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

Abstract 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 analytics. 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 comprehensive test bench for the EV powertrain as the physical reference model. The mathematical 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.

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Publication Details

Journal
Digital twins and applications.
Published
2025-01-01
DOI
https://doi.org/10.1049/dgt2.12024
Citations
2
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
1.46

Funders

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article

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

Mahmoud Ibrahim, Viktor Rjabtšikov, Anton Rassõlkin
2 citations
Digital twins and applications.
Sensorless Control of Electric Motors
1.46
article

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

Mahmoud Ibrahim, Viktor Rjabtšikov, Anton Rassõlkin
article en
2 citations

Abstract

Abstract 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 analytics. 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 comprehensive test bench for the EV powertrain as the physical reference model. The mathematical 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.

Digital twins and applications.Vol. 2(1)
Tallinn University of Technology (EE)
Eesti Teadusagentuur, Estonian Research Competency Council
Openalex Percentile: Top 20%
Sensorless Control of Electric Motors
1.46
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