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 (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale applications. To address these issues, this paper presents a rapid MRC estimation method using a hybrid Convolutional Neural Network (CNN), Conv Block Attention Module (CBAM), and Long Short-Term Memory (LSTM) architecture. The proposed approach extracts key voltage and capacity features from only the initial 30 min charging phase, integrating both factory and laboratory data. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies, and the CBAM adaptively emphasizes critical characteristics. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, achieving a testing R2 of 98.05% and a Mean Absolute Percentage Error (MAPE) of 1.60%. These results highlight the superior performance of the proposed framework, exhibiting strong potential for high-throughput battery sorting and large-scale health monitoring systems.