The rapid growth of electric vehicles has increased the demand for efficient and safe recycling processes for end-of-life electric vehicle battery packs. However, as pack structures, fastening locations, module layouts, cables, and auxiliary components vary significantly across vehicle models, battery pack disassembly is still highly dependent on manual labor. This study presents the development and experimental validation of a human–robot collaborative disassembly system for end-of-life EV battery packs. The proposed system integrates model-database-based part information, perception-assisted task execution, collaborative and industrial robots, customized end-effectors, and a conveyor-based process layout. A full-scale testbed was constructed in a 20 m × 8 m workspace, which consisted of twelve main disassembly processes, main and sub-conveyor systems, eight collaborative robots, and two industrial robots. The target battery pack was disassembled through the following sequence: robotic bolt removal, robotic cover handling, manual removal of cables and connectors, robotic module bolt removal, module transfer, bracket removal, module inversion, lower bolt removal, and cutting operations. A process time analysis based on the measured unit process times showed that the calculated total time for the first disassembly cycle was 255.7 min, while the calculated continuous disassembly time after the first cycle was 195 min. Furthermore, for a configuration with one main conveyor and five sub-conveyors for parallel module-level operations, the continuous disassembly time was estimated at 40 min. These calculated results indicate that, for the investigated battery pack model, process overlap and parallel module-level processing can substantially reduce the effective disassembly time of the integrated line. As continuous operation of the complete line was not performed experimentally, the study is presented as a system-level feasibility demonstration. As these reductions were obtained within the context of the proposed system, a comparison with conventional manual disassembly under equivalent conditions remains a topic for future research.
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Yun et al. (2026) studied this question.
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