Project cooperationUpdated on 20 August 2025
Use of the Asset Administration Shell to increase the degree of automation in the dismantling and reuse determination of battery-powered systems
About
The proposed project aims to promote the circular economy within the field of battery-powered systems. The goal is to enable the economic recycling of batteries in line with the circular economy and new business models. The project is divided into two key areas: designing battery systems for easy dismantling and verifying this through robot-assisted, automated dismantling; and determining the condition of individual battery system components for automatic decision-making on the recycling strategy.
The development of easy-to-disassemble designs is done in an agile approach by verifying each design concept by an automated robot-based disassembly. The robots program is therefore automatically generated from the disassembly instruction in the battery passport to avoid the manual, time-consuming task of robot programming. This makes the process economically efficient and resilient to a wide variety of design variants, enabling new business models.
The second key topic focuses on automatically deciding on a recycling strategy for battery system components, such as cells and electronics. Therefore, it is necessary to acquire data throughout the entire lifetime of the battery system. This includes, for example, state variables of battery cells during production, before packaging, and usage patterns when the system is in use. This data is gathered unambiguously for each battery system and depicted in a digital twin. A digital twin is created for each produced battery system using an asset administration shell, which is the Industry 4.0 standard for digital twins. This enables big data analytics. Closely related to this is the holistic assessment of reuse strategies. These are evaluated in terms of their ecological and economic impact.
Stage
- Already defined
Topic
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Design of an adaptable Digital Product Pass:
Type
- Consortium seeks Partners
Organisation
Similar opportunities
Project cooperation
Digital Twins for Remanufacturing
- Already defined
- Consortium seeks Partners
- Enabling technologies | Robotic / handling - and assistance systems
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Adrian Barwasser
Research Fellow at Fraunhofer IAO
Stuttgart, Germany
Project cooperation
LLM4CVC - AI based planning for automated disassembly
- Early idea
- Already defined
- Consortium seeks Partners
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Robotic / handling - and assistance systems
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Approaches to support SME fully exploit the value of existing CVC-related data
- Data technologies | Interoperability of CVC-relevant data ecosystems, quality assurance and traceability across systems
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Tim Schnieders
Researcher at WZL RWTH Aachen University
Aachen, Germany
Expertise
Adaptive Robotics for (Dis-)Assembly
- Reverse Manufacturing
- Industry 4.0 technologies
- Manufacturing and machine learning
- Robotic / handling - and assistance systems
Ron Martin
Group Manager Adaptive Robotics at Fraunhofer IIS, Division Engineering of Adaptive Systems
Dresden, Germany