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Dynamic performance improvement of PMSM drive using fuzzy-based adaptive control strategy for EV applications

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Abstract

The permanent magnet synchronous motor (PMSM) is the heart of the electric drive system in electric vehicle technology. The effects of load variation and motor parameter changes are the important key challenges, which deteriorate the dynamic performances of interior PMSM (IPMSM) drives. To overcome these issues, this study suggests the development of an efficient new control drive system by integrating the Model Reference Adaptive Control (MRAC) with a fuzzy logic controller (FLC) using a finite-element model optimized motor model. The proposed cascaded system comprises two loops: a main outer loop that runs MRAC to mitigate the effects of load variation, and a secondary inner loop with FLC for resilient performance against parametric fluctuations of the IPMSM drive system. The proposed controller uses the hybrid space vector pulse width modulation technique to regulate the switching components of the inverter. It also reduces total harmonic distortion (THD) and torque ripple during the startup of the motor. The overall examination of the PMSM drive system is accomplished by co-simulation using MATLAB and Simcenter MAGNET software. The simulated results demonstrate the superiority of the proposed fuzzy adaptive controller in terms of higher maximum torque and improved speed tracking accuracy. A prototype of the proposed PMSM is developed and validated by experiment, which shows the robustness of the proposed methodology against load and speed fluctuations by reducing THD and torque ripples.

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References

  1. Ogbuka, C., Nwosu, M., Agu, M.: Dynamic and steady state performance comparison of line-start permanent magnet synchronous motors with interior and surface rotor magnets. Arch. Electr. Eng. 65(1), 105–116 (2016)

    Article  Google Scholar 

  2. Kanuch, J., Girovsky, P.: Analysis of the PM motor with external rotor for direct drive of electric wheelchair. Commun. Sci. Lett. Univ. Zilina Komunikacie 21(1), 66–71 (2019)

    Google Scholar 

  3. Li, G.J., Ren, B., and Zhu, Z.Q.: Cogging torque and torque ripple reduction of modular permanent magnet machines. International Conference on Electrical Machines (ICEM), IEEE, Lausanne, pp. 193–199 (2016)

  4. Mustafa, Y., Bdewi, A., Mohammed, M., Ezzaldean, M.M.: Design and performance analysis of permanent magnet synchronous motor for electric vehicles application. Eng. Technol. J. 39(3A), 394–406 (2021)

    Article  Google Scholar 

  5. Wu, Z., Zuo, S., and Chen, S.: Accurate Modeling of PMSM Considering Orthotropic Material Parameters of Stator System for Vibro-acoustic Prediction. SAE Technical Paper 2022-01-0725, (2022).

  6. Liu G., Dong. B., Chen L., Zhao W.: A new model reference adaptive control of PMSM using neural network generalized inverse. Proceedings of the 8th international conference on Advances in neural networks, Vol. 3, pp. 58–67. Springer, Switcherland (2011)

  7. Qu, Z., Ye, Z.: Speed regulation of a permanent magnet synchronous motor via model reference adaptive control. Adv. Mater. Res. 268–270, 513–516 (2011)

    Article  Google Scholar 

  8. Sharaf, B.S.M., Hogg, B.W., Abdalla, O.H., El-Sayed, M.L.: Multi-variable adaptive controller for a turbogenerator. Proc. IEEE 133(2), 83–89 (1986)

    Article  MATH  Google Scholar 

  9. Sharaf, B.S.M., Hogg, B.W., Abdalla, O.H.: Real-time adaptive controllers for a turbine generator. Int. J. Control 50(2), 603–626 (1989)

    Article  MATH  Google Scholar 

  10. Uma Maheswara Rao, C., Krishna, B.M., Soundarya, A.L., Kumari, N.K.: Field oriented control of PMSM with model reference adaptive control using Fuzzy-PI controller. Int. J. Circuit Theor. Appl. 8(1), 96–108 (2015)

    Google Scholar 

  11. SaiKumar, P., SivaKumar, J.S.V.: Model reference adaptive controlled application to the vector controlled permanent magnet synchronous motor drive. Int. J. Power Syst. Oper. Energy Manag. 1(1), 10 (2012)

    Google Scholar 

  12. Li, X., Li, S.: Speed control for a PMSM servo system using model reference adaptive control and an extended state observer. J. Power Electron. 14(3), 549–563. (2014)

    Article  Google Scholar 

  13. Huang, Y., Xu, Y., Zhang, W., Zou, J.: Hybrid RPWM technique based on modified SVPWM to reduce the PWM acoustic noise. IEEE Trans. Power Electron. 99, 1–9 (2018)

    Google Scholar 

  14. Bhattacharya S., Mascarella D., Joos G., Moschopoulos G., Reduced Switching Random PWM Technique for Two-Level Inverters. 2015 IEEE Energy Conversion Congress and Exposition (ECCE). pp. 695–702. IEEE, Montreal, QC, Canada (2015).

