[1]仇逸,汪凤翔,黄卫东,等.LCL-S型无线电能传输系统负载与互感辨识方法[J].福建工程学院学报,2022,20(03):275-280.[doi:10.3969/j.issn.1672-4348.2022.03.012]
　QIU Yi,WANG Fengxiang,HUANG Weidong,et al.Identification method of load and mutual inductance in LCL-S 〖JZ〗wireless power transmission system[J].Journal of FuJian University of Technology,2022,20(03):275-280.[doi:10.3969/j.issn.1672-4348.2022.03.012]
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LCL-S型无线电能传输系统负载与互感辨识方法()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第20卷
期数:
2022年03期
页码:
275-280
栏目:
出版日期:
2022-06-25

文章信息/Info

Title:
Identification method of load and mutual inductance in LCL-S 〖JZ〗wireless power transmission system
作者:
仇逸; 汪凤翔; 黄卫东; 黄东晓
福建工程学院机械与汽车工程学院
Author(s):
QIU Yi;  WANG Fengxiang;  HUANG Weidong;  HUANG Dongxiao
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
磁耦合谐振; 无线电能传输; 多参数辨识; 粒子群算法; 多谐振补偿拓扑
Keywords:
magnetically-coupled resonance;  wireless power transmission;  multi-parameter identification;  particle swarm optimization;  multi-resonance compensation topology
分类号:
TM724
DOI:
10.3969/j.issn.1672-4348.2022.03.012
文献标志码:
A
摘要:
针对无线电能传输系统因线圈偏移、受电设备阻抗变化等导致系统模型参数不精确的问题，提出一种基于粒子群算法的LCL-S 型无线电能传输系统负载与互感辨识方法。根据LCL-S补偿拓扑原边恒流及副边恒压的输出特性，采用滤波电容能量守恒理论，建立负载输出电压与互感、原边线圈电压与负载的数学描述。以输出电压理论值与实际值之间的误差构建成本函数，设计粒子群算法，通过数值寻优实现参数的辨识。仿真与实验结果均验证了该方法能够较为准确地辨识出互感与负载大小，且无需增加额外硬件电路及控制，降低了系统复杂程度。
Abstract:
Aiming at the problem that the model parameters of wireless power transmission system are not accurate due to coil deviation and impedance change of the receiving device, a method of load and mutual inductance identification of LCL-S wireless powerless transmission system based on particle swarm optimization was proposed. According to the output characteristics of constant current on the primary side and constant voltage on the secondary side of LCL-S compensation topology, the mathematical description of load output voltage and mutual inductance, the primary side coil voltage and load was established by using the conservation theory of filter capacitor energy. The cost function was constructed based on the error between the theoretical value and the actual value of the output voltage, and the particle swarm optimization algorithm was designed to transform the parameter identification into a numerical optimization problem, thus realizing the parameter identification. Simulation and experimental results verify that the proposed method can accurately identify the mutual inductance and load, and no additional hardware circuits and controls are needed, thus reducing the complexity of the system.

参考文献/References:

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更新日期/Last Update: 2022-06-25