预言
计算机科学
人工神经网络
卷积神经网络
可靠性(半导体)
可靠性工程
数据挖掘
机器学习
人工智能
工程类
量子力学
物理
功率(物理)
作者
Pan Ding,Xiaojuan Liu,Huiqin Li,Zequan Huang,Ke Zhang,Liwei Shao,Oveis Abedinia
标识
DOI:10.1016/j.rser.2021.111287
摘要
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.
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