Component Based and Machine Learning Aided Optimal Filter Design for Full-Bridge Current Doubler Rectifier

Liu, Guihua; Chen, Yan-Bo; Gao, Yuan; Zhu, Jing; Bo-xin, Wang; Yang, Tao

doi:10.1109/iecon48115.2021.9589272

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…In traditional design method [25], firstly, select the magnetic core model based on the current. Then, the number of wire reaches N = L A L , A L is the nominal inductance coefficient of the magnetic core; under DC current, the magnetic field intensity of magnetic core is biased to H = N I l e .…”

Section: Traditional Design Methods For Inductor and Capacitor Of Con...mentioning

confidence: 99%

“…Reference [24] optimized the membership function of fuzzy controllers in the same way, which greatly improved the efficiency of energy management systems At present, the integration of SVM and ANN in the design of circuit parameters is still relatively rare. References [25,26] adopted the SVM-ANN integration method to optimize the design of filter inductance and capacitors at different frequencies and verified the advanced nature of this method through simulation experiments and hardware experiments. Inspired by these articles [25,26], this article focuses on a high-gain boost converter-synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) and uses SVM and ANN integration to optimize the design of inductance and switching frequency to achieve optimal converter mass and efficiency.…”

Section: Introductionmentioning

confidence: 96%

“…References [25,26] adopted the SVM-ANN integration method to optimize the design of filter inductance and capacitors at different frequencies and verified the advanced nature of this method through simulation experiments and hardware experiments. Inspired by these articles [25,26], this article focuses on a high-gain boost converter-synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) and uses SVM and ANN integration to optimize the design of inductance and switching frequency to achieve optimal converter mass and efficiency. Due to the use of chip capacitors in the design process, the changes in capacitor mass and power loss are relatively small and can be ignored, so there is no optimization design for capacitors.…”

Section: Introductionmentioning

confidence: 96%

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Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

Liu,

Kui,

Gao

et al. 2023

Energies

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For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method for passive components and the switching frequency of the converter to improve efficiency and power density. A machine learning-integrated optimization method is proposed to minimize the converter mass and power loss of the converter. In this method, first, a component-based automatic design model is built with embedded SLIC-QBC simulation. Then, a series of design schemes is generated within a large parameter range, and training data for machine learning are collected. Support vector machine (SVM) and artificial neural network (ANN) are used to validate the converter design scheme, where ANN establishes the mapping relationship from design parameters to optimization objectives. After the optimization, an experimental prototype is built for experimental verification. The simulation and experimental results verify the practicability of the proposed method.

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Section: Traditional Design Methods For Inductor and Capacitor Of Con...mentioning

confidence: 99%