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Multi-objectives optimization of parameter design for LLC converter based on data-driven surrogate model
IET Power Electronics ( IF 1.7 ) Pub Date : 2023-05-24 , DOI: 10.1049/pel2.12525
Buxiang Zhou 1 , Miao Zhang 1 , Huan Luo 1 , Yiwei Qiu 1 , Shi Chen 1 , Tianlei Zang 1 , Yi Zhou 1 , Xiang Zhou 2
Affiliation  

Due to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi-objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root-mean-square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non-dominated sorting genetic algorithm (NSGA-II) is utilized to deal with these multi-objective formulations. In order to reduce the computation complexity, a data-driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA-II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%.

中文翻译:

基于数据驱动代理模型的LLC变流器参数设计多目标优化

由于计算复杂度高,传统的LLC变换器设计方法通常考虑的性能种类有限,而且寻找最优参数方案的过程是离散的,可能会导致遗漏真正的最优解。为了解决这些问题,本文提出了LLC变换器的多目标优化设计方法。根据数值计算方法,在工作模式、开关频率、零电压开关(ZVS)和谐振电容器的电压应力的约束下,建立谐振参数的封闭参数空间。此外,选择谐振电流和次级电流的均方根(RMS)值以及变压器的铁芯损耗作为优化目标。非支配排序遗传算法 (NSGA-II) 用于处理这些多目标公式。为了降低计算复杂度,选择一种称为自适应多项式逼近(APA)的数据驱动方法来获得参数空间和优化目标的显式表达式。然后,将这个简化的代理模型代入NSGA-II算法,得到最优参数方案。综合对比分析,搭建了400 W/48 V实验样机验证理论分析,表明所提方法最高效率可达95%。被选择以获得参数空间和优化目标的显式表达式。然后,将这个简化的代理模型代入NSGA-II算法,得到最优参数方案。综合对比分析,搭建了400 W/48 V实验样机验证理论分析,表明所提方法最高效率可达95%。被选择以获得参数空间和优化目标的显式表达式。然后,将这个简化的代理模型代入NSGA-II算法,得到最优参数方案。综合对比分析,搭建了400 W/48 V实验样机验证理论分析,表明所提方法最高效率可达95%。
更新日期:2023-05-26
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