ARTIFICIAL INTELLIGENCE-BASED EARLY PREDICTION OF THERMAL RUNAWAY IN AGV BATTERY CELLS

Automated Guided Vehicles (AGVs) in semiconductor manufacturing operate continuously with lithium-ion battery packs which undergo thermal runaway under abusive conditions such as over-charge, over-discharge or cooling failure. This study presents an artificial-intelligence (AI) - based early - warning framework that predicts imminent thermal runaway in AGV battery cells. We collected a large-scale dataset of 12.7 million time-series records from the battery-management system (BMS) of AGVs in the real factory environment, including cell-level temperature, voltage, current, alarms and cycle counts information. Three predictive models — XGBoost, 1D CNN and LSTM — were developed to forecast the next-step maximum cell temperature using a 24-minute sliding window. LSTM achieved the best performance (MAPE ≈ 1.66 %) compared to CNN (≈ 1.67 %) and XGBoost (≈ 1.76 %). Based on LSTM residuals and predicted threshold exceedance, an early warning mechanism was implemented, raising alarms several minutes before the temperature crossed critical limits. The results demonstrate that AI-driven forecast algorithm enables practical pre-runaway warning and can be embedded in AGV safety-monitoring systems in high-throughput semiconductor logistics applications.