@article{Zhang:2026:10.1016/j.etran.2026.100591,
author = {Zhang, J and Tong, Z and Ren, N and Chen, X and Cao, XE},
doi = {10.1016/j.etran.2026.100591},
journal = {Etransportation},
title = {Developing an efficient hierarchical regrouping method for uncharacterized retired electric vehicle Li-ion batteries based on partial pulse discharge curves},
url = {http://dx.doi.org/10.1016/j.etran.2026.100591},
volume = {28},
year = {2026}
}

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TY  - JOUR
AB  - The echelon utilization of retired electric vehicle lithium-ion batteries (REV-LIBs) has been proven to reduce the carbon emissions and costs throughout the whole life cycle, an advantage that has been fully verified through diverse application scenarios such as energy storage systems. However, the significant performance discrepancies among cells and time-consuming characterization remain key bottlenecks hindering its large-scale implementation. To address these challenges, this study proposes a data-driven hierarchical screening and grouping framework for REV-LIBs with unknown initial states, based on partial pulse testing and eliminating the need for pre-adjustment of initial states. By analyzing the unsupervised clustering results derived from different features, the incorporation of polarization characteristics into clustering features improves the consistency of batteries in the same group by 7.3%, compared to the conventional clustering method relying on capacity and direct current internal resistance (DCIR). Subsequently, partial pulse discharge curves and a two-dimensional convolutional neural network (2D-CNN) model are employed to conduct classification and estimate the capacity and DCIR of retired batteries, respectively, with experimental results showing that this approach achieves a classification accuracy exceeding 96.0%, the maximum absolute error (MaxAE) of capacity estimation below 3.0%, and that of DCIR estimation below 5.0%. The test duration can be shortened to 18 min by increasing the discharge rate and shortening the rest time. Finally, topology-aware regrouping is implemented to further enhance the capacity uniformity of series branches by over 52.9% and the resistance uniformity of parallel branches by more than 86.0%. This research presents a practical and feasible solution for both the estimation of capacity and DCIR and the multi-level regrouping of REV-LIBs with unknown initial states, which is highly significant for large-scale engineeri
AU  - Zhang,J
AU  - Tong,Z
AU  - Ren,N
AU  - Chen,X
AU  - Cao,XE
DO  - 10.1016/j.etran.2026.100591
PY  - 2026///
TI  - Developing an efficient hierarchical regrouping method for uncharacterized retired electric vehicle Li-ion batteries based on partial pulse discharge curves
T2  - Etransportation
UR  - http://dx.doi.org/10.1016/j.etran.2026.100591
VL  - 28
ER  - 

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