MACHINE LEARNING-ASSISTED BATTERY THERMAL MANAGEMENT FOR SAFE ELECTRIC VEHICLE OPERATION

Abstract

The rapid adoption of electric vehicles (EVs) has increased the demand for intelligent battery thermal management systems that ensure operational safety, maximize energy efficiency, and extend battery lifespan. Lithium-ion batteries generate considerable heat during charging, discharging, and high-power operation, making temperature regulation essential for preventing thermal runaway and performance degradation. Traditional battery thermal management systems rely on fixed control strategies that often fail to adapt to dynamic operating conditions. This paper proposes a Machine Learning-Assisted Battery Thermal Management (ML-BTM) framework that integrates real-time sensor monitoring, predictive thermal analytics, intelligent cooling control, and cloud-enabled battery health monitoring. The proposed system utilizes historical battery operating data, environmental conditions, and charging characteristics to predict future thermal behavior and optimize cooling decisions. Experimental evaluation demonstrates improvements in temperature regulation, prediction accuracy, battery safety, charging efficiency, thermal uniformity, and battery lifespan. The proposed intelligent framework offers a scalable, adaptive, and energy-efficient solution for next-generation electric vehicle battery management systems. Keywords— Battery Thermal Management, Machine Learning, Electric Vehicles, LithiumIon Battery, Predictive Analytics, Thermal Safety, Battery Health Monitoring, Intelligent Control.