A rapid sorting framework for retired lithium-ion batteries via electrode-level aging state diagnosis

The second-life utilization of retired lithium-ion batteries (LIBs) is critical to achieving global sustainability goals, yet prevailing industrial sorting strategies suffer from inherent limitations. Specifically, current methods rely on full-cell features, causing poor electrode-level aging consistency in reassembled packs and lacking fast, low-cost testing for economic viability. This study proposes a rapid sorting method tailored to the electrode-level aging pathways of retired LIBs, with the aim of guiding rational sorting based on electrode-level aging diagnosis. Concretely, LIB aging pathways are determined by estimating full-cell capacity, lithium inventory, and cathode/anode capacity via voltage curve fitting. Also, a mapping model library consisting of 1260 models correlates rapid-test resistance features with the four aforementioned capacity metrics. Experiments show mechanism-derived features play a dominant role in capacity estimation, yielding high precision with an average R ² of 0.9 and an optimal R ² exceeding 0.95. Furthermore, the method enables targeted and accurate diagnosis of cathode and anode aging states that are consistent with the actual operational conditions of LIBs. Compared with conventional sorting strategies, the proposed approach significantly improves the intra-pack capacity consistency, reducing the cell-to-cell capacity variation by 19.9%, 16.1%, and 13.2% at discharge rates of 0.2C, 1C, and 2C, respectively. This work realizes rapid and accurate estimation of the electrode-level aging states of retired LIBs, enhances the consistency and reliability of recombined battery packs, and thus provides technical support for the industrialization of LIB second-life utilization.