The patent proposes a method for rapidly sorting retired batteries using a support vector machine (SVM) algorithm. First, a binary feature vector is constructed from the external parameters of the retired batteries, followed by a primary classification to distinguish batteries for echelon use and direct recycling. Afterward, the battery is charged at a high rate, and the incremental capacity (IC) curve is obtained to extract key features as secondary sorting indicators. These results, combined with the primary classification, are input into a multi-classification model to achieve precise sorting, improving efficiency and accuracy in handling batteries of varying health states.

Recycling of massive spent lithium-ion batteries (LIBs) is urgently required with the development of electric vehicles and energy storage industries. However, due to their complex composition and uncertain state, spent LIBs pose significant fire hazards during the recycling process. In this work, liquid nitrogen (LN) and dry ice (DI) were utilized as refrigerants to investigate the inerting mechanism and thermal stability of spent LIBs. Post-mortem and thermal analyses indicated that when spent LIBs are subjected to low temperatures (below −60 °C), the solidification of the electrolyte and the separation of internal components cause an increase in internal resistance, leading to a drop in terminal voltage where it cannot deliver energy. Nail penetration tests demonstrated that cryogenic freezing effectively suppresses thermal runaway, reducing peak internal battery temperatures from 921.2 °C to below 150 °C, with a temperature rise rate suppressed to under 3 °C/s. Additionally, DI exhibited a more sustained cooling effect than LN and is proposed as a safer and more cost-effective alternative for enhancing safety in LIBs recycling.

With the increasing number of lithium-ion batteries (LIBs) reaching the end of their life, the potential for reusing these retired LIBs has become a critical area of research. However, inconsistencies among retired batteries and high sorting costs remain major obstacles. Current methods rely on experimental testing or algorithm-based data analysis to identify aging indicators, yet they often fail to balance precision with efficiency. To address these challenges, this study introduces an optimized high-rate incremental capacity (IC) curve acquisition method for the rapid and accurate extraction of sorting features. By systematically evaluating the trade-offs between model accuracy and sorting time, two feature combination strategies are proposed to improve the sorting process. The proposed method employs a two-step classification strategy for high-efficiency classification, achieving an accuracy of 98.1 % and 95.1 % for binary and multi-class classification, respectively. Compared to conventional methods, it increases sorting efficiency sixfold and significantly improves battery consistency, with capacity and internal resistance enhanced by 64.85 % and 82.71 %, respectively. This high-precision, machine-learning-based sorting approach addresses critical barriers in LIBs reuse, enabling reduced sorting costs and improved battery performance. It represents a significant step toward a circular economy for LIBs and contributes to the broader development of sustainable energy storage technologies.