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Research Article |

Inductance Estimation of PMSM Using Extended Kalman Filter

Author(s): Komal Naikawadi1*, Sudhir Madhav Patil2, Kapil Kalantri3, Maneetkumar R. Dhanvijay4 and Atul V. Karanjkar5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 1

Publisher : FOREX Publication

Published : 15 March 2023

e-ISSN : 2347-470X

Page(s) : 138-142

DOI: https://doi.org/10.37391/IJEER.110119

Abstract

Estimation of parameters of Permanent Magnet Synchronous Motor (PMSM) plays an important role for motor controller tuning in Electric Vehicle (EV) application. Under running condition motor parameters vary due to different effects such as temperature, saturation and Voltage Source Inverter (VSI) non-linearities. Identification of parameters in running condition increases the control performance of system. This paper uses Extended Kalman Filter (EKF), which allows estimation of d-q axis inductances of PMSM. The control algorithm considered here is Field Oriented Control (FOC) for EV system having position sensor. The simulation is performed using MATLAB- Simulink software. The simulation results show that EKF identifies the d- and q- axis inductances Ld, Lq considering measurement and process noise using a state space model of motor equations for implementation.

Keywords: Extended Kalman Filter, MATLAB-Simulink, PMSM, Parameter Estimation, Simulation




Komal Naikawadi, PG Student (M.Tech. Mechatronics), Department of Manufacturing Engineering and Industrial Management, College of Engineering Pune (COEP), Shivaji Nagar, Pune: 411005, Maharashtra State, INDIA; Email: naikawadikm20.mfg@coep.ac.in

Sudhir Madhav Patil*, Associate Professor, Department of Manufacturing Engineering and Industrial Management, College of Engineering Pune (COEP), Shivaji Nagar, Pune: 411005, Maharashtra State, INDIA; Email: smp.prod@coep.ac.in

Kapil Kalantri, Head, Design and Development, HELLA eMobionics, Hella India Automotive Pvt. Ltd., Baner, Pune: 411045, Maharashtra State, INDIA; Email: kapil.kalantri@forvia.com

Maneetkumar R. Dhanvijay, Associate Professor, Department of Manufacturing Engineering and Industrial Management, College of Engineering Pune (COEP), Shivaji Nagar, Pune: 411005, Maharashtra State, INDIA; Email: mrd.mfg@coep.ac.in

Atul V. Karanjkar, Associate Professor, Department of Mechanical & Mechatronics Engineering (Additive Manufacturing), ZSCT's Thakur College of Engineering and Technology, Kandivali (E), Mumbai: 400101, Maharashtra State, INDIA; Email: avk.mechanical@gmail.com

