that a digital product twin can be effectively structured and implemented in a digital interface based on its product architecture in combination with data from the physical production process. The results of this study show the potential of digital product twins, in which statements about material, design, and behavior can be made using real information from production. Further research will focus on the practical application and implementation of digital product twins in a battery cell pilot production