A novel machine learning model for safety risk analysis in flywheel-battery hybrid energy storage system. (May 2022)

• Record Type: Journal Article
• Authors: Wen, Zhenhua; Fang, Pengya; Yin, Yibing; Królczyk, Grzegorz; Gardoni, Paolo; Li, Zhixiong
• Is Part Of: Journal of energy storage. Volume 49(2022)
• Publication Date: 2022-05
• Subjects: Flywheel energy storage system -- Electric vehicles -- Machine learning -- Remaining life prediction -- Acronyms: Abbreviations: FESS: flywheel energy storage system -- RUL: remaining useful life -- PCA: principle component analysis -- EMD: empirical mode decomposition: RMSE: root mean square error -- HI: health indicator; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.104072
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Provide a novel method for intelligent maintenance strategy and energy management based on machine learning. A new method for the construction of a health indicator for a bearing, the feature extraction, the feature selection and the feature fusion are included in the proposed method. The trend content extraction of the health indicator is achieved by the EMD method. A prediction method based on the Kriging model-based is proposed to predict the remaining useful life, and the feasibility and superiority of the prediction method proposed are verified via experiments. Abstract: 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.