INTELLIGENT THERMAL MONITORING AND HEAT REGULATION FRAMEWORK FOR ELECTRIC VEHICLE BATTERY PACKS

Abstract

The rapid expansion of electric mobility has increased the demand for battery packs capable of supporting high energy density, fast charging, rapid acceleration, regenerative braking, extended driving range, and reliable operation under diverse environmental conditions. Lithium-ion batteries are widely adopted in electric vehicles because of their favorable energy and power characteristics, yet their safety, performance, charging capability, degradation behavior, and service life remain strongly influenced by temperature. Conventional battery thermal management systems frequently depend on fixed temperature thresholds, predefined cooling schedules, limited sensing locations, and reactive control mechanisms that may respond only after significant thermal imbalance has developed. This paper proposes an intelligent thermal monitoring and heat regulation framework for electric vehicle battery packs that integrates distributed thermal sensing, electrical operating data, multi-source data fusion, edgeassisted preprocessing, intelligent thermal state assessment, hotspot identification, predictive risk analytics, adaptive cooling coordination, digital twin-based monitoring, secure API integration, and governed model lifecycle management. The proposed methodology continuously acquires cell and module temperature, ambient temperature, current, voltage, state of charge, charging rate, coolant condition, and vehicle operating context. Sensor observations are validated, synchronized, and transformed into a spatially aware battery thermal representation. Machine learning models classify battery operation into normal, warming, elevated-risk, and critical thermal states while identifying abnormal rates of temperature rise and persistent cell-to-cell temperature differences. An adaptive regulation layer coordinates passive heat spreading, air cooling, liquid cooling, and available thermal control resources according to current and predicted conditions. The framework further maintains a digital representation of battery thermal history and supports secure communication among battery management, vehicle control, analytics, and cloud-edge services. Representative experimental analysis indicates that the proposed framework can reduce peak battery temperature, improve temperature uniformity, shorten hotspot duration, decrease auxiliary cooling energy demand, improve thermal risk classification, and strengthen fast-charging stability compared with conventional threshold-based thermal control. The proposed approach provides a scalable foundation for safe, energy-aware, predictive, and adaptive battery heat regulation in nextgeneration electric vehicles.