The Battereverse project (May 2023 – October 2026), after the first 18 months of the project, has reached main achievements. Focused on defining technical, legal, and business requirements, RUL assessment, and external insights through circular analysis, a mapping of key European industrial and legal stakeholders have been performed with the creation of a model. A list of 14 key stakeholder types were identified, and usual battery handling processes were defined too, with the objective to analyze potential risks associated to the processes. Preliminary process hazard analysis (PPHA) method was chosen. Standards and requirements (regulation (EU) 2023/1542 of the European Parliament and of the Council of 12 July 2023 on batteries and waste batteries) have been analysed. In the AIDC domain, standards and solutions in line with GS1 were recommended for identifying, marking and sharing data, using automatic identification techniques. A comprehensive data model has been proposed, aligned with other European and global initiatives for a Digital Battery Passport, and a corresponding blueprint for a system architecture to streamline data exchange during battery collection, repurposing, and recycling. Moreover, initial work has been performed on mechanisms for data governance, including mapping of information clusters and technology review. The technical and legal requirements have been firstly defined, focusing on the packaging and transportation steps. The Skoda modules characterization has been defined and partially executed, as well as the architecture and the design of the hardware and software of the DCH tools. The safe prototype transport has been designed and validated. An automated system has been developed for laboratory and industrial environments to dismantle and sort battery pack components, minimizing human exposure to the batteries and other hazardous elements, thereby reducing health risks and associated labour costs. In parallel, a hybrid battery characterization tool, based on an electrical and acoustic diagnosis battery tests combined with Machine Learning (ML)-algorithms able to merge different sorts of data, will be developed in the project, to deliver not only the battery status but also a prediction of the expected 2nd lifetime. In terms of communication, dissemination, exploitation and stakeholder engagement, the project has brought together 306 battery reverse logistics stakeholders through the BatteReverse community, sharing news (5 circular business cases, 5 Short Circuit webinars, the Battery Circularity Game, conference and webinar presentations and 1 peer-reviewed journal paper,…).
