Skip to main content
SpringerLink
Log in

MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization

  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

In order to effectively achieve torque demand in electric vehicles (EVs), this paper presents a torque control strategy based on model predictive control (MPC) for permanent magnet synchronous motor (PMSM) driven by a two-level three-phase inverter. A centralized control strategy is established in the MPC framework to track the torque demand and reduce energy loss, by directly optimizing the switch states of inverter. To fast determine the optimal control sequence in predictive process, a searching tree is built to look for optimal inputs by dynamic programming (DP) algorithm on the basis of the principle of optimality. Then we design a pruning method to check the candidate inputs that can enter the next predictive loop in order to decrease the computational burden of evaluation of input sequences. Finally, the simulation results on different conditions indicate that the proposed strategy can achieve a tradeoff between control performance and computational efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Gasbaoui, A. Nasri. A novel 4WD electric vehicle control strategy based on direct torque control space vector modulation technique. Nonlinear Engineering, 2012, 3: 236–242.

    Google Scholar 

  2. P. Liu, H. P. Liu. Permanent-magnet synchronous motor drive system for electric vehicles using bidirectional Z-source inverter. IET Electrical Systems in Transportation, 2012, 4(2): 178–185.

    Article  Google Scholar 

  3. C. H. T. Lee, K. T. Chau, C. H. Liu. Design and analysis of an electronic-geared magnetless machine for electric vehicles. IEEE Transactions on Industrial Electronics, 2016, 63(11): 6705–6714.

    Article  Google Scholar 

  4. K. C. Kim. A novel calculation method on the current information of vector inverter for interior permanent magnet synchronous motor for electric vehicle. IEEE Transactions on Magnetics, 2014, 50(2): 829–832.

    Article  Google Scholar 

  5. D. Casadei, F. Profumo, G. Serra. FOC and DTC: two viable schemes for induction motors torque control. IEEE Transactions on Power Electronics, 2002, 17(5): 779–787.

    Article  Google Scholar 

  6. J. Rodriguez, P. Cortés. Predictive Control of Power Converters and Electrical Drives. Chichester: John Wiley & Sons,2012.

    Book  Google Scholar 

  7. T. Geyer, G. Papafortiou, M. Morari. Model predictive direct torque contro–Part I: Concept, algorithm, and analysis. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1894–1905.

    Article  Google Scholar 

  8. C. Bordons, C. Montero. Basic principles of MPC for power converters. IEEE Industrial Electronics Magazine, 2015, 9(3): 31–43.

    Article  Google Scholar 

  9. P. K. Manakos, T. Geyer, N. Oikonomou, et al. Direct model predictive control: A review of strategies that achieve long prediction intervals for power electronics. IEEE industrial Electronics Magazine, 2014, 8(1): 32–43.

    Article  Google Scholar 

  10. P. Cortés, M. P. Kazmierkowski, R. M. Kennel, et al. Predictive control in power electronics and drives. IEEE Transactions on Industrial Electronics, 2008, 55(12): 4312–4324.

    Article  Google Scholar 

  11. F. Morel, X. F. Lin-Shi, J. M. Rétif, et al. A comparative study of predictive current control schemes for a permanent-magnet synchronous machine drive. IEEE Transactions on Industrial Electronics, 2009, 56(7): 2715–2728.

    Article  Google Scholar 

  12. M. Preindl, S. Bolognani. Model predictive direct torque control with finite control set for pmsm drive systems, part 1: Maximum torque per ampere operation. IEEE Transactions on Industrial Informatics, 2013, 9(4): 1912–1921.

    Article  Google Scholar 

  13. J. J. Hong, D. H. Pan, Z. J. Zong. Comparison of the two current predictive-control methods for a segment-winding permanentmagnetlinear synchronous motor. IEEE Transactions on Plasma Science, 2013, 41(5): 1167–1173.

    Article  Google Scholar 

  14. F. Barrero, J. Prieto, E. Levi, et al. An enhanced predictive current control method for asymmetrical six-phase motor drives. IEEE Transactions on Industrial Electronics, 2011, 58(8): 3242–3252.

    Article  Google Scholar 

  15. S. Kouro, P. Coréts, R. Vargas, et al. Model predictive controla simple and powerful method to control power converters. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1826–1838.

