MACHINE LEARNING-ASSISTED THERMAL RUNAWAY RISK DETECTION IN LITHIUM-ION BATTERIES

Abstract

The increasing deployment of lithium-ion batteries in electric vehicles, energy storage systems, and portable electronic devices has created significant challenges related to battery safety and thermal management. Thermal runaway is one of the most critical failure mechanisms in lithium-ion batteries, causing rapid temperature escalation, cell damage, fire hazards, and potential safety risks. Conventional battery monitoring approaches mainly depend on threshold-based temperature monitoring and reactive protection mechanisms, which provide limited capability for early detection of abnormal thermal behavior. This paper proposes a Machine Learning-Assisted Thermal Runaway Risk Detection Framework for Lithium-Ion Batteries that integrates real-time battery monitoring, Machine Learning algorithms, thermal behavior analysis, predictive modeling, and intelligent risk assessment mechanisms. The proposed framework analyzes battery parameters including temperature variation, voltage fluctuations, current behavior, state-ofcharge, internal resistance, and environmental conditions to identify early indicators of thermal instability. Machine Learning models detect complex patterns associated with thermal runaway initiation and provide predictive risk estimation before critical failure conditions occur. Experimental analysis demonstrates that the proposed framework improves early fault detection accuracy, reduces response time, enhances battery safety, and supports proactive thermal management compared with conventional monitoring approaches. The proposed solution provides an intelligent, scalable, and reliable approach for nextgeneration battery safety management systems. Keywords: Machine Learning, Thermal Runaway Detection, Lithium-Ion Battery, Battery Safety, Predictive Analytics, Thermal Management, Fault Detection, Battery Management System, Electric Vehicle, Intelligent Monitoring.