  15. Muthukumar, P., Melba Mary, P., Jeevananthan, S.: An improved hybrid space vector PWM technique for IM drives. Circuits Syst. 7, 2120–2131 (2016)

    Article  Google Scholar 

  16. Du, R., Liu, Q., Bu, F., Qin, H.: Hybrid Random SVPWM Strategy for High Order Harmonics Suppression of Permanent Magnet Synchronous Motor Servo Drive System. 24th International Conference on Electrical Machines and Systems (ICEMS). pp. 1928–1932. IEEE, Gyeongju, Korea, Republic of Korea (2021)

  17. Boopathi, R., Jayanthi, R., Ansari, M.M.T.: Power quality improvement in wind energy conversion system using hybrid SVPWM inverter control technique for THD reduction. Int. J. Dyn. Control 8, 592–603 (2020)

    Article  Google Scholar 

  18. Fitouri, M., Bensalem, Y., Abdelkrim, M.N.: Modeling and detection of the short-circuit fault in PMSM using finite element analysis. IFAC Pap. OnLine 49(12), 1418–1423 (2016)

    Google Scholar 

  19. You, Y.-M.: Optimal design of PMSM based on automated finite element analysis and metamodeling. Energies 12(24), 4673 (2020)

    Article  Google Scholar 

  20. Oksuztepe, E., Omac, Z., Polat, M., Celik, H., Selcuk, A.H., Kurum, H.: Sensorless field oriented control of non-sinusoidal flux-distribution permanent magnet synchronous motor with a FEM based ANN observer. Turk. J. Electr. Eng. Comput. Sci. 24(4), 67 (2016)

    Google Scholar 

  21. Suryakant, M.S., Singh, M.: Improved ANFIS based MRAC observer for sensor-less control of PMSM. J. Intell. Fuzzy Syst. 42, 1061–1073 (2022)

    Article  Google Scholar 

  22. Mersha, T.K., Du, C.: Co-simulation and modeling of PMSM based on ansys software and simulink for EVs. World Electr. Veh. J. 4, 1–13 (2022)

    Google Scholar 

  23. Sheela, A., Atshaya, M., Revathi, S., Jeyapaul Singh, N.: Investigation on PMSM for electric vehicle applications using co-simulation of MATLAB and magnet software. IOP Conf. Ser. 1055, 012138 (2021)

    Article  Google Scholar 

  24. Iqbal, A., Abu-Rub, H., Nounou, H.: Adaptive fuzzy logic controlled surface mount permanent magnet synchronous motor drive. Syst. Sci. Control Eng. 2(1), 465–475 (2014)

    Article  Google Scholar 

  25. Qiu, G., Jiang, K., Shengyou, Xu., Yang, X., Wang, W.: Modeling and analysis of the characteristics of SiC MOSFET. IOP Publ. J. Phys. 2125, 1–7 (2021)

    Google Scholar 

  26. Ding, X., Min, D., Guo, T.Z.H., Zhang, C.: Comprehensive comparison between silicon carbide MOSFETs and silicon IGBTs based traction systems for electric vehicles. Appl. Energy (2016). https://doi.org/10.1016/j.apenergy.2016.05.059

    Article  Google Scholar 

  27. Ding, X., Chen, F., Min, D., Guo, H., Ren, S.: Effects of silicon carbide MOSFETs on the efficiency and power quality of a micro-grid connected inverter. Appl. Energy 201, 270–283 (2016)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Department of Science & Technology, Government of India for funding the Research Infrastructure under the Scheme entitled “Funds for the Improvement of S&T Infrastructure (DST-FIST)” with Ref. No. SR/FST/College–110/2017.

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  1. Department of Electrical and Electronics Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India

    S. Suganthi & R. Karpagam

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  1. S. Suganthi
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Correspondence to R. Karpagam.

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Suganthi, S., Karpagam, R. Dynamic performance improvement of PMSM drive using fuzzy-based adaptive control strategy for EV applications. J. Power Electron. 23, 510–521 (2023). https://doi.org/10.1007/s43236-023-00594-3

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