Impact Factor:6.3

DOI number:10.1109/TMECH.2026.3708599

Teaching and Research Group:W. Ma, G. Luo, H. Xiao, M. Lin, J. Wu

Journal:IEEE/ASME Transactions on Mechatronics

Key Words:Battery sorting; echelon utilization; electrochemical impedance spectroscopy (EIS); lithium-ion battery (LIBs); multimodal learning

Abstract:The rapid increase of retired lithium-ion batteries has posed critical challenges for sustainable energy management and intelligent recycling systems. Efficient echelon utilization requires accurate sorting strategies to ensure the consistency and reliability of reassembled packs. However, existing methods primarily rely on single-modal charge–discharge data, neglecting the internal electrochemical states that fundamentally influence battery behavior. In this study, we propose a multimodal sorting method that enables synergistic fusion of external dynamic responses and internal electrochemical characteristics for enhanced decision accuracy. Electrochemical impedance spectroscopy (EIS) is introduced as a complementary modality to conventional discharge curves, offering insights into complex internal reactions and electrode interface changes within batteries. EIS signals are encoded into 2-D representations via Gramian angular field to achieve feature augmentation. A dual-branch residual network is designed to accommodate different modal input forms, while a bidirectional cross-attention mechanism is employed to facilitate deep feature interaction across modalities. Comprehensive experiments on retired commercial lithium-ion batteries demonstrate that the proposed model performs excellently across multiple evaluation metrics, attaining an average classification accuracy of 94.00%, which highlights its effectiveness and potential for echelon utilization.

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Page Number:Early Access

Translation or Not:no

Date of Publication:2026-07-15

Links to published journals:https://ieeexplore.ieee.org/document/11611614