Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin

Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin

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Electric mobility has become an essential part of the future of transportation. Detection, diagnosis and prognosis of fault in electric drives are improving the reliability, of electric vehicles (EV). Permanent magnet synchronous motor (PMSM) drives are used in a large variety of applications due to their dynamic performances, higher power density and higher efficiency. In this study, health monitoring and prognosis of PMSM is developed by creating intelligent digital twin (i-DT) in MATLAB/Simulink. An artificial neural network (ANN) and fuzzy logic are used for mapping inputs distance, time of travel of EV and outputs casing temperature, winding temperature, time to refill the bearing lubricant, percentage deterioration of magnetic flux to compute remaining useful life (RUL) of permanent magnet (PM). Health monitoring and prognosis of EV motor using i-DT is developed with two approaches. Firstly, in-house health monitoring and prognosis is developed to monitor the performance of the motor in-house. Secondly, Remote Health Monitoring and Prognosis Centre (RHMPC) is developed to monitor the performance of the motor remotely using cloud communication by the service provider of the EV. The simulation results prove that the RUL of PM and time to refill the bearing lubricant obtained by i-DT twins theoretical results.


    1. 1)
      • 1. Loureiro, R., Benmoussa, S., Touati, Y., et al: ‘Integration of fault diagnosis and fault-tolerant control for health monitoring of a class of MIMO intelligent autonomous vehicles’, IEEE Trans. Veh. Technol., 2014, 63, (1), pp. 3039, doi: 10.1109/TVT.2013.2274289.
    2. 2)
      • 2. Gritli, Y., Rossi, C., Casadei, D., et al: ‘Demagnetizations diagnosis for permanent magnet synchronous motors based on advanced wavelet analysis’. Proc. 20th Int. Conf. Electr. Mach., Marseille, France, September 2012, pp. 23972403.
    3. 3)
      • 3. Weng, C., Sun, J., Peng, H.: ‘Model parametrization and adaptation based on the invariance of support vectors with applications to battery state-of-health monitoring’, IEEE Trans. Veh. Technol., 2015, 64, (9), pp. 39083917.
    4. 4)
      • 4. Loureiro, R., Merzouki, R., Bouamama, B.O.: ‘Bond graph model based on structural diagnosability and recoverability analysis: application to intelligent autonomous vehicles’, IEEE Trans. Veh. Technol., 2012, 61, (3), pp. 986997.
    5. 5)
      • 5. Djeziri, M., Merzouki, R., Ould-Bouamama, B.: ‘Robust monitoring of electric vehicle with structured and unstructured uncertainties’, IEEE Trans. Veh. Technol., 2009, 58, (9), pp. 47104719.
    6. 6)
      • 6. Khan, M.A.S.K., Rahman, M.A.: ‘Development and implementation of a novel fault diagnostic and protection technique for IPM motor drives’, IEEE Trans. Ind. Electron., 2009, 56, (1), pp. 8592.
    7. 7)
      • 7. Liu, X.-Q., Zhang, H.-Y., Liu, J., et al: ‘Fault detection and diagnosis of permanent-magnet DC motor based on parameter estimation and neural network’, IEEE Trans. Ind. Electron., 2000, 47, (5), pp. 10211030.
    8. 8)
      • 8. Nyanteh, Y., Edrington, C., Srivastava, S., et al: ‘Application of artificial intelligence to real-time fault detection in permanent-magnet synchronous machines’, IEEE Trans. Ind. Appl., 2013, 49, (3), pp. 12051214.
    9. 9)
      • 9. Hamidizadeh, S., Alatawneh, N., Chromik, R.R., et al: ‘Comparison of different demagnetization models of permanent magnet in machines for electric vehicle application’, IEEE Trans. Magn., 2016, 52, (5), pp. 14.
    10. 10)
      • 10. Al-Timimy, A., Al-Ani, M., Degano, M., et al: ‘Influence of rotor endcaps on the electromagnetic performance of high-speed PM machine’, IET Electr. Power Appl., 2018, 12, (8), pp. 11421149.
    11. 11)
