Main Page

Project Wiki and Semantic Dataspace for the KIproBatt Cell Manufacturing (public) and Project Management (private).

To access the JupyterLab Workflow Environment, visit kiprobatt.de/jupyter (private)

Git Repos exist both on Github and FhG Gitlab (no public content yet).

What can I do here?

• Take a look at our interactive Battery value chain glossary - especially the Cell manufacturing from Separation to EOL_Testing
• Explore our Semantic Process Definition - from the top down to single parameters
• ... and the visualization of the complete Semantic Metadata Structure of a manufactured cell
• Follow the correlations we have classified as High-priority parameter correlations
• Inspect our Key Performance Indicators (KPIs)
• View related ontologies at onto-wiki.eu
• ... even more in the future

About KIproBatt - Intelligent battery cell manufacturing with AI-supported process monitoring based on a generic system architecture

Funding authority:

BMBF InZePro Cluster

Project duration:

10/1/2020 – 9/30/2023

Partners:

• Fraunhofer Institute for Silicate Research ISC
• Fraunhofer Institute for Casting, Composite and Processing Technology IGCV
• University of Bayreuth
• Aschaffenburg University of Applied Sciences (UAS)

Abstract:

Due to its complexity and vast economic as well as ecological impact, the Li-ion battery cell production process is subject to ongoing digitization and optimization in order to increase cell performance while reducing resource consumption and production costs.

In this context, artificial intelligence (AI) holds immense potential in leveraging manufacturing data to improve the cell production process. Hence, we aim to enhance cell production with AI-based end-to-end process monitoring, which covers all steps of the process chain. For this purpose, we develop a generic, software-implemented system architecture as reusable structure that allows us to connect the process data acquisition with a carefully constructed ontology-based semantic data space. Based on this system architecture, we attach machine learning approaches from two perspectives: In the first perspective, we apply both data and physics driven models to specific process parameters to detect and evaluate correlations and process anomalies. In the second perspective, we develop an overarching end-to-end process monitoring. For this application, we integrate the previously developed models into a dashboarding system in order to assess their relevance for cell performance and monitor the cells' state in production constantly and as real-time as possible.

The combination of these two perspectives allows us to detect defects early in the production process, rapidly increase the quality performance and derive flexible adjustments to the process parameters in case of malfunction or defects. Thus, we expect to reduce the total cost of cell production as well as improve its carbon footprint by reductions in resource and energy consumption. Finally, the generic, semantically structured design enables an easy transfer to other research and industrial processes.

About the KIproBatt Dataspace

Technically the KIproBatt plattform consists of the following OpenSource components

• MediaWiki: Most important Wiki framework, basis of Wikipedia
• Semantic MediaWiki: Semantic extension for MediaWiki
• JupyterLab: Cloud-based scripting environment, e. g. for Python

MediaWiki provides the basic document-based data structure. Furthermore, extensive interfaces for human (editors) and machine (API) are provided. Templates and forms ensure uniformity and efficiency. The extension Semantic MediaWiki provides the Wiki with numerous possibilities for linking and annotating data. In addition, semantic queries can be sent directly to the Wiki and results can be visualized in a variety of ways. Via JupyterLab users can access data stored in the Semantic MediaWiki, process it and push the results back to the Wiki.

This project is funded by the German Federal Ministry of Education and Research (BMBF), grant no. 03XP0309A-C.