Real-Time Battery Temperature Forecasting Through Digital Twin and Sensor Fusion Technologies

Abstract

Accurate battery temperature prediction is essential for ensuring the safety, efficiency, and longevity of lithium-ion battery systems used in electric vehicles. Conventional battery thermal management systems primarily rely on real-time temperature measurements, offering limited capability to predict future thermal behavior under dynamic operating conditions. Digital Twin technology combined with sensor fusion provides a comprehensive framework for continuously synchronizing physical battery systems with virtual models, enabling predictive thermal analysis and intelligent decision-making. This paper proposes a Real-Time Battery Temperature Forecasting framework integrating Digital Twin technology, multi-sensor data fusion, machine learning, cloud computing, and predictive analytics for intelligent battery thermal management. The proposed architecture continuously estimates future battery temperatures by combining information from multiple sensors with virtual battery simulations. Experimental evaluation demonstrates improvements in prediction accuracy, thermal stability, battery safety, charging efficiency, fault detection capability, and battery lifespan. The proposed framework offers a scalable, adaptive, and intelligent solution for next-generation electric vehicle battery monitoring and thermal management systems.