电动汽车动态无线充电系统输出电流模型预测控制

doi:10.19595/j.cnki.1000-6753.tces.212029

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Abstract Dynamic wireless power transfer (DWPT) technique is helpful to reduce electric vehicle drivers' range anxiety and save charging time because it can charge an electric vehicle in motion. However, output power of the DWPT system fluctuates dramatically and frequently due to random coil misalignment and mutual inductance change in an adjacent coil region. Therefore, an accurate and rapid controller is highly expected to stabilize the output power of the system. Some control algorithms, such as PI control, μ control, model predictive control (MPC), have been introduced for this goal. Nevertheless, they are difficult to meet the requirements on rapid response and low complexity simultaneously. To this end, this paper proposes a constant current control strategy for an LCC-S compensated DWPT system by combining a Kalman filter (KF) and a MPC controller.
A Buck converter is introduced on the secondary side to regulate the output power. Firstly, the state-space model of the Buck converter is established by analyzing its work mode. Secondly, a MPC controller is developed for determining the duty ratio of the Buck converter. Thirdly, a Kalman filter is designed to estimate state variables of the Buck converter (i.e., capacitor voltage and inductor current), instead of measuring them using sensors, thus reducing hardware complexity and cost. Finally, simulation in Matlab/Simulink and experiments on STM32F334 are carried out to demonstrate the effectiveness of the proposed method. Also, the proposed method is compared with the traditional PI controller.
Simulation and experimental results show that the Kalman filter is able to estimate the state variables of the Buck converter accurately, which is the base for implementing the MPC controller. In the STM32F334 processor, the proposed KF-MPC requires a longer computation time (153 μs) than the PI controller (22 μs). However, the KF-MPC performs better significantly in respond speed because it needs much less total control cycles than the PI controller. Particularly, the KF-MPC just takes about 15 ms, while the PI controller takes about 2.2 s to track a new reference current. As the input voltage of the Buck converter changes due to coil misalignment, the KF-MPC can always keep the output current constant, while the PI controller takes about 3 s to recover the reference output current. When the load suddenly changes from 20 Ω to 15 Ω, and from 15 Ω to 25 Ω, the KF-MPC controller only takes 10 ms, while the PI controller takes about 1.4 s in average to recover the output current. In addition, both the KF-MPC and PI controllers do not influence the system efficiency obviously. The KF-MPC performs can work stably in a wide range of parameter value, which is valuable for practical applications.
Conclusions of the paper can be summarized as follows: (1) Although the KF-MPC controller requires a longer computation time than the PI controller, it performs a faster respond speed due to the significant reduction in the total control cycle. (2) The proposed KF-MPC only needs to measure the load current for implementing the MPC for the Buck converter, so it is more practical than traditional MPC controller, which needs more measurements. (3) The proposed KF-MPC controller performs extremely high robustness to mutual inductance change, and it does not depend on the communication between the primary side and the secondary side.

Key words： Electric vehicles dynamic wireless power transfer power control Kalman filter model predictive control

Received: 13 December 2021

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TM724

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	Tian Yong
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Cite this article:

Tian Yong,Feng Huayi,Tian Jindong等. Model Predictive Control for Output Current of Electric Vehicle Dynamic Wireless Charging Systems[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2310-2322.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.212029 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I9/2310

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