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Special Issue Paper

An Automatic Optimization Topology Recommendation Method for DC-DC Converters

Yidi Liang1Hong Li2( )Mingbo Wei1Yangbin Zeng3Zhenyu Zhao4Bo Zhang3
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
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Abstract

Current topology recommendation methods for DC-DC converters predominantly rely on manual experience, often involving the analysis of performance metrics (either manually or via a computer) and subsequently selecting the most suitable topology to meet specific engineering requirements. However, as the number of available topologies increases and engineering demands vary, these methods are increasingly unable to provide optimal recommendations. To address this limitation, the present study presents an automatic optimization topology recommendation method (AO-TRM) for DC-DC converters that can accommodate a broad range of engineering requirements. The proposed method begins by identifying precise engineering requirements and then progresses through three key stages: topology generation, analysis, and recommendation. Two engineering applications are used as case studies to validate the effectiveness and capabilities of the proposed AO-TRM. From a pool of 1186 topologies, the proposed method successfully identified and recommended optimal topologies based on specific requirements. Finally, experimental results are presented, demonstrating the capability, efficiency, and cost-effectiveness of the proposed method.

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Chinese Journal of Electrical Engineering
Pages 235-256

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Cite this article:
Liang Y, Li H, Wei M, et al. An Automatic Optimization Topology Recommendation Method for DC-DC Converters. Chinese Journal of Electrical Engineering, 2025, 11(2): 235-256. https://doi.org/10.23919/CJEE.2025.000124

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Received: 21 September 2024
Revised: 16 December 2024
Accepted: 22 January 2025
Published: 30 June 2025
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