A Knowledge Graph Based Disassembly Sequence Planning For End-of-Life Power Battery

The accurate and efficient intelligent planning of disassembly sequences plays a crucial role in ensuring the high-quality recycling of end-of-life power batteries. However, the solution space obtained by the metaheuristic algorithm is often incomplete, resulting in suboptimal sequence accuracy. Additionally, the complex and dynamic disassembly information associated with end-of-life power batteries poses challenges in analysis and reuse, leading to low efficiency in disassembly sequence planning. To address these issues, we propose a novel approach for planning disassembly sequences based on the knowledge graph representation of power batteries. Firstly, we construct an updateable and scalable disassembly information model using knowledge graphs to capture the dynamic information and assembly relationships among battery parts. Subsequently, we utilize a combination of topological sorting and backtracking algorithms on the constructed disassembly information graph to derive the optimal disassembly sequence. Finally, we demonstrate the feasibility and effectiveness of our approach through an illustrative case study involving an end-of-life power battery pack.