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Three dimensional (3D) Scanning of Battery Packs
Following the manual dismantling of various battery packs during the first six months of project, researchers at University of Agder (UiA) have developed a semi-automated process to address the diverse nature of battery packs. Their advanced robotic system can estimate the size of individual components of a battery pack. Afterwards, using different angles, it identifies optimal locations to capture precisely 3D images of these components, thus ensuring no detail is missed through this comprehensive scanning.
Using different perspectives, this thorough scanning process is further translated into a list of point clouds. Applying sophisticated algorithms, these point clouds are later combined and merged into a solid mesh component. This process is repeated individually for each new component which needs to be scanned.
Scanned components of Battery pack
Beyond geometric characteristics
While geometric characteristics are important, they provide even greater value when combined with other physical attributes and interconnection data of the components. The researchers have successfully documented these details, resulting in a rich digital repository of the battery pack. With the establishment of this detailed digital repository, the focus is now shifting towards its applications, where the primary goal is to automate the disassembly sequences.
Simultaneously, the team is also focusing on the automatic characterisation of battery packs and modules. Significant efforts are channeled towards creating a robust digital simulator, which will serve as a platform for training and rigorous testing of the disassembly planner – currently under development.
Innovation in disassembly tools
At the same time, the research team involved in work package 3 have been working on improving the disassembly operations tools. The results reported positive feedback, with several tools already successfully tested in lab environment. This is a significant step towards the fully automated disassembly process.
A noteworthy development is the automation of the non-destructive disconnection of cables. This procedure is essential as it stands as the second most frequent operation in battery pack disassembly, just after the unscrewing operation.
You can read more about previous activities developed in work package 3 in the article ‘Manual dismantling of a battery pack‘.
During the first six months, University of Adger [UiA] received three battery packs (out of the five planned) and manually disassembled them, opening for further analysis. In the future, this activity will feed a digital repository as promised in the first delivery of Work package 3.
For each battery pack, the analysis includes:
- a precedence graph informing how components are connected, which, in the upcoming steps, will help determine the best order to dismantle these components automatically.
- an Excel table listing the characteristics of each type of component other than geometrical characteristics: number of items, mass, material, or other specific features.
- 3D scanning in the form of point clouds (pcls) to provide information on the geometry and texture of the components constituting the different battery packs. After testing several hardware and algorithms, two of them have been selected.
In parallel, several of the main important tools have already been identified based on the manual disassembly of these three battery packs, and a tool changer is under development. End effectors (tools) will be able to be changed quickly, including their connection to their power source (electric and/or pneumatic) and their signals.
In addition, the disconnection of power and signal cables using non-destructive methods – operation identified as critical, has been investigated and currently, a concept is prototyped and evaluated. The main challenge is to design a tool that “fits them all”. Additional activities carried out within WP3 have investigated different sorting (characterisation) methods, based on temperature, mass loss, and other flaws, such as deformations, leakage, trace of heat damages.
Safety has also been an important part of the work completed within WP3 during the first six months. A complete monitoring system and a set of safety measures to be followed during the scheduled demanufacturing (discharge, sorting and disassembly) activities have been established.
During February, when the researchers started examining the available methods for automatic task planning using search algorithms and/or reinforcement learning, the robotic system adaptability was discussed. In anticipation of the implementation and testing phases of these adaptive robotic methods, thorough battery knowledge stored within the digital repository must first be developed.
