Digital shadow of an electric vehicle-permanent magnet synchronous motor drive for real-time performance monitoring  

作　　者：Mahmoud Ibrahim Viktor Rjabtšikov Anton Rassõlkin 

机构地区：[1]Department of Electrical Power Engineering and Mechatronics,Tallinn University of Technology,Tallinn,Estonia

出　　处：《Digital Twins and Applications》2025年第1期1-12,共12页数字孪生及应用(英文)

基　　金：Estonian Research Competency Council,Grant/Award Number:PSG453,Eesti Teadusagentuur。

摘　　要：Digital twin(DT)technology has been utilised in many applications including electric vehicles(EVs).A DT is a virtual representation of a physical object,enabled through real-time data integration,simulation,and optimisation tools.Unlike conventional simulations,which are typically offline and lack real-time interaction,a DT continuously synchronises with the physical system,enabling dynamic performance monitoring and predictive an-alytics.Achieving a full DT involves progressive stages,with the digital shadow(DS)being the final step before realising a bidirectional DT.Building a DS provides a scalable real-time performance monitoring and fault detection framework,enabling proactive decision-making in EV operations.This study introduces a DS system specifically designed to monitor the performance of a permanent magnet synchronous motor(PMSM)drive system in EVs,marking a critical phase towards a complete DT.The methodology for creating the DS is detailed,including the establishment of a compre-hensive test bench for the EV powertrain as the physical reference model.The mathe-matical model of the EV-PMSM was formulated,and an advanced estimation model utilising the extended Kalman filter(EKF)was implemented.MATLAB/Simulink was employed to develop the motor’s digital model.Real-time data acquisition from the physical model was facilitated through a data acquisition system(DAS)equipped with a controller area network(CAN)communication interface.The digital model underwent thorough validation against sensory data collected from the test bench.The motor digital model was deployed to a DS framework enabled through real-time data flow from the actual EV during real-world driving conditions.The results demonstrated a high accuracy of 97%between the DS predictions and the corresponding EV data,confirming the DS’s reliability.These findings pave the way for future advancements,including bidirectional interaction and the realisation of a full DT.

关 键 词：digital shadow electrical engineering MODELLING MONITORING 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]