TY - GEN
T1 - An Improved Model Predictive Control Approach for Wireless Power Transfer System of Electric Vehicles
AU - Xu, Baohua
AU - Deng, Junjun
AU - Gan, Haiqing
AU - Jiao, Xize
AU - Xu, Qi
AU - Jiang, Lingbo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the context of implementing dynamic wireless power transfer (DWPT) in electric vehicles (EVs), the mutual inductance of the magnetic coupler is continuously changing. Therefore, a controller with enough dynamic response is required to ensure the utilization of the energy transfer capacity and stability. In this paper, a model predictive control (MPC) approach with variable step-size and optimization-point-number is proposed to balance the dynamic response speed, steady-state error and computational burden of the system. The dynamics of the wireless power transfer (WPT) system is modeled and the boundary conditions for the zero-voltage-switching (ZVS) implementation are derived. The rules for the selection of step-size and optimization-point-number are established and it is worth noting that the variance of the output power is introduced as a basis for judgment, which greatly accelerates the convergence rate near the steady state. Finally, the simulation actively verifies the feasibility of the proposed controller.
AB - In the context of implementing dynamic wireless power transfer (DWPT) in electric vehicles (EVs), the mutual inductance of the magnetic coupler is continuously changing. Therefore, a controller with enough dynamic response is required to ensure the utilization of the energy transfer capacity and stability. In this paper, a model predictive control (MPC) approach with variable step-size and optimization-point-number is proposed to balance the dynamic response speed, steady-state error and computational burden of the system. The dynamics of the wireless power transfer (WPT) system is modeled and the boundary conditions for the zero-voltage-switching (ZVS) implementation are derived. The rules for the selection of step-size and optimization-point-number are established and it is worth noting that the variance of the output power is introduced as a basis for judgment, which greatly accelerates the convergence rate near the steady state. Finally, the simulation actively verifies the feasibility of the proposed controller.
KW - electric vehicles (EVs)
KW - model predictive control
KW - zero-voltage-switching (ZVS)
UR - http://www.scopus.com/inward/record.url?scp=85188087476&partnerID=8YFLogxK
U2 - 10.1109/ACFPE59335.2023.10455571
DO - 10.1109/ACFPE59335.2023.10455571
M3 - Conference contribution
AN - SCOPUS:85188087476
T3 - Proceedings - 2023 2nd Asian Conference on Frontiers of Power and Energy, ACFPE 2023
SP - 693
EP - 698
BT - Proceedings - 2023 2nd Asian Conference on Frontiers of Power and Energy, ACFPE 2023
A2 - Liu, Junyong
A2 - Liu, Youbo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd Asian Conference on Frontiers of Power and Energy, ACFPE 2023
Y2 - 20 October 2023 through 22 October 2023
ER -
