Digital Twin-Based Temperature Prediction and Thermal Control for Lithium-Ion Battery Systems

Abstract

Lithium-ion batteries are the primary energy storage technology for modern electric vehicles due to their high energy density, long cycle life, and superior charging efficiency. However, excessive heat generation during charging, discharging, and high-power operation significantly affects battery safety, performance, and longevity. Digital Twin technology has emerged as a powerful solution for creating realtime virtual representations of physical battery systems, enabling continuous monitoring, predictive temperature estimation, and intelligent thermal control. This paper presents a Digital Twin-Based Temperature Prediction and Thermal Control framework that integrates sensor networks, machine learning, cloud computing, and real-time battery simulation for intelligent thermal management. The proposed framework continuously synchronizes physical battery conditions with virtual battery models to predict thermal behavior and optimize cooling strategies before unsafe conditions occur. Experimental evaluation demonstrates improvements in temperature prediction accuracy, thermal stability, battery safety, charging efficiency, cooling performance, and battery lifespan. The proposed intelligent framework provides a scalable and reliable solution for next-generation electric vehicle battery thermal management.