Rapid Estimation for the Maximum Remaining Capacity of Retired Lithium-Ion Batteries Based on CNN-CBAM-LSTM

With the continuous increase in the number of retired lithium-ion batteries, accurately and quickly estimating their MRC has become a key challenge for the rapid sorting and secondary utilization of retired lithium-ion batteries. Conventional detection methods often suffer from low efficiency, prolonged detection cycles, and limited scalability for large-scale applications. To address these issues, this paper presents a fast MRC estimation method for retired lithium-ion batteries using a hybrid Convolutional Neural Network (CNN)-Conv Block Attention Module (CBAM)-Long Short-Term Memory (LSTM) architecture (CNN-CBAM-LSTM). The proposed approach integrates both factory-scale test data and laboratory experimental data to extract key voltage and capacity features from the initial 30-minute charging phase. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies in the time-series data, and the CBAM enhances feature representation by emphasizing critical characteristics. Experimental results demonstrate that the proposed method achieves MRC estimation within 30 minutes, significantly outperforming traditional approaches in terms of accuracy. The R² value increased to 99.42%, while the MAPE decreased to 1.55%. These results highlight the superior performance of the proposed method, which not only holds strong potential for rapid battery sorting and cascaded utilization but also exhibits broad applicability in large-scale battery health monitoring systems.