Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Modeling and flexible power control of electric vehicle wireless charging applied to building side AC/DC power distribution and consumption on building side

  • Hui Zhang (Economic and Technical Research Institute of the State Grid Anhui Electric Power Co., Ltd.) ;
  • Zhifan Li (School of Electrical Engineering and Automation, Wuhan University) ;
  • Xuli Wang (Economic and Technical Research Institute of the State Grid Anhui Electric Power Co., Ltd.) ;
  • Haozhe Deng (Wuhan Britain-China School)
  • Received : 2024.07.10
  • Accepted : 2025.01.12
  • Published : 2025.08.20
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Abstract

For AC/DC power distribution and consumption on the building side, EV charging provides a flexible and fast power regulation potential. With wireless charging, household users can actively execute load power adjustment according to the direction of the distribution network dispatch center to balance the supply between the power generation side and the demand side. For independent residential scenarios, the power of wireless charging electric vehicle (EV) systems can be regulated conveniently. To investigate the dynamic characteristic of EV wireless charging power regulation, a data-driven modeling method is proposed in this paper, which identifies the dynamic model based on the instrument variable method. Based on the estimated model, an internal model controller is further designed to regulate the wireless charging power of EVs to realize rapid response adjustments of the demand side loads. Experimental results show that the designed controller can autonomously adjust the charging power to suppress household power fluctuations and quickly respond to load regulation instructions from a distribution scheduling center to achieve a rapid response for the demand side.

Keywords

Acknowledgement

This work was supported in part by the State Grid Anhui Provincial Power Grid Co., Ltd. Technology Project under Grant B31209230003.

