Generative Physics Informed Machine Learning Method for DC-Link Capacitance Estimation

Tianhao Qie, Xinan Zhang*, Chaoqun Xiang, Shuai Zhao, Chaoqiang Jiang, Herbert H.C. Iu, Tyrone Fernando

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This article proposes an innovative generative physics-informed machine learning (GPIML) method for the estimation of dc-link capacitance during the pre-charging process of the vehicular power systems, which contributes to greatly enhancing the reliability of electrified transportation. Different from the other machine learning-based estimation approaches, the proposed method produces highly accurate results using small input experimental dataset. To enable sufficient neural network training, diffusion algorithm is first adopted in the proposed method to augment the training data based on small input dataset. Then, the augmented data is fed to a physics-informed long short-term memory (PILSTM) algorithm to estimate the dc-link capacitance. Superior accuracy and strong robustness to measurement noises are achieved. The effectiveness of the proposed method is validated through experimental studies.

Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
ISSN0278-0046
DOIs
Publication statusAccepted/In press - 2025

Bibliographical note

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

  • Capacitance estimation
  • dc-link capacitor
  • electrified transportation
  • generative machine learning
  • physic informed machine learning

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