Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

Power flow predictive model control to improve the efficiency of regenerative energy storage and utilization

  • Sun, Shizhen (College of Electrical and Power Engineering, Taiyuan University of Technology) ;
  • Zhang, Hongjuan (College of Electrical and Power Engineering, Taiyuan University of Technology) ;
  • Wang, Xiaoji (College of Electrical and Power Engineering, Taiyuan University of Technology) ;
  • Gao, Yan (College of Electrical and Power Engineering, Taiyuan University of Technology) ;
  • Jin, Baoquan (Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology)
  • Received : 2021.12.25
  • Accepted : 2022.05.20
  • Published : 2022.10.20
⟨ Previous Next ⟩

Abstract

In dual-motor drive systems, a supercapacitor is connected to a common direct current (DC) bus through a DC/DC converter for the storage and utilization of regenerative energy, which is an effective energy saving method. However, the uncoordinated control of this type of system results in undesirable power circulation and reduced energy utilization efficiency. In this paper, an optimal power tracking control strategy based on a power flow predictive model is proposed. The power flow of the system is analyzed and a power flow predictive model is established. In addition, an objective function is deduced from the perspective of optimal performance tracking and minimum grid side energy consumption. The reference power of a supercapacitor is obtained in real time under constraints. The power flows among the grid side, the motors, and the energy storage unit are fully coordinated to realize a reasonable energy distribution. Experimental results indicate that the energy utilization efficiency of the system is improved by 25.4% in comparison with double closed-loop control in one working period.

Keywords

Acknowledgement

The authors would like to thank the National Natural Science Foundation of China (No. 51775363) and the Key Research and Development Projects of Shanxi Province (No. 201803D121124) for funding this research project.

