A novel machine learning model for safety risk analysis in flywheel-battery hybrid energy storage system

Zhenhua Wen, Pengya Fang, Yibing Yin, Grzegorz Królczyk, Paolo Gardoni, Zhixiong Li

Research output: Contribution to journalArticlepeer-review


Flywheel energy storage system (FESS) has been regarded as the most promising hybrid storage technique to manage the battery charging process of electric vehicles. Thanks to properly regulating with the FESS, the battery life can be significantly prolonged. In order to ensure the safety of the hybrid storage system, it is imperative to monitor the mechanical operation condition of the FESS. Because the rolling bearing is a critical mechanical component in the FESS, the performance degradation monitoring and remaining useful life (RUL) prediction of the rolling bearing must be performed. This paper proposes a new machine learning method for the construction of a health indicator to quantitatively evaluate the bearing health status. In this new method, the original feature set is firstly selected through three feature evaluation indicators and the principle component analysis (PCA) is employed to fuse the original feature set as a new health indicator. Then the primary trend of the health indicator is extracted by the empirical mode decomposition (EMD); and the Kriging model-based prediction method is proposed to predict the bearing RUL. The feasibility and superiority of the proposed method is verified through experimental test and analysis result shows that the root mean square error (RMSE) of the prediction is as small as 0.0425.

Original languageEnglish (US)
Article number104072
JournalJournal of Energy Storage
StatePublished - May 2022


  • Acronyms: Abbreviations: FESS: flywheel energy storage system
  • EMD: empirical mode decomposition: RMSE: root mean square error
  • Electric vehicles
  • Flywheel energy storage system
  • HI: health indicator
  • Machine learning
  • PCA: principle component analysis
  • RUL: remaining useful life
  • Remaining life prediction

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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