DATA-DRIVEN BATTERY TEMPERATURE PREDICTION FOR SAFE AND EFFICIENT ELECTRIC MOBILITY SYSTEMS

Abstract

The rapid adoption of electric mobility has increased the importance of reliable battery thermal monitoring and predictive temperature management because battery temperature directly influences safety, charging performance, energy efficiency, degradation rate, driving range, and operational life. Lithium-ion battery packs operate under highly dynamic conditions involving rapid acceleration, regenerative braking, high-rate charging, variable ambient temperature, traffic congestion, changing state of charge, cell imbalance, and repeated electrochemical cycling. Conventional battery thermal management systems generally depend on measured temperature values, fixed protection thresholds, predefined cooling strategies, and reactive control mechanisms. Such approaches may identify thermal abnormalities only after significant temperature increase has already occurred and may not adequately anticipate future thermal behavior under changing vehicle loads. This paper proposes a data-driven battery temperature prediction framework for safe and efficient electric mobility systems. The proposed methodology integrates distributed battery sensing, Internet of Things connectivity, edge preprocessing, historical thermal data management, multi-sensor data fusion, contextual feature engineering, machinelearning-based temperature forecasting, Digital Twin-assisted battery representation, predictive thermal risk classification, adaptive cooling decision support, secure API interoperability, MLOps lifecycle management, and sustainability-aware cloud processing. Real-time observations involving cell voltage, pack voltage, charging and discharging current, battery temperature, ambient temperature, state of charge, estimated state of health, vehicle speed, acceleration behavior, charging mode, cooling activity, energy consumption, and historical thermal exposure are continuously collected and analyzed. The framework predicts near-future battery temperature trends and identifies emerging overheating risk before critical thresholds are reached. Edge intelligence enables rapid processing of safety-relevant observations, while centralized analytics supports long-term model training and fleet-level battery intelligence. A representative evaluation demonstrates improvements in temperature prediction accuracy, thermal anomaly anticipation, early warning time, false alarm reduction, excessive heat exposure, cooling efficiency, energy utilization, battery availability, and predictive maintenance effectiveness compared with conventional threshold-based and reactive thermal monitoring approaches. The results indicate that data-driven temperature prediction can transform battery thermal management from a reactive protection function into a proactive intelligence mechanism supporting safer, more efficient, and more sustainable electric mobility.