TY  - EJOU
AU  - Kumar, J. S. V. Siva 
AU  - Mustafa, Mahmad 
AU  - Begum, Sk. M. Unnisha 
AU  - Suresh, Badugu 
AU  - Narasipuram, Rajanand Patnaik 

TI  - A Digital Twin Driven IoT Architecture for Enhanced xEV Performance Monitoring
T2  - Energy Engineering

PY  - 2025
VL  - 122
IS  - 10
SN  - 1546-0118

AB  - Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of EV dynamics, enabling enhanced monitoring accuracy. Six critical parameters are evaluated using root mean square error (RMSE), mean absolute error (MAE), maximum deviation, and correlation coefficient (R<sup>2</sup>). Results show that the digital twin significantly improves estimation fidelity, with RMSE for speed reduced from 2.5 km/h (IoT) to 1.2 km/h and R<sup>2</sup> values generally exceeding 0.99, except for regenerative braking which achieved 0.982. These findings demonstrate the framework’s effectiveness in improving operational safety, energy management, and system reliability, offering a robust foundation for future advancements in adaptive calibration, predictive analytics, and fault detection in EV systems.
KW  - Digital twin (DT); electric vehicle (EV); IoT; state of charge (SoC); predictive analytics; RMSE; real-time estimation; sensor validation

DO  - 10.32604/ee.2025.070052