Fraunhofer Research Institution for Battery Cell Production FFB
Fraunhofer Research Institution for Battery Cell Production FFB

Data Science in Battery Cell Production

By request / on-site

Learn how Data Science is becoming a key technology for battery cell production

In this specialized training module, you will explore how data-driven methods can significantly improve efficiency, quality, and sustainability in battery manufacturing. You will gain an understanding of typical use cases, learn how to assess data quality, and select the right tools for your specific production environment.

At Fraunhofer FFB, we take a holistic view of Data Science – from factory planning and IT infrastructure to day-to-day production operations. This practical course not only teaches you the methodological foundations, but also demonstrates how companies across the battery value chain benefit from data-driven processes – whether in energy management, quality assurance, or shop floor operations.

In this training course, you will gain: 

  • An in-depth understanding of data quality and process integration
  • A detailed understanding of relevant data science use cases and how to identify them
  • An introduction to AI-supported analysis methods
  • Practical know-how through the development and application of simple models
  • Applicable knowledge based on a practical example taken directly from battery cell production
OVERVIEW
Type of event
On-site training
Format
Attendance
Graduation
Certificate of attendance
Dates, registration deadline and location
  • By Request
Duration
13 learning hours on two consecutive days
Language
By Request
Organizer
Fraunhofer FFB
Event location
By Arrangement
TARGET GROUP
 
  • Stakeholders in battery cell production (e.g. foremen, production managers, department heads) who only have basic digitalization know-how to date.
  • Technical and scientific employees or engineers with an interest in data science and data analysis who would like to carry out their own data science projects in the future.

Requirements

  • It is necessary to bring your own laptop to participate.
  • Basic knowledge of battery cell production and digitization is required.
  • Hands-on methods can also be carried out without programming knowledge. However, basic knowledge (e.g. from studies/training) and prior knowledge of statistics are advantageous. (We will be happy to send you information on free introductory and basic courses on request).
  • Programming knowledge is less important for stakeholders than for participants who will carry out data science projects independently in the future where wollen. 

 

 

 

 

CONTENT AND EXPIRATION

Experience an interactive learning environment that not only impresses with a variety of practical exercises and well thought-out hands-on methods, but also with the promotion of active exchange and joint learning. You will not only have the opportunity to ask questions, but also to benefit from the experiences and perspectives of other participants.

LEARNING GOALS
  • Uncovering and utilizing the potential for resource efficiency, emission reduction and process stability through data science
  • Suitable methods for use cases for effective data use

After participating...

...you will be able to classify the topics of data science, artificial intelligence and machine learning (ML) and understand how they contribute to the improvement of battery cell production.

...understand which data is collected at which point in cell production, how you can evaluate its quality and know methods of data preparation to improve data quality for modeling.

...understand which tasks an ML model fulfills in the battery cell production and how the performance of the model can be evaluated and influenced.

SPEAKERS

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