Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements to track key performance parameters such as vehicle speed,state of charge(SoC),battery temperature,power consumption,motor RPM,and...Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements 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,andmeasurement 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^(2)).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^(2) 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.展开更多
文摘Electric vehicle(EV)monitoring systems commonly depend on IoT-based sensormeasurements 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,andmeasurement 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^(2)).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^(2) 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.