      • 11. Zeng, C., Huang, S., Yang, Y., et al: ‘Inter-turn fault diagnosis of permanent magnet synchronous machine based on tooth magnetic flux analysis’, IET Electr. Power Appl., 2018, 12, (6), pp. 837844.
    12. 12)
      • 12. Wang, L., Aleksandrov, S., Tang, Y., et al: ‘Fault-tolerant electric drive and space-phasor modulation of flux-switching permanent magnet machine for aerospace application’, IET Electr. Power Appl., 2017, 11, (8), pp. 14161423.
    13. 13)
      • 13. Hou, Z., Huang, J., Liu, H., et al: ‘No-load losses based method to detect demagnetisation fault in permanent magnet synchronous motors with parallel branches’, IET Electr. Power Appl., 2017, 11, (3), pp. 471477.
    14. 14)
      • 14. Qazalbash, A.A., Sharkh, S.M., Irenji, N.T., et al: ‘Rotor eddy loss in high-speed permanent magnet synchronous generators’, IET Electr. Power Appl., 2015, 9, (5), pp. 370376.
    15. 15)
      • 15. Lee, K.-D., Kim, W.-H., Jin, C.-S., et al: ‘Local demagnetisation analysis of a permanent magnet motor’, IET Electr. Power Appl., 2015, 9, (3), pp. 280286.
    16. 16)
      • 16. Farasat, M., Trzynadlowski, A.M., Fadali, M.S.: ‘Efficiency improved sensorless control scheme for electric vehicle induction motors’, IET Electr. Syst. Transp., 2014, 4, (4), pp. 122131.
    17. 17)
      • 17. Lee, J., Jeon, Y.J., Choi, D.-C., et al: ‘Demagnetization fault diagnosis method for PMSM of electric vehicle’. Proc. 39th Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Vienna, Austria, November 2013, pp. 27092713.
    18. 18)
      • 18. Fernandez, D., Hyun, D., >Park, Y., et al: ‘Permanent magnet temperature estimation in PM synchronous motors using Low-cost hall effect sensors’, IEEE Trans. Ind. Appl., 2017, 53, (5), pp. 45154525.
    19. 19)
      • 19. Sarikhani, A., Mohammed, O.A.: ‘Inter-turn fault detection in PM synchronous machines by physics-based back electromotive force estimation’, IEEE Trans. Ind. Electron., 2013, 60, (8), pp. 34723484.
    20. 20)
      • 20. Wang, C., Prieto, M.D., Romeral, L., et al: ‘Detection of partial demagnetization fault in PMSMs operating under nonstationary conditions’, IEEE Trans. Magn., 2016, 52, (7), pp. 14, Art. no. 8105804.
    21. 21)
      • 21. Milanfar, P., Lang, J.H.: ‘Monitoring the thermal condition of permanent-magnet synchronous motors’, IEEE Trans. Aerosp. Electron. Syst., 1996, 32, (4), pp. 14211429.
    22. 22)
      • 22. Antunes Cezario, C., Silva, H.P.: ‘Electric motor winding temperature prediction using a simple two-resistance thermal circuit’, COMPEL-Int. J. Comput. Math. Electri. Electron.Eng., 2010, 29, (5), pp. 13251330.
    23. 23)
      • 23. Guide to Electric Motor Bearing Lubrication, ExxonMobil Lubricants and specialities, February 2002.
    24. 24)
      • 24. Ding, X., Liu, J., Mi, C.: ‘Online temperature estimation of IPMSM permanent magnets in hybrid electric vehicles’. 2011 6th IEEE Conf. on Industrial Electronics and Applications (ICIEA), Beijing, China, 2011, pp. 179183.
    25. 25)
      • 25. Manickavasagam, K.: ‘Intelligent energy control center for distributed generators using multi-agent system’, IEEE Trans. Power Syst., 2015, 30, (5), pp. 24422449.
    26. 26)
      • 26. Morinigo-Sotelo, D., Duque-Perez, O., Perez-Alonso, M.: ‘Assessment of the lubrication condition of a bearing using spectral analysis of the stator current’. 2010 Int. Symp. on Power Electronics Electrical Drives Automation and Motion (SPEEDAM), Naples, Italy, 2010, pp. 11601165.
    27. 27)
      • 27. TDC7200 Time-to-Digital Converter for Time-of-Flight Applications in. Available at
    28. 28)
      • 28. Armstrong, R.W.Jr: ‘Load to motor inertia mismatch: unveiling the truth’, Drives and Controls Conference, Telford, England, 1998. Available at
    29. 29)
      • 29. A guide to preventing failure Motors don't just they?. Available at ABB.pdf.

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