BT/MT: »AI-driven digital twin to enable a closed-loop lifecycle of vehicle batteries«

The shift to renewable energy sources due to climate change is leading to a boom in demand for battery cells for a wide range of applications, including in the commercial vehicle sector. Battery packs that no longer meet the high requirements of transportation can often be reassembled flexibly in a resource-saving manner for alternative second-life use, e.g., as buffer batteries for wind farms (so-called remanufacturing). Due to the high diversity of variants, this entails pronounced complexities for production control. The digitalization and networking of production, driven by Industry 4.0, holds great potential with regard to the optimization and flexible control of production, among other things through the use of digital twins and the subsequent, beneficial processing of all generated data using machine learning methods.

 

The aim of this thesis is to develop a concept for the physical and digital implementation of a traceability system in the context of remanufacturing of commercial vehicle batteries, as well as the conceptual design of a corresponding digital twin, which enables the use of smart prediction algorithms and decision systems. The validation of the developed conceptual design is carried out within the framework of the project, which aims at the development of a modular, competitive production plant for the remanufacturing of batteries for a second-life application.

 

What you will do

• Familiarization with the state of the art on the topic complex of traceability, artificial intelligence, digital twins and battery cell production
• Identification of physical and IT requirements for traceability systems and digital twins
• Development of a methodology for the implementation of a traceability system and digital twin in the context of flexible remanufacturing of commercial vehicle batteries, incorporating a holistic digital data pipeline from acquisition to processing in ML algorithms
• Application in the context of battery remanufacturing through expert interviews and/or a prototypical implementation based on a real application scenario
• Evaluation and documentation of the results

 

What you bring to the table

• You are studying mechanical engineering, industrial engineering, computer science or a comparable major
• Interest in trend topics such as digitalization, data science, artificial intelligence and digital twins
• A high degree of initiative and team spirit
• Very good language skills in German and English

 

What you can expect

• Ideal conditions for practical experience alongside your studies
• Professional supervision and collaboration in a dedicated team
• A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
• Flexible working to combine study and job in the best possible way

 

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. 

 

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!

 

 

Questions according to this position will be answered by:
Alexander Gabriel M.Sc.
Research assistant Production Quality
Phone: +49 241 8904-524

Fraunhofer Institute for Production Technology IPT 

www.ipt.fraunhofer.de 

 

Requisition Number: 65543                Application Deadline: