Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Yuan GAO, Benjamin CHEONG, Serhiy BOZHKO, Pat WHEELER, Chris GERADA, Tao YANG

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.

Original languageEnglish
Pages (from-to)213-228
Number of pages16
JournalChinese Journal of Aeronautics
Volume36
Issue number2
DOIs
Publication statusPublished - Feb 2023
Externally publishedYes

Keywords

  • Artificial Neural Network (ANN)
  • Design and Optimization
  • Machine Learning (ML)
  • More-Electric Aircraft (MEA)
  • Motor drive
  • Permanent Magnet Synchronous Motor (PMSM)
  • Search Algorithm
  • Surrogate Algorithm

ASJC Scopus subject areas

  • Aerospace Engineering
  • Mechanical Engineering

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