Skip to main content

Advertisement

Log in

A Supercapacitor-Based Interior Permanent Magnet Synchronous Motor Drive Using Intelligent Control for Light Rail Vehicle

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

A supercapacitor (SC)-based interior permanent magnet synchronous motor (IPMSM) drive including the speed tracking of a specific velocity profile and the charging of the SC is developed in this study to emulate the operation of an urban light rail vehicle (LRV). In the SC-based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC are both designed. In the motoring mode, a field-oriented controlled (FOC) IPMSM drive system is developed to emulate the speed control of an LRV. In the charging mode, the constant current and constant voltage (CC–CV) charging strategy is developed for the charging of the SC. Moreover, the above two modes use the same inverter and coordinate transformations to reduce the design complexity. Furthermore, in order to test the performance of SC, the speed command of the emulated LRV is obtained using a specific testing driving cycle. The design objective is for fast charging of SC being able to provide enough energy for the emulated LRV to operate a full testing driving cycle. In addition, to improve the transient speed response of the emulated LRV, a Chebyshev fuzzy neural network (CheFNN) intelligent speed controller is proposed. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the developed CC–CV charging strategy for the SC and the proposed CheFNN speed controller for the emulated LRV.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Harrop, P.: Supercapacitors: Applications, Players, Markets 2020–2040. IDTechEx, Cambridge, UK, 2020. https://www.idtechex.com/en/research-report/supercapacitors-applications-players-markets-2020-2040/661 (2020)

  2. Abeywardana, D.B.W., Hredzak, B., Agelidis, V.G.: Single-phase grid-connected LiFePO4 battery–supercapacitor hybrid energy storage system with interleaved boost inverter. IEEE Trans. Power Electron. 30(10), 5591–5604 (2015)

    Article  Google Scholar 

  3. Mamun, A.A., Liu, Z., Rizzo, D.M., Onori, S.: An integrated design and control optimization framework for hybrid military vehicle using lithium-ion battery and supercapacitor as energy storage devices. IEEE Trans. Transp. Electr. 5(1), 239–251 (2019)

    Article  Google Scholar 

  4. Burke, A., Zhao, H.: Present and future applications of supercapacitors in electric and hybrid vehicles. In: 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, 1–5 (2015)

  5. Zhao, J., Gao, Y., Burke, A.F.: Performance testing of supercapacitors: important issues and uncertainties. J. Power Sources 363, 327–340 (2017)

    Article  Google Scholar 

  6. Xu, D., Zhang, L., Wang, B., Ma, G.: Modeling of supercapacitor behavior with an improved two-branch equivalent circuit. IEEE Access. 7, 26379–26390 (2019)

    Article  Google Scholar 

  7. Mir, L., Etxeberria-Otadui, I., de Arenaza, I.P., Sarasola, I., Nieva, T.: A supercapacitor based light rail vehicle: system design and operations modes. In: 2009 IEEE Energy Conversion Congress and Exposition, San Jose, CA, 1632–1639 (2009)

  8. Barrero, R., Tackoen, X., Van Mierlo, J.: Analysis and configuration of supercapacitor based energy storage system on-board light rail vehicles. In: 2008 13th International Power Electronics and Motion Control Conference, Poznan, pp. 1512–1517 (2008)

  9. Ghavihaa, N., Campilloa, J., Bohlinb, M., Dahlquista, E.: Review of application of energy storage devices in railway transportation. Energy Procedia. 105, 4561–4568 (2017)

    Article  Google Scholar 

  10. Yang, Z., Yang, Z., Xia, H., Lin, F., Zhu, F.: Supercapacitor state based control and optimization for multiple energy storage devices considering current balance in urban rail transit. Energies 10(4), 520 (2017)

    Article  Google Scholar 

  11. Radu, P.V., Szelag, A., Steczek, M.: On-board energy storage devices with Supercapacitors for metro trains—case study analysis of application effectiveness. Energies 12, 1291 (2019)

