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Model predictive control of dual active bridge converter based on the lookup table method

  • Guoqing Gao
  • , Wanjun Lei
  • , Yao Cui
  • , Kai Li
  • , Xiufang Hu
  • , Jinghui Xu
  • , Gaotai Lv
  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

The dual active bridge (DAB) converters are widely used as the pivotal interface in the distributed generation and energy storage systems. In this paper, the lookup table method of the model predictive control (MPC) DAB converter is proposed based on the discrete-time model. The iterative relationship of the state variables is obtained. Part of the complicated predictive process, which hindered the discrete-time model based MPC to be widely applied to the DAB converter before, has been calculated off-line to form a lookup table. Therefore, the accurate predictive control and optimization algorithm can be realized on-line via the proposed lookup table method within one switching cycle. The experiment has also been performed to verify the theoretical analysis and demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationPEDG 2019 - 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages183-186
Number of pages4
ISBN (Electronic)9781728124551
DOIs
StatePublished - Jun 2019
Event10th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2019 - Xi'an, China
Duration: 3 Jun 20196 Jun 2019

Publication series

NamePEDG 2019 - 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems

Conference

Conference10th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2019
Country/TerritoryChina
CityXi'an
Period3/06/196/06/19

Keywords

  • Discrete-time model
  • Dual active bridge converter
  • Lookup table method
  • Model predictive control

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