Cross-cutting areas

Making manufacturability and recyclability integral parts of battery research and development at an early stage.

The battery of the future will be designed based on virtual representation considering sustainability and circular economy concepts including life cycle assessment. Materials sourcing, processing, manufacturing and assembly processes must be tailored to accommodate new chemistries and follow innovative approaches to allow for efficient remanufacturing and re-use requirements.

The manufacturability and recyclability of batteries are thus key cross-cutting areas that will develop through close collaboration between those addressing themes I and II. From the outset, new knowledge and ideas about how to manufacture and recycle batteries will inform the materials discovery and development processes.

The manufacturing of future battery technologies is addressed in this roadmap from the standpoint of the fourth industrial revolution, i.e., Industry 4.0 and digitalisation. The power of modelling and the use of AI should be exploited to deliver “digital twins” for both innovative cell designs, avoiding or substantially minimising classical trial-and-error approaches, and manufacturing methodologies.

The new materials and cell architectures envisioned in BATTERY 2030+, call for new recycling concepts, such as reconditioning or reusing active materials and electrodes. To pave the way for such a shift, material suppliers, cell and battery manufacturers, main application actors, and recyclers will be directly coupled to accommodate the constraints of recycling when developing new batteries. The discovery of new materials using BIG–MAP will integrate parameters such as recyclability, critical raw materials, and toxicity into the algorithms.

With these two research areas, Theme III will ensure that all research approaches will consider the feasibility of scaling up new materials and battery cells as well as the possibility of recycling and reusing battery components at low cost and using climate-neutral approaches.

Illustration showing the concept of a digital twin.
AI-driven design and manufacturing methodologies linked together as a whole. 
The short-, medium- and long-term goals for manufacturability and recyclability
Research areas Manufacturability Recyclability
Short-term (3 years)
  • Improving simulation tools, such as multiphysics models for reducing the computational burden of the manufacturing process. 
  • Demonstrating the implementation of current AI technologies through deep learning and machine learning methods for cell design (for Li-ion chemistries). 
  • Implementation of the AI-driven methodology for manufacturing (Li-ion chemistries) – including digitalisation. 
  • Improving and scaling up of new manufacturing processes (3D printing, dry processing). 
  • Integrated design for sustainability and dismantling.
  • Demonstration of new technologies for battery packs/modules sorting and re-use/re-purposing.
  • Establishing a European system for data collection and analysis. 
  • Developing automated dissassembly of battery cells. 
Medium-term (6 years)
  • Proof of concept of a digital twin of a cell design (based on Li-ion chemistries).
  • Proof of concept of a digital twin of a cell manufacturing process (based on Li-ion chemistries).
  • Input from BIG, MAP, sensing, self-healing, recycling and other innovation areas integrated into the design and manufacturing process.
  • Digitial twin methodology adapted to the manufacturability of new battery technologies and innovative new manufacturing processes.
  • Demonstrating automated cell dissassembly into individual components. 
  • Sorting and recovery technologies for powders and components and their reconditioning to new active battery-grade materials demonstrated.
  • Significantly improve, relative to current process, the recovery rate of critical raw materials. 
  • Testing of recovered materials in battery applications.
  • Develop prediction and modelling tools for the reuse of materials in secondary applications.  
Long-term (10 years)
  • An AI-driven methodology established for manufacturing, by integrating cell design sub-loops that converge in a fully autonomous prototype system nourishing from BIG-MAP. The new concept is deployed to the industry and academia.
  • This methodology, which will help found a new commoditised state-of-the-art, will be progressively deployed in industry and academia. 
  • A full system for direct-recycling is developed and qualified.