Machine Learning Methods for Feedforward Power Flow Control of Multi-Active-Bridge Converters

Mian Liao, Haoran Li, Ping Wang, Tanuj Sen, Yenan Chen, Minjie Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Controlling the multiway power flow in a multi-active-bridge (MAB) converter is important for achieving high performance and sophisticated functions. Traditional feedforward methods for MAB converter control rely on precise lumped circuit models. This article presents a machine learning (ML) method for the feedforward power flow control of an MAB converter without a precise circuit model. A feedforward neural network was developed to capture the nonlinear characteristics and predict the phases needed to achieve the targeted power flow. The neural network was trained with a large amount of data, collected with a set of known phase angles. This trained network was used to predict the phases to achieve the targeted power flow. A six-port MAB converter was built and tested to validate the methodology and demonstrate the 'machine-learning-in-the-loop' implementation. Transfer learning was proven to be effective in reducing the size of the training data needed to obtain an accurate ML model. ML-based feedforward power flow control can achieve comparable accuracy as traditional model-based methods and can function without a precise lumped circuit element model of the MAB converter.

Original languageEnglish (US)
Pages (from-to)1692-1707
Number of pages16
JournalIEEE Transactions on Power Electronics
Issue number2
StatePublished - Feb 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Artificial intelligence (AI)
  • machine learning (ML)
  • machine-learning-in-the-loop
  • multi-active-bridge (MAB) converter
  • neural network (NN)
  • power flow control
  • transfer learning


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