    Article  Google Scholar 

  16. G. Prior, M. Krstic. A control Lyapunovapproach to finite control set model predictive control for permanent magnet synchronous motors. ASME Journal of Dynamic Systems, Measurement, and Control, 2015, 137(1): 1–10.

    Google Scholar 

  17. M. J. Duran, J. Prieto, F. Barrero, et al. Predictive current control of dual three-phase drives using restrained search techques. IEEE Transactions on Industrial Electronics, 2011, 58(8): 3253–3263.

    Article  Google Scholar 

  18. T. Geyer. Computationally efficient model predictive direct torque control. IEEE Transactions on Power Electronics, 2011, 26(10): 2804–2816.

    Article  Google Scholar 

  19. D. P. Bertsekas. Dynamic Programming and Optimal Control. 3rd ed. Nashua: Athena Scientific,2005.

    MATH  Google Scholar 

  20. D. Graovac, M. Pürschel. IGBT Power Losses Calculation Using the Data-Sheet Parameters. 129th ed. Neubiberg: Infineon Technologies AG,2009.

    Google Scholar 

  21. H. Chen, F. Allgöwer. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica, 1998, 34(10): 1205–1217.

    Article  MathSciNet  MATH  Google Scholar 

  22. H. Chen. Model Predictive Control. 1st ed. System and Control Series. Beijing: Science Press,2013.

    Google Scholar 

  23. J. F. Stumper, A. Döltinger, R. Kennel. Classical model predictive control of a permanent magnet synchronous motor. EPE Journal, 2012, 22(3): 24–31.

    Article  Google Scholar 

  24. E. L. Lawler, D. E. Wood. Branch-and-bound methods: A survey. Operations Research, 1996, 14(4): 699–719.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun Jilin, 130025, China

    Bingtao Ren, Hong Chen & Haiyan Zhao

  2. Department of Control Science and Engineering, Jilin University, Changchun Jilin, 130025, China

    Bingtao Ren, Hong Chen, Haiyan Zhao & Wei Xu

Authors
  1. Bingtao Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiyan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Chen.

Additional information

This work was supported by the NSFC Projects of International Cooperation and Exchanges (No. 61520106008), the National Natural Science Foundation of China (Nos. 61503149, U1564207) and the Graduate Innovation Fund of Jilin University (No. 2016093).

Bingtao REN received the B.Sc. degree in Automation from Jilin University, Changchun, China, in 2012, where he is currently working toward the Ph.D. degree in Control Theory and Control Engineering. His current research interests include torque coordination optimization and motor control of electric vehicles.

Hong CHEN received the B.Sc. and M.Sc. degrees in Process Control from Zhejiang University, Zhejiang, China, in 1983 and 1986, respectively, and the Ph.D. degree in System Dynamics and Control Engineering from the University of Stuttgart, Stuttgart, Germany, in 1997. Since 1999, she has been a Professor in Jilin University, Changchun, China, where she currently serves as Tang Aoqing Professor and as the Director of the State Key Laboratory of Automotive Simulation and Control. Her current research interests include model predictive control, optimal and robust control, nonlinear control and applications in mechatronic systems focusing on automotive systems.

Haiyan ZHAO received the B.Sc. degree in Automation, the M.Sc. and Ph.D. degree in Control Theory and Control Engineering from Jilin University, Changchun, China, in 1998, 2004 and 2007, respectively. Since 2007, she has been a lecturer in Jilin University, Changchun, China. Her current research interests include vehicle stability control and state estimation of electric vehicles.

Wei XU received the B.Sc. degree in Automation from Harbin University of Science and Technology, Harbin, China, in 2010, and received the M.Sc. degree in Pattern Recognition and Intelligent System from Jilin University, Changchun, China, in 2015, where she is currently working toward the Ph.D. degree in Control Theory and Control Engineering. Her current research focuses on regenerative braking system of electric vehicles.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, B., Chen, H., Zhao, H. et al. MPC-based torque control of permanent magnet synchronous motor for electric vehicles via switching optimization. Control Theory Technol. 15, 138–149 (2017). https://doi.org/10.1007/s11768-017-6193-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-017-6193-z

Keywords

Search

Navigation