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.