Predictive Thermal Runaway Risk Assessment Using Battery Sensor Analytics and Machine Learning

Abstract

Thermal runaway remains one of the most critical safety challenges in lithium-ion battery systems used in electric vehicles. Rapid temperature escalation caused by overcharging, internal short circuits, mechanical damage, or excessive current can lead to catastrophic battery failures if not detected at an early stage. Traditional battery protection mechanisms primarily respond after abnormal conditions occur, limiting their effectiveness in preventing thermal incidents. This paper proposes a Predictive Thermal Runaway Risk Assessment framework that integrates battery sensor analytics, machine learning, real-time monitoring, and cloud-based predictive intelligence for early thermal hazard detection. The proposed framework continuously analyzes battery temperature, voltage, current, state of charge, and environmental conditions to estimate thermal runaway probability before dangerous operating conditions develop. Experimental evaluation demonstrates improvements in prediction accuracy, fault detection capability, battery safety, operational reliability, maintenance efficiency, and battery lifespan. The proposed intelligent risk assessment framework provides a scalable, proactive, and reliable solution for enhancing thermal safety in next-generation electric vehicle battery systems.