Adaptive Thermal Management of Electric Vehicle Batteries Using Data-Driven Control Models

Abstract

The performance, safety, and lifespan of lithiumion batteries used in electric vehicles are significantly influenced by operating temperature. Rapid charging, high-power acceleration, regenerative braking, and harsh environmental conditions generate excessive heat that can reduce battery efficiency and accelerate degradation if not effectively managed. Conventional battery thermal management systems rely on fixed control strategies that cannot adapt to dynamic operating conditions, resulting in inefficient cooling and unnecessary energy consumption. This paper proposes an Adaptive Thermal Management framework based on data-driven control models that integrates real-time sensor monitoring, predictive analytics, machine learning, and intelligent cooling optimization. The proposed system continuously analyzes battery operating conditions to dynamically regulate cooling mechanisms while maintaining battery temperature within the optimal operating range. Experimental evaluation demonstrates improvements in temperature regulation, thermal uniformity, battery safety, cooling efficiency, charging performance, and battery lifespan. The proposed adaptive control framework provides a scalable, energy-efficient, and intelligent thermal management solution for sustainable nextgeneration electric vehicle battery systems.