Citation :

Darjon Dhamo, Aida Spahiu, Denis Panxhi, Nuri Rusta, "Mechanical Parameter Estimation of an Induction Motor for a Digital Twin Using Coast-Down Data," International Journal of Electrical and Electronics Engineering, vol. 13, no. 8, pp. 22-34, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I8P103

Abstract

Accurate mechanical parameters are essential for developing Digital Twin (DT) models of Electric Drives (ED) that are both physically consistent and predictive across operating conditions. This paper presents an offline workflow for the mechanical parameter estimation and model validation of an Induction Motor (IM) drive for DT development. The experiments were performed on a laboratory setup in which an induction motor, supplied by a power converter, was mechanically coupled to an alternator through a belt–pulley transmission. Before parameter estimation, the measured voltage, current, and rotor speed signals are preprocessed, after which the coast-down interval is extracted from the full operating record for mechanical parameter identification. The mechanical parameters are then estimated through nonlinear least-squares fitting of the measured deceleration trajectories. The proposed procedure identifies the parameters that characterize the inertial and frictional behavior of the drive, enabling an accurate representation of the mechanical subsystem. A MATLAB/Simulink mechanical model is subsequently validated over separate time intervals and under operating conditions different from those used for estimation, so that any observed deviations can be attributed to model mismatch rather than to variations in parameter values with the operating point. Validation results show very good agreement between measured and simulated speed responses across multiple frequencies, with normalized root mean square error values of approximately (1.39−3.80%) and (𝑅2≥ 0.9964). These results demonstrate that the proposed workflow provides reliable and predictive mechanical parameters, forming a solid basis for future online adaptation and condition monitoring of induction motor drives.

Keywords

Coast-down experiments, Digital twins, Induction motors, Mechanical parameter estimation, Nonlinear least-squares estimation.

References

1. Kaiyu Wang et al., “A Nonlinear Least-Squares Approach for Identification of the Induction Motor Parameters,” IEEE Transactions on Automatic Control, vol. 50, no. 10, pp. 1622-1628, 2005.
   [CrossRef] [Google Scholar] [Publisher Link]
2. D. Lindenmeyer et al., “An Induction Motor Parameter Estimation Method,” International Journal of Electrical Power and Energy Systems, vol. 23, no. 4, pp. 251-262, 2001.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Mohsen Soori, Behrooz Arezoo, and Roza Dastres, “Digital Twin for Smart Manufacturing: A Review,” Sustainable Manufacturing and Service Economics, vol. 2, pp. 1-15, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Mohsen Ebadpour et al., “Digital Twin Model of Electric Drives Empowered by EKF,” Sensors, vol. 23, no. 4, pp. 1-21, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Jhennifer F. Dos Santos et al., “Digital Twin-based Monitoring System of Induction Motors using IoT Sensors and Thermo-Magnetic Finite Element Analysis,” IEEE Access, vol. 11, pp. 1682-1693, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
6.  Weiming Yin et al., “Digital Twin-Driven Condition Monitoring System for Traditional Complex Machinery in Service,” Machines, vol. 13, no. 6, pp. 1-21, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Fei Tao et al., “Digital Twin-Driven Product Design, Manufacturing and Service with Big Data,” International Journal of Advanced Manufacturing Technology, vol. 94, no. 9, pp. 3563-3576, 2017.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Yuqian Lu et al., “Digital Twin-Driven Smart Manufacturing: Connotation, Reference Model, Applications and Research Issues,” Robotics and Computer-Integrated Manufacturing, vol. 61, pp. 1-14, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Ke Zhang et al., “Digital Twin-Enabled Predictive Thermal Modeling for Stator Temperature Monitoring in Induction Motors,” Electronics, vol. 14, no. 14, pp. 1-19, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Michael Grieves, and John Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” Transdisciplinary Perspectives on Complex Systems, pp. 85-113, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Guillaume Pronost et al., “Digital Twins along the Product Lifecycle: A Systematic Literature Review of Applications in Manufacturing,” Digital Twin, vol. 1, no. 2, pp. 1-31, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Diptarshi Bhowmick, and Suparna Kar Chowdhury, “Estimation of Induction Motor Equivalent Circuit Parameters and Losses from Transient Measurement,” ISA Transactions, vol. 157, pp. 573-590, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Fatma Zohra Aboubi et al., “Experimental Identification of a Coupled-Circuit Model for the Digital Twin of a Wound-Rotor Induction Machine,” Energies, vol. 17, no. 8, pp. 1-25, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Zhiwei Gao, Anand Paul, and Xiaokang Wang, “Guest Editorial: Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation,” IEEE Transactions on Industrial Informatics, vol. 18, no. 2, pp. 1355-1358, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Tomas Kostal, and Pavel Kobrle, “Induction Machine On-Line Parameter Identification for Resource-Constrained Microcontrollers based on Steady-State Voltage Model,” Electronics, vol. 10, no. 16, pp. 1-19, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Azad M. Madni, Carla C. Madni, and Scott D. Lucero, “Leveraging Digital Twin Technology in Model-based Systems Engineering,” Systems, vol. 7, no. 1, pp. 1-13, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
17. V.P. Sakthivel, R. Bhuvaneswari, and S. Subramanian, “Multi-Objective Parameter Estimation of Induction Motor using Particle Swarm Optimization,” Engineering Applications of Artificial Intelligence, vol. 23, no. 3, pp. 302-312, 2010.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Chiara Cimino, Elisa Negri, and Luca Fumagalli, “Review of Digital Twin Applications in Manufacturing,” Computers in Industry, vol. 113, pp. 1-15, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Gusztáv Áron Sziki, Attila Szántó, and Éva Ádámkó, “Review of Methods for Determining the Moment of Inertia and Friction Torque of Electric Motors,” Acta Polytechnica Hungarica, vol. 21, no. 4, pp. 203-218, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Adamou Amadou Adamou et al., “Squirrel Cage Induction Motors Accurate Modelling for Digital Twin Applications,” Modelling, vol. 5, no. 4, pp. 1582-1600, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
21. R. Raja Singh et al., “Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines,” Machines, vol. 11, no. 8, pp. 1-20, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
22. V.S.S. Pavan Kumar Hari, Avanish Tripathi, and G. Narayanan, “Experimental Determination of Mechanical Parameters in Sensorless Vector-Controlled Induction Motor Drive,” Sādhanā, vol. 42, no. 8, pp. 1285-1297, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Attila Szántó et al., “Simultaneous Measurement of the Moment of Inertia and Braking Torque of Electric Motors Applying Additional Inertia,” Measurement, vol. 204, pp. 1-7, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
24. J.O. Ojo, A. Consoli, and T.A. Lipo, “An Improved Model of Saturated Induction Machines,” IEEE Transactions on Industry Applications, vol. 26, no. 2, pp. 212-221, 1990.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Sergey Edward Lyshevski, Electromechanical Systems, Electric Machines, and Applied Mechatronics, 1^st ed., Boca Raton, CRC Press, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Prabha S. Kundur, Power System Stability and Control, 3^rd ed., Boca Raton, CRC Press, 2016.
    [Google Scholar]