References

  1. Lin, Q.F., Niu, S.X., Cai, F.B., Fu, W.N., Shang, L.K.: Design and optimization of a novel dual-PM machine for electric vehicle applications. IEEE Trans. Veh. Technol. 69(12), 14391-14400 (2020) https://doi.org/10.1109/TVT.2020.3034573
  2. Herrera, V.I., Gaztanaga, H., Milo, A., Saez-de-Ibarra, A., Etxeberria- Otadui, I., Nieva, T.: Optimal energy management and sizing of a battery-supercapacitor-based light rail vehicle with a multiobjective approach. IEEE Trans. Ind. Appl. 52(4), 3367-3377 (2016) https://doi.org/10.1109/TIA.2016.2555790
  3. Yang, X.F., Zhang, L.W., Xie, W., Zhang, J.F.: Sequential and iterative distributed model predictive control of multi-motor driving cutterhead system for TBM. IEEE Access. 7, 46977-46989 (2019) https://doi.org/10.1109/ACCESS.2019.2908388
  4. Hu, X.S., Li, Y.P., Lv, C., Liu, Y.G.: Optimal energy management and sizing of a dual motor-driven electric powertrain. IEEE Trans. Power Electron. 34(8), 7489-7501 (2019) https://doi.org/10.1109/TPEL.2018.2879225
  5. Jones, M., Vukosavic, S.N., Dujic, D., Levi, E., Wright, P.: Fiveleg inverter PWM technique for reduced switch count two-motor constant power applications. IET Electr. Power App. 2(5), 275-287 (2008) https://doi.org/10.1049/iet-epa:20070497
  6. Wu, Y.J., Cheng, Y.B., Wang, Y.L.: Research on a multi-motor coordinated control strategy based on fuzzy ring network control. IEEE Access. 8, 39375-39388 (2020) https://doi.org/10.1109/ACCESS.2020.2974906
  7. Pal, S., Majumder, M.G., Rakesh, R., Gopakumar, K., Umanand, L., Zielinski, D., Beig, A.R.: A cascaded nine-level inverter topology with T-type and H-bridge with increased DC-bus utilization. IEEE Trans. Power Electron. 36(1), 285-294 (2021) https://doi.org/10.1109/TPEL.2020.3002918
  8. Hu, W., Ruan, C.H., Nian, H., Sun, D.: Simplified modulation scheme for open-end winding PMSM system with common DC bus under open-phase fault based on circulating current suppression. IEEE Trans. Power Electron. 35(1), 10-14 (2020) https://doi.org/10.1109/TPEL.2019.2924821
  9. Hu, W., Nian, H., Zheng, T.Y.: Torque ripple suppression method with reduced switching frequency for open-winding PMSM drives with common DC bus. IEEE Trans. Ind. Electron. 66(1), 674-684 (2019) https://doi.org/10.1109/TIE.2018.2833803
  10. Zhang, X.G., Wang, K.Q.: Current prediction based zero sequence current suppression strategy for the semicontrolled open-winding PMSM generation system with a common DC bus. IEEE Trans. Ind. Electron. 65(8), 6066-6076 (2018) https://doi.org/10.1109/TIE.2017.2784353
  11. Li, J.F., Tang, T.H., T.Z. Wang, Yao, G.: Modeling and simulation for common DC bus multi-motor drive systems based on activity cycle diagrams. In: IEEE International Symposium on Industrial Electronics (ISIE 2010), Bari, Italy, 4-7 July 2010
  12. Zhu, F.Q., Yang, Z.P., Lin, F., Xin, Y.: Decentralized cooperative control of multiple energy storage systems in urban railway based on multiagent deep reinforcement learning. IEEE Trans. Power Electron. 35(9), 9368-9379 (2020) https://doi.org/10.1109/TPEL.2020.2971637
  13. Khodaparastan, M., Mohamed, A.A., Brandauer, W.: Recuperation of regenerative braking energy in electric rail transit systems. IEEE Trans. Intell. Transp. 20(8), 2831-2847 (2019) https://doi.org/10.1109/TITS.2018.2886809
  14. Chen, J.Y., Hu, H.T., Ge, Y.B., Wang, K., Huang, W.L., He, Z.Y.: An energy storage system for recycling regenerative braking energy in high-speed railway. IEEE Trans. Power deliver. 36(1), 320-330 (2021) https://doi.org/10.1109/TPWRD.2020.2980018
  15. Bi, K., Sun, L., An, Q., Duan, J.: Active SOC balancing control strategy for modular multilevel super capacitor energy storage system. IEEE Trans. Power Electron. 34(5), 2129-2140 (2019)
  16. Tummuru, N.R., Mishra, M.K., Srinivas, S.: Dynamic energy management of hybrid energy storage system with high-gain PV converter. IEEE Trans. Energy Conver. 30(1), 150-160 (2015) https://doi.org/10.1109/TEC.2014.2357076
  17. Kreczanik, P., Venet, P., Hijazi, A., Clerc, G.: Study of supercapacitor aging and lifetime estimation according to voltage, temperature, and RMS current. IEEE Trans. Ind. Electron. 61(9), 4895-4902 (2014) https://doi.org/10.1109/TIE.2013.2293695
  18. Zhang, H.J., Zhang, F., Yang, L., Gao, Y., Jin, B.Q.: Multi-parameter collaborative power prediction to improve the efficiency of supercapacitor-based regenerative braking system. IEEE Trans. Energy Convers. 36(4), 2612-2622 (2021) https://doi.org/10.1109/TEC.2021.3074697
  19. Iannuzzi, D., Pagano, E., Tricoli, P.: The use of energy storage systems for supporting the voltage needs of urban and suburban railway contact lines. Energies 6(4), 1802-1820 (2013) https://doi.org/10.3390/en6041802
  20. Zhang, J.J., Yang, Y., Qin, D.T., Fu, C.Y., Cong, Z.P.: Regenerative braking control method based on predictive optimization for four-wheel drive pure electric vehicle. IEEE Access. 9, 1394-1406 (2021) https://doi.org/10.1109/ACCESS.2020.3046853
  21. Liu, H.W., Lei, Y.L., Fu, Y., Li, X.Z.: An optimal slip ratio-based revised regenerative braking control strategy of range-extended electric vehicle. Energies 13(6), 1526 (2020) https://doi.org/10.3390/en13061526
  22. Xu, Q.W., Zhou, C., Huang, H., Zhang, X.F.: Research on the coordinated control of regenerative braking system and ABS in hybrid electric vehicle based on composite structure motor. Electronics 10(3), 223 (2021) https://doi.org/10.3390/electronics10030223
  23. Yang, Z.H., Yang, Z.P., Xia, H., Lin, F.: Brake voltage following control of supercapacitor-based energy storage systems in metro considering train operation state. IEEE Trans. Ind. Electron. 65(8), 6751-6761 (2018) https://doi.org/10.1109/TIE.2018.2793184
  24. Zhong, Z.H., Yang, Z.P., Fang, X.C., Lin, F., Tian, Z.B.: Hierarchical optimization of an on-board supercapacitor energy storage system considering train electric braking characteristics and system loss. IEEE Trans. Veh. Technol. 69(3), 2576-2587 (2020) https://doi.org/10.1109/TVT.2020.2967467
  25. Heydari, S., Fajri, P., Rasheduzzaman, M., Sabzehgar, R.: Maximizing regenerative braking energy recovery of electric vehicles through dynamic low-speed cutoff point detection. IEEE Trans. Transp. Electr. 5(1), 262-270 (2019) https://doi.org/10.1109/TTE.2019.2894942
  26. Wang, C.Y., Zhao, W.Z., Li, W.K., Yu, L.Y.: Multi-objective optimisation of electro-hydraulic braking system based on MOEA/D algorithm. IET Intell. Transp. Sy. 13(1), 183-193 (2019) https://doi.org/10.1049/iet-its.2018.5090