Data Science in Battery Cell Production

June 25-26, 2025, In-person seminar in Münster, Germany

Explore Digital Methods in Battery Cell Manufacturing with Our Specialized Hands-On Training Module

Battery cell production offers enormous potential for optimization in terms of resource efficiency, emission reduction, and process stability. In this hands-on training, you’ll learn how data science can uncover and harness these opportunities. After completing the course, you will be able to select suitable methods for your use cases and make effective use of your data. At Fraunhofer FFB, data-driven production optimization using data science is integrated from the very beginning—starting with factory planning and IT infrastructure through to full-scale production operations. This holistic approach enables well-prepared and successful implementation of data science use cases across departments such as energy management, quality assurance, and manufacturing operations. Leverage our strong practical experience to your advantage!

Course Summary

Join us in exploring the future of battery cell manufacturing through digitalization! This course equips you with knowledge on how to identify use cases, understand the importance of data quality, and apply artificial intelligence effectively. Together, we’ll develop models and show you how to fully utilize data in battery production to drive innovation and optimize processes.

Vertiefung
Digitalisierung
On-site training

Please register for the course via email »ellb@ffb.fraunhofer.de«

Summer session: June 25-26, 2025

Fall session: November 19-20, 2025

OVERVIEW
Type of event
On-site training
Format
Attendance
Graduation
Certificate of attendance
Dates, registration deadline and location
  • June 25-26, 2025
  • In-person seminar in Münster, Germany
Duration
13 learning hours over two consecutive days
Language
German
Price
1.490 Euro (USt. befreit gemäß §4 Nr. 22 Buchstabe a UStG)
Attendance
On-site
Organizer
Fraunhofer FFB
Event location
Münster
TARGET GROUP AN REQUIREMENTS

Target Audience

  • Graduates of the module “Introduction to Battery Cell Manufacturing”
  • Stakeholders in battery production (e.g., supervisors, production managers, department heads) with only basic knowledge of digitalization
  • Technical and scientific staff or engineers interested in data science and data analysis, who plan to implement their own data science projects in the future

Requirements

  • Participants must bring their own laptop
  • Basic knowledge of battery cell manufacturing and digitalization is required
  • Hands-on methods can be completed without programming experience; however, basic understanding (e.g., from university or vocational training) and prior knowledge of statistics are beneficial
  • Upon request, we’re happy to provide information on free introductory and foundation courses
  • Programming skills are less critical for stakeholders, but more relevant for participants aiming to carry out data science projects independently
ADVANTAGES AT GLANCE

Immerse Yourself in an Interactive Learning Environment

Experience a dynamic and engaging learning atmosphere that combines hands-on methods and practice-oriented exercises with an emphasis on collaboration and active knowledge exchange. Ask your questions directly to our battery experts and benefit not only from their in-depth expertise, but also from the diverse experiences and perspectives of fellow participants.

LEARNING OBJECTIVES AND GOALS
  • Identifying and Leveraging Potentials in Resource Efficiency, Emission Reduction, and Process Stability Through Data Science
  • Suitable methods for use cases to enable effective data utilization

 

After completing this course, you will...

...be able to classify and understand the fields of data science, artificial intelligence, and machine learning (ML), and know how they contribute to improving battery cell manufacturing (BCM).

...understand which data is collected where in BCM, how to assess data quality, and be familiar with data preparation methods to enhance data quality for modeling.

...understand the roles of ML models in BCM, and know how to evaluate and influence their performance.

© Fraunhofer FFB

Franziska Schulze Bockeloh

As a research associate in the »Digitalization of Battery Cell Manufacturing« group, I have been working since 2023 on data-driven production optimization and the integration of digital technologies into manufacturing processes. This includes, among other things, the development of predictive quality models aimed at reducing inspection efforts and improving quality assurance. These approaches make a significant contribution to enhancing efficiency and ensuring quality in battery cell production.

Julian Wonneberger

I have been working at Fraunhofer FFB for 1.5 years in the »Digitalization of Battery Cell Manufacturing« group. My main responsibilities include data traceability in production and the analysis of cause-and-effect relationships. To achieve this, I apply both data-driven methods and production expertise. Through this work, I help identify optimization potential and develop implementation strategies that enhance efficiency and quality in manufacturing.

 

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