Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries
Ding P. Liu X. Li H. Huang Z. Zhang K. Shao L. Abedinia O.
September 2021Elsevier Ltd
Renewable and Sustainable Energy Reviews
2021#148
It is important to know the replace time for reducing the lithium-ion battery risk and assessing its reliability. For this purpose, the remaining useful life (RUL) can play an important role in the prognostics and health management of battery to solve the inaccurate prediction issue. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning long-term dependencies among capacity degradations. In this work, a new forecasting approach is proposed based on wavelet packet decomposition, two-dimensional convolutional neural network, and adaptive multiple error corrections. In this model, the bivariate Dirichlet mixture model is considered to make the heteroscedasticity of the unpredictable residuals signal based non-parametric distribution. To show the validity of the proposed model, the experimental data are considered based on Continental Europe and NASA Ames Prognostics Center of Excellence battery datasets. The obtained numerical analysis presents an accurate forecasting model. Different comparisons with the well-known models are made to show the validity of the suggested approach, which proves the superiority and forecasting stability of the proposed model.
Lithium-ion batteries , Remaining useful life- forecasting , Two-D CNN , WPD
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Zhengzhou Key Laboratory of Agricultural Equipment Intelligent Design and Green Manufacturing, College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
Zhengzhou Normal University, Zhengzhou, 450044, China
Zhengzhou Key Laboratory of Agricultural Biomimetic Materials and Low Carbon Technology, College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
Electrical and Computer Engineering Department, Nazarbayev University, Nursultan, 010000, Kazakhstan
Zhengzhou Key Laboratory of Agricultural Equipment Intelligent Design and Green Manufacturing
Zhengzhou Normal University
Zhengzhou Key Laboratory of Agricultural Biomimetic Materials and Low Carbon Technology
Electrical and Computer Engineering Department
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