Project cooperationUpdated on 10 July 2025
Looking for Partners in Battery Remanufacturing & Reuse (POEN Europe GmbH)
About
POEN Europe GmbH, based in Stuttgart area, Germany, specializes in EV battery remanufacturing, reuse, and second-life applications. We are actively seeking collaboration partners across Europe and other regions who share our vision of building sustainable and circular battery value chains.
We work with OEMs, mobility service providers, recycling firms, and second-life storage integrators. Our expertise covers battery health diagnostics, smart disassembly, cell/module-level evaluation, and repurposing solutions for energy storage or automotive reuse.
We are particularly open to partnerships in the following areas:
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Sourcing used EV battery packs (damaged or end-of-life)
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Joint development of evaluation and remanufacturing solutions
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Pilot projects in second-life storage applications
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Research consortiums or EU-funded circular economy projects
If you're interested in collaborating with POEN Europe GmbH, we’d love to hear from you. Let’s build the future of sustainable battery use — together.
Stage
- Early idea
- Already defined
Topic
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Network design of reverse supply chains
Type
- Consortium seeks Partners
- Expertise offered
Attached files
Organisation
Similar opportunities
Expertise
VARTA Storage GmbH - Battery Storage Producer for EU R&D Collaboration
Dennis Hein
Innovation Scientist / Research Coordinator at VARTA Storage GmbH
Nördlingen, Germany
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
Circular Adaptation for Road Vehicle Sustainability (CARS)
- Early idea
- Already defined
- Consortium seeks Partners
- Enabling technologies | Network design of reverse supply chains
- 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
- Enabling technologies | Tools and solutions addressing challenges emerging from product focused regulations (such as the ESPR)
- 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 | 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)
Alireza Ahmadi
Professor-Operation and Maintenance Engineering at Luleå University of Technology-Division of Operation and Maintenance Engineering
Luleå, Sweden