DIGITAL TWIN-BASED BATTERY TEMPERATURE PREDICTION FOR INTELLIGENT ELECTRIC VEHICLES

Abstract

The increasing adoption of electric vehicles (EVs) has created significant challenges in maintaining optimal battery operating conditions, particularly due to complex thermal behaviors during charging, discharging, and high-performance driving scenarios. Lithiumion battery temperature variations directly influence battery efficiency, degradation, safety, and overall vehicle performance. Conventional battery monitoring approaches mainly depend on sensor-based measurements and reactive thermal control methods, which provide limited prediction capability and delayed response to abnormal thermal conditions. This paper proposes a Digital TwinBased Battery Temperature Prediction Framework for Intelligent Electric Vehicles that integrates Digital Twin technology, realtime battery data synchronization, Machine Learning-based prediction models, thermal modeling, and intelligent battery management mechanisms. The proposed framework creates a dynamic virtual representation of the physical battery system by continuously analyzing temperature variations, charging patterns, environmental conditions, battery states, and operational parameters. Machine Learning algorithms predict future thermal behavior and identify potential overheating conditions before critical failures occur. Experimental analysis demonstrates that the proposed framework improves temperature prediction accuracy, enhances thermal management efficiency, reduces battery degradation risks, and supports proactive battery control compared with conventional monitoring approaches. The proposed framework provides an intelligent, scalable, and predictive solution for nextgeneration electric vehicle battery management systems. Keywords: Digital Twin, Electric Vehicle, Battery Temperature Prediction, Lithium-Ion Battery, Machine Learning, Thermal Management, Battery Management System, Predictive Analytics, Intelligent Vehicles, EV Technology.