Reducing Circling Currents in a VHF Class Φ2 Inverter Based on a Fully Analytical Loss Model

Author:

Zhang Desheng,Min RunORCID,Liu Zhigang,Tong Qiaoling,Zhang Qiao,Wu Ting,Zhang Ming,Zhou Aosong

Abstract

This paper proposes a fully analytical loss model to reduce circling currents and improve the power efficiency of a class Φ2 inverter. Firstly, analytical expression of the switching node voltage is derived by analyzing its harmonic components. Based on the result, the power switch is modeled as a voltage source, where the circuit is simplified to a linear network and analytical expressions of branch currents are solved. Secondly, root mean square (RMS) values of branch currents and component losses are calculated to form the analytical loss model for a Φ2 inverter. The influence of circuit parameters on the circling current and power efficiency are thoroughly analyzed, which derives optimal design constraints to reduce circling currents of a class Φ2 inverter. Furthermore, detailed design guidance and equations are provided to calculate circuit parameters of a class Φ2 inverter, which reduces its circling currents and improves overall power efficiency. Finally, a class Φ2 inverter prototype is built, and experimental results demonstrate a 7% efficiency improvement compared to conventional empirical design methods.

Funder

National Natural Science Foundation of China

the Ministry of Industry and Information Technology of the People’s Republic of China, and Science, and Technology Project of State Grid Corporation of China Headquarters

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Design and Optimization of VHF Class $\Phi 2$ Inverter Dedicated for Wireless Charging Application;2023 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE);2023-10-12

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