A large number of battery pack returns from electric vehicles (EV) is expected for the next years, which requires economically efficient disassembly capacities. This cannot be met through purely manual processing and, therefore, needs to be automated. The variance of different battery pack designs in terms of (non-) solvable fitting technology and superstructures complicate this. In order to realize an automated disassembly, a computer vision pipeline is proposed. The approach of instance segmentation and point cloud registration is applied and validated within a demonstrator grasping busbars from the battery pack. To improve the sorting of the battery pack components to achieve high-quality recycling after the disassembly, a labeling system containing the relevant data (e.g., cathode chemistry) about the battery pack is proposed. In addition, the use of sensor-based sorting technologies for peripheral components of the battery pack is evaluated. For this purpose, components such as battery pack and module housings of multiple manufacturers were investigated for their variation in material composition. At the current stage, these components are usually produced as composites, so that, for a high-quality recycling, a pre-treatment may be necessary.

Electric mobility is on the verge of becoming a mass market. Major automotive OEMs have initiated programs to electrify their product portfolio. This transition poses new challenges and requires new innovative concepts in automotive development processes, especially for battery systems as the key component within electric powertrains. Battery system costs account for up to 40% of the electric vehicle’s total costs. Additionally, development cycles of battery systems for automotive applications are characterized by long development periods. Hence, the initiatives to advance electrification result in numerous development projects affiliated with significant development expenses. Battery systems can be referred to as mechatronic and electrochemical systems. They require a complex interaction of diverse scientific and engineering disciplines. Fast innovation cycles have effects regarding product requirements and assumptions towards their allocation. Hereby, uncertainties can lead to risks within development projects, especially in terms of time and costs. In current development processes, necessary changes are only dealt with reactively, causing unplanned additional expenses and delays. Thus, there is need for handling potential changes proactively, i.e. managing uncertainties leading to those changes as early as possible. New methods are necessary to identify and handle uncertainties of complex product systems within requirements engineering. An approach towards comprehensive uncertainty management is taken within this publication.