References

  1. Zheng, X., Peng, P.: Fault diagnosis of wind power converters based on compressed sensing theory and weight constrained AdaBoost-SVM. J. Power Electron. 23, 1004–1015 (2023) https://doi.org/10.1007/s43236-022-00578-9
  2. Kim, Y.C., Song, S.H.: Frequency regulation algorithm of wind turbines with control gain based on maximum power point estimation. J. Power Electron. 24, 414–424 (2024) https://doi.org/10.1007/s43236-023-00732-x
  3. Fu, Y., Liu, Y., Huang, L., Ying, F., Li, F.: Collection system topology for deep-sea offshore wind farms considering wind characteristics. IEEE Trans. Energy Convers. 37(1), 631–642 (2022) https://doi.org/10.1109/TEC.2021.3104040
  4. Ko, M., Lee, K., Kim, J., Hong, C., Dong, Z., Hur, K.: Deep concatenated residual network with bidirectional LSTM for one hour-ahead wind power forecasting. IEEE Trans. Sustain. Energy. 12(2), 1321–1335 (2021) https://doi.org/10.1109/TSTE.2020.3043884
  5. Khan, M., Forouzesh, M., Siwakoti, Y., Li, L., Kerekes, T., Blaabjerg, F.: Transformerless inverter topologies for single-phase photovoltaic systems: a comparative review. IEEE J. Emerg. Select. Topics Power Electron. 8(1), 805–835 (2020) https://doi.org/10.1109/JESTPE.2019.2908672
  6. Jain, P., Poon, J., Singh, J., Spanos, C., Sanders, S., Panda, S.: A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Trans. Power Electron. 35(1), 940–956 (2020) https://doi.org/10.1109/TPEL.2019.2911594
  7. Grigoletto, F.: Five-level transformerless inverter for single-phase solar photovoltaic applications. IEEE J. Emerg. Select. Topics Power Electron. 8(4), 3411–3422 (2020) https://doi.org/10.1109/JESTPE.2019.2891937
  8. Zhao, Y., Zhang, G., Hu, W., Huang, Q., Chen, Z., Blaabjerg, F.: Meta-learning based voltage control for renewable energy integrated active distribution network against topology change. IEEE Trans. Power Syst. 38(6), 5937–5940 (2023) https://doi.org/10.1109/TPWRS.2023.3309536
  9. Dutta, R., Chakrabarti, S., Sharma, A.: Topology tracking for active distribution networks. IEEE Trans. Power Syst. 36(4), 2855–2865 (2021) https://doi.org/10.1109/TPWRS.2020.3045855
  10. Ding, T., Wang, Z., Qu, M., Wang, Z., Shahidehpour, M.: A Sequential black-start restoration model for resilient active distribution networks. IEEE Trans. Power Syst. 37(4), 3133–3136 (2022) https://doi.org/10.1109/TPWRS.2022.3164589
  11. Gasmi, H., Mendaci, S., Laifa, S., et al.: Fractional-order proportional-integral super twisting sliding mode controller for wind energy conversion system equipped with doubly fed induction generator. J. Power Electron. 22, 1357–1373 (2022) https://doi.org/10.1007/s43236-022-00430-0
  12. Aeishwarya, B., Kaushik, D., Matti, K., Anca, D.H.: Multi-voltage level active distribution network with large share of weather dependent generation. IEEE Trans. Power Syst. 37(6), 4874–4884 (2022) https://doi.org/10.1109/TPWRS.2022.3154613
  13. Chen, Y., Chen, W., Yang, R., Li, Z.: Optimal allocation method of hybrid active power filters in active distribution networks based on differential evolution algorithm. J. Power Electron. 19(5), 1289–1302 (2019)
  14. Zhang, Y., Jing, X., Xie, G., Han, X., Zhang, J., Bian, H: Analysis and modelling of user behaviors' impact on load characteristics. In: 2022 7th Asia Conference on Power and Electrical Engineering (ACPEE). 1304–1308 (2022)
  15. Monteiro, V., Pinto, J., Afonso, J.: Operation modes for the electric vehicle in smart grids and smart homes: present and proposed modes. IEEE Trans. Veh. Technol. 65(3), 1007–1020 (2016) https://doi.org/10.1109/TVT.2015.2481005
  16. Pang, C., Dutta, P., Kezunovic, M.: BEVS/PHEVs as dispersed energy storage for V2B uses in the smart grid. IEEE Trans. Smart Grid. 3(1), 473-482 (2012) https://doi.org/10.1109/TSG.2011.2172228
  17. Casaucao Tenllado, I., Trivino Cabrera, A., Lin, Z. Simultaneous wireless power and data transfer for electric vehicle charging: a review. IEEE Trans. Transp. Electrif. 10(2), 4542-4570 (2024) https://doi.org/10.1109/TTE.2023.3309505
  18. Hua, Z., Chau, K., Pang, H., Yang, T.: Dynamic wireless charging IEEE for electric vehicles with autonomous frequency control. Trans. Magn. 59(11), 1-5 (2023) https://doi.org/10.1109/TMAG.2023.3293793
  19. Liu, Y., Zhang, D., Hoay, B.G.: Optimization strategy based on deep reinforcement learning for home energy management. CSEE J Power Energy Syst. 6(3), 572-582 (2020)
  20. Li, S., Zhao, P., Gu, C., Li, J., Huo, D., Cheng, S.: Aging mitigation for battery energy storage system in electric vehicles. IEEE Trans. Smart Grid. 14(3), 2152-2163 (2023) https://doi.org/10.1109/TSG.2022.3210041
  21. Alexandros-M, K., Emmanouil, S.R., Nick, B., Sarvapali, D.R.: Offline and online electric vehicle charging scheduling with V2V energy transfer. IEEE Trans. Intell. Transp. Syst. 21(5), 2128-2138 (2019)
  22. Shen, J., Wang, L., Zhang, J.: Integrated scheduling strategy for private electric vehicles and electric taxis. IEEE Trans. Ind. Inform. 17(3), 1637-1647 (2021) https://doi.org/10.1109/TII.2020.2993239
  23. Tabish, I., Ahmed, E., Marizan, M., Soon, T.K., Saad, M.: Enhancing misalignment tolerance using naturally decoupled identical dual-transmitter-dual-receiver coils for wireless EV charging system. IEEE J. Emerg. Select. Topics Power Electron. 12(5), 5337-5351 (2024) https://doi.org/10.1109/JESTPE.2024.3447286
  24. Peyman, D., Amran, H., Saad, M., Soon, T.K., Marizan, B.M., Yin, J.: Dual-receiver based naturally decoupled coil structure with improved rotational misalignment tolerance for WPT systems. IEEE Trans. Transp. Electrif. 10(3), 5412-5423 (2024) https://doi.org/10.1109/TTE.2023.3330638
  25. Yin, J., Saad, M., Peyman, D., Hazlie, M., Tey, K.S: A new cross-overlapped decoupling coil structure for EV dynamic inductive wireless charging system. IEEE Trans. Ind. Electron. 72(2), 13141324 (2025) https://doi.org/10.1109/TIE.2024.3423446
  26. Ye, Z., Gao, Y., Yu, N.: Learning to operate an electric vehicle charging station considering vehicle-grid integration. IEEE Trans. Smart Grid. 13(4), 3038-3048 (2022) https://doi.org/10.1109/TSG.2022.3165479
  27. Li, S., Chunting, C.M.: Wireless power transfer for electric vehicle applications. IEEE J. Emerg. Select. Topics Power Electron. 3(1), 4-17 (2014)
  28. Habiballah, R., Federico, B., Mo-Yuen, C.: Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells. IEEE Trans. Ind. Electron. 61(4), 2053-2061 (2014) https://doi.org/10.1109/TIE.2013.2263774
  29. Sun, Y., Xue, B., Zhang, M., Yen, G., Lv, J.: Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans. Cybern. 50(9), 3840-3854 (2020) https://doi.org/10.1109/TCYB.2020.2983860
  30. Yang, L., Cheng, J., Jonathan, F.H.: Maximum likelihood estima-tion of the Lognormal-Rician FSO channel model. IEEE Photonics Technol. Lett. 27(15), 1656-1659 (2015) https://doi.org/10.1109/LPT.2015.2433871
  31. Chen, F., Young, P.C., Hugues, G., Deng, Q., Marian, K.K.: Data-driven modeling of wireless power transfer systems with multiple transmitters. IEEE Trans. Power Electron. 35(11), 11363-11379 (2020) https://doi.org/10.1109/TPEL.2020.2989155
  32. Li, P., Zhu, G., Zhang, M.: Linear active disturbance rejection con - trol for servo motor systems with input delay via internal model control rules. IEEE Trans. Ind. Electron. 68(2), 1077–1086 (2021) https://doi.org/10.1109/TIE.2020.2970617
  33. Deng, Q., Li, Z., Liu, J., Li, S., Luo, P., Cui, K.: Data-driven modeling and control considering time delays for WPT system. IEEE Trans. Power Electron. 37(8), 9923-9932 (2022) https://doi.org/10.1109/TPEL.2022.3151941