Author
Yan Gao
Bio: Yan Gao is an academic researcher from Shanghai University of Engineering Sciences. The author has contributed to research in topics: Fractional Brownian motion & Detrended fluctuation analysis. The author has co-authored 1 publications.
Papers
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TL;DR: In this article, an iterative model of the generalized Cauchy process with LRD characteristics is proposed for the remaining useful life (RUL) prediction of lithium-ion batteries.
13 citations
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TL;DR: In this article , a hybrid method is proposed for the accurate prediction of lithium-ion batteries capacity considering regeneration, where the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the raw capacity signal into the global degradation trend components and the local fluctuation components.
10 citations
7 citations
TL;DR: In this paper , a least-squares generative adversarial network with the gated recurrent unit as the generator and multi-layer perceptron as the discriminator is used to predict the remaining useful life of lithium-ion batteries.
7 citations
TL;DR: A new method for the RUL prediction of LIBs is developed, which is combined with complete ensemble empirical mode decomposition with adaptive noise, whale optimization algorithm, and support vector regression to show that the accurate prediction of the proposed method is superior to the two methods.
Abstract: The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is a crucial element of battery health management. The accurate prediction of RUL enables the maintenance and replacement of batteries with potential safety hazards, which ensures safe and stable battery operation. This paper develops a new method for the RUL prediction of LIBs, which is combined with complete ensemble empirical mode decomposition with adaptive noise (CEEDMAN), whale optimization algorithm (WOA), and support vector regression (SVR). Firstly, the CEEMDAN is employed to perform noise reduction in battery capacity data for prediction accuracy improvement. Then, an SVR model optimized by the WOA is proposed to predict the RUL. Finally, the public battery datasets are selected to validate the performance of the CEEMDAN-WOA-SVR method. The RUL prediction accuracy of the CEEMDAN-WOA-SVR method is better than the WOA-SVR method. In addition, a comparison is made between the proposed method and the existing methods (artificial bee colony algorithm-SVR method, ensemble empirical mode decomposition-gray wolf optimization-SVR method). The results show that the accurate prediction of the proposed method is superior to the two methods.
4 citations
TL;DR: Wang et al. as discussed by the authors proposed a novel model named Poly-Cell Long Short-Term Memory Network, which adds a hierarchical division unit and a poly-cell unit to improve the prediction accuracy of RUL.
3 citations