Optimization of Retired Lithium-Ion Battery Pack Reorganization and Recycling Using 3D Assessment Technology

Abstract

This study introduces a sophisticated methodology that integrates 3D assessment technology for the reorganization and recycling of retired lithium-ion battery packs, aiming to mitigate environmental challenges and enhance sustainability in the electric vehicle sector. By deploying a kernel extreme learning machine (KELM), variational mode decomposition (VMD), and an advanced sparrow search algorithm (SSA), the research achieves a marked increase in the precision of battery classification and performance forecasting. Implementing a three-dimensional dynamic evaluation model, the study optimizes battery pack grouping strategies, culminating in superior secondary utilization rates, extended operational lifespans, and minimized ecological footprints. The research demonstrates that balanced weight distribution strategies, which maximize energy density to 61.37571 Wh/L and cycle counts up to 947 cycles, are pivotal for the efficient reorganization of battery packs, substantiating the economic feasibility and environmental sustainability of recycling initiatives. Future endeavors will extend this research to investigate the influence of diverse battery materials and morphologies on reorganization efficacy, with the aim of broadening the application horizons to include real-world scenarios, thereby refining battery performance and lifespan predictions and propelling forward the frontiers of recycling technology and policy development.