Reinforcement Learning Strategies for Parameter Design of Bidirectional Cllc Resonant Converters With Ultrawide Voltage Range

Yang X, Kruse G, Schwanninger R, Coelho R, Wunder B, Rosskopf A, Lorentz V, März M (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: IEEE

City/Town: New York City

Pages Range: 1-7

Conference Proceedings Title: 2024 IEEE Design Methodologies Conference (DMC)

Event location: Grenoble FR

DOI: 10.1109/DMC62632.2024.10812123

Abstract

In this paper, a design methodology of the resonant tank for a bidirectional CLLC resonant converter based on massively parallelized circuit simulations is proposed to achieve ultrawide input/output voltage range. A simplified closed-loop model of the CLLC converter with variable circuit parameters is built. To investigate potential configurations of the circuit parameters, various sampling methods and optimization techniques are used to generate diverse sets of parameters: Grid Search (GS), Tree-structured Parzen Estimator (TPE), Covariance-Matrix Adaptation Evolution Strategy (CMA-ES), standard Reinforcement Learning (RL), Diversity-driven Reinforcement Learning (DdRL). Appropriate criteria have been added to greatly narrow down the plausible sampled design configurations which can be used for further detailed analysis. Together with the conventional design method following the design guideline, the design performances are analyzed and compared. The sampling results indicate the RL-based algorithms can obtain the comparable number of effective designs as GS and have much better diversity at the same time, which demonstrates the RL-based design methods can effectively help power electronic engineers design the resonant tank parameters of the CLLC converter.

Authors with CRIS profile

Raffael Schwanninger Lehrstuhl für Leistungselektronik (LEE) Martin März Lehrstuhl für Leistungselektronik (LEE)

Involved external institutions

Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE) Universität Bayreuth DE Germany (DE)

How to cite

APA:

Yang, X., Kruse, G., Schwanninger, R., Coelho, R., Wunder, B., Rosskopf, A.,... März, M. (2024). Reinforcement Learning Strategies for Parameter Design of Bidirectional Cllc Resonant Converters With Ultrawide Voltage Range. In 2024 IEEE Design Methodologies Conference (DMC) (pp. 1-7). Grenoble, FR: New York City: IEEE.

MLA:

Yang, Xiaotian, et al. "Reinforcement Learning Strategies for Parameter Design of Bidirectional Cllc Resonant Converters With Ultrawide Voltage Range." Proceedings of the 2024 IEEE Design Methodologies Conference (DMC), Grenoble New York City: IEEE, 2024. 1-7.

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