AI-Based Risk Estimation Framework for Safety and Sustainability in Lithium- Ion Battery Recycling

다국어 초록 (Multilingual Abstract)

The rapid expansion of electric vehicles and energy storage systems has led to an unprecedented accumulation of end-of-life lithium-ion batteries (LIBs), creating both environmental challenges and safety hazards. This study proposes an AI-based risk estimation framework that integrates real-time sensor data and machine learning analytics to enhance the safety and efficiency of LIB recycling processes. The framework comprises four key layers—data acquisition, preprocessing, AI risk estimation, and decision support—and applies a hybrid Deep Neural Network (DNN) and Gradient Boosted Tree (GBT) model to predict potential hazards across mechanical dismantling, thermal pretreatment, and hydrometallurgical recovery stages. A pilot-scale experimental setup was constructed, collecting over 10,000 time-series samples of temperature, gas emission rate, pH, pressure, and current under varying recycling conditions. The proposed model achieved 98% accuracy, 0.97 AUC, and a 45% reduction in mean time-to-alarm (MTTA) compared with rule-based monitoring systems, providing proactive early warnings 2–3 minutes before hazardous events. Explainability analysis using SHAP values identified temperature deviation (ΔT) and gas emission rate (G) as the dominant contributors to risk prediction, jointly explaining over 60% of model variance. The system maintained stable performance under noisy and incomplete data, confirming its applicability for real-time industrial deployment. Overall, this work demonstrates that AIdriven predictive intelligence can significantly improve safety, sustainability, and operational reliability in LIB recycling, supporting the transition toward smart, circular, and Industry 5.0–aligned recycling systems.