THERMAL-AWARE PREDICTIVE ANALYTICS FOR BATTERY MANAGEMENT SYSTEMS IN ELECTRIC VEHICLES

Abstract

The rapid expansion of electric vehicles has increased the demand for safe, reliable, energyefficient, and intelligent battery management systems capable of maintaining lithium-ion battery packs under highly dynamic operating conditions. Battery temperature is one of the most influential parameters affecting electrochemical performance, charging efficiency, power availability, degradation rate, state estimation accuracy, and operational safety. Conventional battery management systems primarily depend on threshold-based temperature monitoring and reactive control mechanisms that initiate cooling or power limitation only after predefined thermal limits are approached. Such methods are often inadequate for modern electric vehicles because battery temperature evolves according to complex interactions among charging current, discharge demand, ambient conditions, driving behavior, cell aging, cooling effectiveness, and internal electrochemical characteristics. This paper proposes a Thermal-Aware Predictive Analytics framework for Battery Management Systems in Electric Vehicles. The proposed methodology integrates multi-source battery sensing, real-time data preprocessing, thermal feature engineering, predictive machine learning, battery state estimation, anomaly detection, thermal risk classification, and adaptive battery management decisions. Temperature, current, voltage, state of charge, charging rate, vehicle speed, ambient temperature, and cooling-system information are continuously analyzed to forecast future thermal behavior before critical conditions occur. The framework supports early detection of abnormal temperature rise, cell-level thermal imbalance, cooling inefficiency, accelerated degradation risk, and potential thermal runaway precursors. A predictive decision layer converts analytical outputs into actionable recommendations for cooling control, charging-current adjustment, power derating, maintenance alerts, and driver notification. The proposed framework is conceptually evaluated against a conventional threshold-based battery management approach. The results indicate improvements in thermal event prediction accuracy, early warning capability, temperature uniformity, cooling energy utilization, and battery health preservation. The study demonstrates that thermal-aware predictive analytics can transform battery management from reactive protection into proactive intelligence, thereby supporting safer operation, improved battery longevity, and more sustainable electric mobility.