    Article  Google Scholar 

  12. Zou, Z., Cao, J., Cao, B., Chen, W.: Evaluation strategy of regenerative braking energy for supercapacitor vehicle. ISA Trans. 55, 234–240 (2015)

    Article  Google Scholar 

  13. Adib, A., Dhaouadi, R.: Modeling and analysis of a regenerative braking system with a battery-supercapacitor energy storage. In: 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), Sharjah, pp. 1–6 (2017)

  14. Khodaparastan, M., Mohamed, A.A., Brandauer, W.: Recuperation of regenerative braking energy in electric rail transit systems. IEEE Trans. Intel. Transp. Sys. 20(8), 2831–2847 (2019)

    Article  Google Scholar 

  15. Sahoo, D.M., Chakraverty, S.: Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data. IEEE Trans. Syst. Man Cybern. Syst. 48(1), 1–10 (2018)

    Article  Google Scholar 

  16. Vyas, B.Y., Das, B., Maheshwari, R.P.: Improved fault classification in series compensated transmission line: comparative evaluation of chebyshev neural network training algorithms. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1631–1642 (2016)

    Article  MathSciNet  Google Scholar 

  17. Jin, L., Huang, Z., Li, Y., Sun, Z., Li, H., Zhang, J.: On modified multioutput Chebyshev-polynomial feed-forward neural network for pattern classification of wine regions. IEEE Access. 7, 1973–1980 (2019)

    Article  Google Scholar 

  18. Hou, S., Fei, J., Chen, C., Chu, Y.: Finite-time adaptive fuzzy-neural network control of active power filter. IEEE Trans. Power Electron. 34(10), 10298–10313 (2019)

    Article  Google Scholar 

  19. Lin, F.J., Huang, M.S., Chen, S.G., Hsu, C.W., Liang, C.H.: Adaptive backstepping control for synchronous reluctance motor based on intelligent current angle control. IEEE Trans. Power Electron. 35(7), 7465–7479 (2020)

    Article  Google Scholar 

  20. Lin, F.J., Liu, Y.T., Yu, W.A.: Power perturbation based MTPA with intelligent speed controller for IPMSM drive system. IEEE Trans. Ind. Electron. 65(5), 3677–3687 (2018)

    Article  Google Scholar 

  21. Lin, F.J., Huang, M.S., Yeh, P.Y., Tsai, H.C., Kuan, C.H.: DSP-based probabilistic fuzzy neural network control for Li-ion battery charger. IEEE Trans. Power Electron. 27(8), 3782–3794 (2012)

    Article  Google Scholar 

  22. Lin, F.J., Hung, Y.C., Hwang, J.C., Chang, I.P., Tsai, M.T.: Digital signal processor-based probabilistic fuzzy neural network control of in-wheel motor drive for light electric vehicle. IET Elec. Power Appl. 6(2), 47–61 (2012)

    Article  Google Scholar 

  23. Wai, R.J., Liu, C.M.: Design of dynamic Petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot. IEEE Trans. Ind. Electron. 56(7), 2667–2683 (2009)

    Article  Google Scholar 

  24. Lin, F.J., Chen, S.G., Hsu, C.W.: Intelligent backstepping control using recurrent feature selection fuzzy neural network for synchronous reluctance motor position servo drive system. IEEE Trans. Fuzzy Syst. 27(3), 413–427 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support from Ministry of Science and Technology of Taiwan, R.O.C. under Grant MOST 109-2221-E-008-024.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Faa-Jeng Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, FJ., Liao, JC. & Chang, EW. A Supercapacitor-Based Interior Permanent Magnet Synchronous Motor Drive Using Intelligent Control for Light Rail Vehicle. Int. J. Fuzzy Syst. 23, 1539–1555 (2021). https://doi.org/10.1007/s40815-021-01075-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01075-0

Keywords

Navigation