    [1] Shahapure S. B., Kulkarni (Deodhar) V. A., and Shinde S. M. A technology review of energy storage systems, battery charging methods and market analysis of EV based on electric drives. International Journal of Electrical and Electronics Research (IJEER). 10(1), (March 2022), 23-35. https://doi.org/10.37391/IJEER.100104. [Cross Ref]
    [2] Liu H., Gao Z., Wu W., Chow L. and Wu T. 2015. Design of a high-efficiency permanent magnet synchronous motor and inverter system. In Proceedings of the 41st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015), 004996-005001, IEEE. https://doi.org/10.1109/IECON.2015.7392884. [Cross Ref]
    [3] Lee, C-H., Shin, H-S., and Kim, K-C. Analysis of interior permanent magnet synchronous motor according to winding method. International Journal of Electrical and Electronics Research (IJEER). 10(2), (June 2022), 207-213. https://doi.org/10.37391/IJEER.100227. [Cross Ref]
    [4] Kulkarni, S., and Thosar, A. Performance analysis of fault tolerant operation of PMSM using direct torque control and fuzzy logic control. International Journal of Electrical and Electronics Research (IJEER).10(2), (June 2022), 297-307. https://doi.org/10.37391/ijeer.100240. [Cross Ref]
    [5] Park, S. H., Lee, E. C., Park, J. C., Hwang, S. W., and Lim, M. S. Prediction of mechanical loss for high-power-density PMSM considering eddy current loss of PMs and conductors. IEEE Transactions on Magnetics. 57(2), Art No. 6300205, (Feb. 2021), 1-5. https://doi.org/10.1109/TMAG.2020.3007439. [Cross Ref]
    [6] Jeong, C-L., Kim, Y-K., and Hur, J. Optimized design of PMSM with hybrid-type permanent magnet for improving performance and reliability. IEEE Transactions on Industry Applications. 55(5), (2019), 4692-4701. https://doi.org/10.1109/TIA.2019.2924614. [Cross Ref]
    [7] Fang, S., Liu, H., Wang, H., Yang, H., and Lin H. High power density PMSM with lightweight structure and high-performance soft magnetic alloy core. IEEE Transactions on Applied Superconductivity. 29(2), Art no. 0602805, (March 2019), 1-5. https://doi.org/10.1109/TASC.2019.2891630. [Cross Ref]
    [8] Liu, H., Zhang, C. and Huang, Q. 2021. Model reference adaptive parameter identification of PMSM based on single-loop model predictive control. In Proceedings of the 40th Chinese Control Conference (CCC), 1365-1372, IEEE. https://doi.org/10.23919/CCC52363.2021.9549990. [Cross Ref]
    [9] Liu, Z., Feng, G., and Han, Y. 2021. Extended-kalman-filter-based magnet flux linkage and inductance estimation for PMSM considering magnetic saturation. In Proceedings of the 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 430-435, IEEE. https://doi.org/10.1109/YAC53711.2021.9486499. [Cross Ref]
    [10] Zwerger, T. and Mercorelli P. 2019. Combining SMC and MTPA using an EKF to estimate parameters and states of an interior PMSM. In Proceedings of the 20th International Carpathian Control Conference (ICCC), 1-6, IEEE. https://doi.org/10.1109/CarpathianCC.2019.876606. [Cross Ref]
    [11] IEEE guide for test procedures for synchronous machines part I-acceptance and performance testing part II-test procedures and parameter determination for dynamic analysis: in IEEE Std 115-2009 (Revision of IEEE Std 115-1995), (May 2010), 1-219. https://doi.org/10.1109/IEEESTD.2010.5464495. [Cross Ref]
    [12] Jiang, J. W., Chen, Z., Wang, Y. H., Peng, T., Zhu, S. L., and Shi L. M. Parameter estimation for PMSM based on a back propagation neural network optimized by chaotic artificial fish swarm algorithm. International Journal of Computers Communications and Control. 14(6), (December 2019), 615-632. Available at https://univagora.ro/jour/index.php/ijccc/article/view/3705/pdf. [Cross Ref]
    [13] Naikawadi, K. M., Patil, S. M., Kalantri, K., and Dhanvijay M. R. Comparative analysis of features of online numerical methods used for parameter estimation of PMSM. International Journal of Power Electronics and Drive Systems (IJPEDS). 13(4), (December 2022), 2172-2180. http://doi.org/10.11591/ijpeds.v13.i4.pp2172-2180. [Cross Ref]
    [14] Rafaq, M. S., and Jung, J-W. A comprehensive review of state-of-the-art parameter estimation techniques for permanent magnet synchronous motors in wide speed range. IEEE Transactions on Industrial Informatics. 16(7), (July 2020), 4747-4758. https://doi.org/10.1109/TII.2019.2944413. [Cross Ref]
    [15] Cui, M., Khodayar, M., Chen, C., Wang, X., Zhang, Y., Khodayar, M. E. Deep learning-based time-varying parameter identification for system-wide load modeling. IEEE Transactions on Smart Grid. 10(6), (Nov. 2019), 6102-6114. https://doi.org/10.1109/TSG.2019.2896493. [Cross Ref]
    [16] Ye, Z., Liu, T., Fuller, M., and Griepentrog, G. Parameter identification of PMSM based on MRAS with considering nonlinearity of inverter. In Proceedings of the 45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), 1255-1260, IEEE. https://doi.org/10.1109/IECON.2019.8927595. [Cross Ref]
    [17] Liu, Z-H., Nie, J., Wei, H-L., Chen, L., Li, X-H., and Lv, M-Y. Switched PI control based MRAS for sensorless control of PMSM drives using fuzzy-logic-controller. IEEE Open Journal of Power Electronics. 3, (June 2022), 368-381. https://doi.org/10.1109/OJPEL.2022.3182053. [Cross Ref]
    [18] Lei, X., and Zhang, Z. Recursive weighted least squares estimation algorithm based on minimum model error principle. Defence Technology. 17(2), (April 2021), 545-558. https://doi.org/10.1016/j.dt.2020.02.003. [Cross Ref]
    [19] Patwardhan, S. C., Narasimhan, S., Jagadeesan, P., Gopaluni, B., Shah, S. L. Nonlinear Bayesian state estimation: A review of recent developments. Control Engineering Practice. 20(10), (October 2012), 933-953. http://dx.doi.org/10.1016/j.conengprac.2012.04.003. [Cross Ref]
    [20] Xu, Y., Yang, K., Liu, A., Wang, X., and Jiang, F. 2019. Online parameter identification based on MTPA operation for IPMSM. In Proceedings of the 22nd International Conference on Electrical Machines and Systems (ICEMS), 1-4, IEEE. https://doi.org/10.1109/ICEMS.2019.8921761. [Cross Ref]
    [21] Li, X., Zhang, P., and Yan, L. Sensorless control of permanent magnet synchronous motor with online parameter identification. Transactions of China Electrotechnical Society. 31(14), (July 2016), 139-147.
    [22] Kim, W-H., Kim, M-J., Lee, K-D., Lee, J-J., Han, J-H., Jeong, T-C., Cho, S-Y., and Lee, J. Inductance calculation in IPMSM considering magnetic saturation. IEEE Transactions on Magnetics. 50(1), (Jan. 2014), 1-4. https://doi.org/10.1109/TMAG.2013.2277586. [Cross Ref]
    [23] Kerid, R., Bourouina, H., and Yahiaoui, R. Parameter identification of PMSM using EKF with temperature variation tracking in automotive application. Periodicals of Engineering and Natural Sciences (PEN). 6(2), (2018), 109-119. http://dx.doi.org/10.21533/pen.v6i2.168. [Cross Ref]
    [24] Wang, Q., Wang, G., Zhao, N., Zhang, G., Cui, Q. and Xu, D. An impedance model-based multiparameter identification method of PMSM for both offline and online conditions. IEEE Transactions on Power Electronics. 36(1), (Jan. 2021) 727-738. https://doi.org/10.1109/TPEL.2020.3000896. [Cross Ref]

Komal Naikawadi, Sudhir Madhav Patil, Kapil Kalantri, Maneetkumar R. Dhanvijay and Atul V. Karanjkar (2023), Inductance Estimation of PMSM Using Extended Kalman Filter. IJEER 11(1), 138-142. DOI: 10.37391/IJEER.110119.