Battery Data Scientist

Become a battery expert bit by bit - with the ELLB Battery Data Scientist training course!

With the help of digitalization, you can efficiently improve your production, thereby saving costs and emissions. After this course, you can select and use the right technology for your field of application and use your data effectively.

At Fraunhofer FFB, digital twins are integrated into project planning right from the start: Factory planning, building management, production control, and product optimization - all aspects of battery cell production are realized physically and digitally. Benefit from our strong application focus.

Learn more about digital methods in battery cell production with our special hands-on training module..

We will delve into the future of battery cell production via digitalization together! You'll discover how to identify use cases, understand the significance of data quality, and implement artificial intelligence. We'll collaborate to create models and demonstrate how data can be utilized in battery cell production to foster innovation and enhance processes.

After participating...

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

...you will understand which data is collected where in the BCP, how you can evaluate its quality and know methods of data preparation to improve the data quality for modeling

....you will understand which tasks an ML model fulfills in the BCP, and how the performance of the model can be evaluated and influenced.

 

OVERVIEW
Type of event
Attendance Seminar
Format
Attendance
Graduation
Certificate of attendance
Dates, registration deadline and location
  • November, 13. & 14. 2024
  • Münster
Duration
13 learning hours over two days
Language
English
Price
1490 Euro (VAT exempt according to §4 No. 22 letter a UStG)
Organizer
Fraunhofer FFB
Event location
Münster
Target group and requirements
  • Graduates of the module Introduction to Digitalization in battery cell production
  • Stakeholders in battery cell production (e.g. foremen, production managers, department heads) who only have basic digitalization know-how so far.
  • 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.
  •  
  • Basic knowledge of battery cell production and digitization is advantageous.

    Hands-on methods can also be carried out without programming knowledge, but basic knowledge (e.g. from studies/training) is advantageous (we will be happy to send you basic courses on request).

    Programming knowledge is less important for stakeholders than for participants who want to carry out data science projects independently in the future.

Advantages at a glance

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

After participating...

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

...you will understand which data is collected where in the BCP, how you can evaluate its quality and know methods of data preparation to improve the data quality for modeling

....you will understand which tasks an ML model fulfills in the BCP, and how the performance of the model can be evaluated and influenced.

 

TRAINER

Julian Wonneberger

I have been working at Fraunhofer FFB for 1.5 years in the "Digitalization of battery cell production" group. My main tasks include the traceability of data in production and the analysis of cause-and-effect relationships. This involves using both data-based methods and expert knowledge from production. Through these activities, I help to identify optimization potential and develop implementation strategies that lead to greater efficiency and quality in production.