A stable machine learning based control for bidirectional interleaved DC/DC converter in battery systems

被引：0

作者：

Li, Ran ^{[1
]}

Wang, Ruigang ^{[2
]}

Feng, Wendong ^{[1
]}

Qie, Tianhao ^{[1
]}

Liu, Yulin ^{[1
]}

Fernando, Tyrone ^{[1
]}

HoChing Iu, Herbert ^{[1
]}

Zhang, Xinan ^{[1
]}

机构：

[1] Univ Western Australia, Dept Elect Elect & Comp Engn, Perth, Australia

[2] Univ Sydney, Sch Aerosp Mech & Mechatron Engn, Australian Ctr Robot, Sydney, Australia

来源：

IET POWER ELECTRONICS | 2025年 / 18卷 / 01期

关键词：

controllers; DC-DC power convertors; CONTROL STRATEGY; DESIGN; STORAGE;

D O I：

10.1049/pel2.12847

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes an innovative machine learning-based control algorithm for the bidirectional interleaved DC/DC converter in battery systems. It offers fast dynamics, gain tuning-free control design, and guaranteed closed-loop stability. Furthermore, compared to the other learning-based control methods, the proposed algorithm shows low computational complexity in both the neural network training and the online digital implementation. The efficacy of the proposed method is substantiated through experimental validation.

引用

页数：10

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