A digital twin–enabled measurement and instrumentation framework for ultra-accurate short-term voltage prediction in lithium-ion batteries for electric vehicle applications

Accurate short-term voltage prediction is essential for safety, efficiency, and optimal power management in electric vehicle (EV) battery systems. However, traditional battery management systems (BMSs) suffer from modeling inaccuracies, sensor drift, low-bandwidth measurements, and limited adaptability to transient load conditions and ageing effects. This research proposes a novel Digital Twin (DT)–enabled measurement and instrumentation framework that integrates high-frequency sensing, adaptive sensor calibration, and hybrid physics–artificial intelligence (AI) modeling to achieve ultra-accurate lithium-ion battery voltage prediction. The proposed system incorporates a real-time DT combining an equivalent circuit model, electro-thermal ageing representation, and a deep learning prediction layer. A multisensor instrumentation platform—comprising high-resolution voltage and current measurement, temperature arrays, and low-amplitude impedance probing—continuously synchronizes the DT with physical battery behavior through advanced sensor-fusion algorithms. An AI-based short-term forecasting module (long short-term memory/temporal convolutional network/physics-informed neural network) predicts terminal voltage 50–500 ms ahead, enabling proactive power management under dynamic EV driving conditions. Experimental validation using real driving cycles demonstrates significant improvements in prediction accuracy, state of charge estimation, and robustness against sensor drift compared to conventional BMS approaches. This work provides a foundation for next-generation predictive BMS architectures, enhancing EV safety, performance, and battery longevity through intelligent DT–assisted instrumentation.