Cause-Effect Relationships in Battery Cell Production - Data based validation of expert knowledge in electrode production

Procedia CIRP, vol. 120, pp. 469–474

Abstract

The advancing climate change requires a shift from conventional energy sources to renewable ones, which is accompanied by the indispensability of an efficient and sustainable storage of electrical energy. The production of lithium-ion battery cells comprises an enormous complexity, as it consists of several interlinked process steps, each with different quality requirements for the (intermediate) products and the processes itself. This complexity means that a supposedly insignificant process parameter can lead to errors and deviations in the manufactured products via complex cause-effect relationships, that are not fully known. The missing knowledge of the processes and inter-process interdependencies lead to difficulties in the setup of a suitable quality assurance in battery cell production to reduce scrap rates. Beside these potentials in planning and optimizing the process chain of the battery cell factory of tomorrow are impeded. We developed a method to build up a relevant knowledge base on cause-effect relationships for battery cell production. Expert knowledge based on experience and intuition has been collected and quantitative cause-effect relationships are established. This knowledge is used to set up the initial data acquisition, ensuring relevant data being available for analysis. Afterwards, the data analysis, that validates and extends this expert knowledge, is defined. This approach is applied to real-world use-cases including machinery and laboratory equipment in battery cell production. The electrode production is selected as the basis for validation. The found inter-relationships either validate the qualitative expert knowledge, contradict this prior knowledge or else add new insights about the battery cell electrode production process that have not been considered in the qualitative expert knowledge. Methods used include correlations and a predictive power score for non-linear interdependencies. The cause-effect relationships are constantly updated by automatically analyzing the production data of the digital factory. These identified and validated cause-effect relationships allow the planning of efficient and sustainable battery cell production and data-based process chain optimization.

Authors 10

1. Antje Fitzner corresponding

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

2. Johann-Philip Abramowski Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

3. Arno Schmetz

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

4. Jonathan Krauß

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

5. Kristina Borzutzki

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

6. Miguel Patricio Eckstein

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

7. K. Pouls

   Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

8. Christoph Baum Aachen Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT · Fraunhofer Research Institution for Battery Cell Production

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

   Fraunhofer Research Institution for Battery Cell Production FFB, Bergiusstr. 8, 48165 Münster, Germany

9. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering (WZL)

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstr. 17, Aachen 52074, Germany

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH University, Campus-Boulevard 30, 52074 Aachen, Germany

10. Achim Kampker Aachen Production Engineering of E-Mobility Components

    RWTH Aachen University

    Affiliation as printed

    Production Engineering of E-Mobility Components, PEM of RWTH University, Bohr 12, 52072 Aachen, Germany