降级(电信)
希尔伯特-黄变换
克里金
高斯分布
锂(药物)
蒙特卡罗方法
计算机科学
算法
生物系统
环境科学
材料科学
工程类
统计
数学
机器学习
化学
电子工程
能量(信号处理)
医学
计算化学
生物
内分泌学
作者
Haipeng Pan,Chengte Chen,Minming Gu
出处
期刊:Energies
[Multidisciplinary Digital Publishing Institute]
日期:2022-03-29
卷期号:15 (7): 2498-2498
被引量:14
摘要
Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is important for electronic equipment. A new algorithm is proposed to aim at the nonlinear degradation caused by capacity regeneration and random fluctuations. Firstly, the health state degradation curve of LIBs is divided into the normal degradation trend part, capacity regeneration part, and random fluctuation part. Secondly, the capacity degradation curve of LIBs is decomposed by the empirical mode decomposition (EMD) to obtain the known long-term degradation trend part of LIBs. Then, the long short-term memory (LSTM) neural network is used to predict the future normal degradation trend part based on the known long-term degradation trend part of LIBs. In addition, the LIBs’ state of health (SOH), the initial state of charge (SOC), and the rest time are taken as the inputs of Gaussian process regression (GPR) to predict the LIBs’ capacity regeneration part. After that, random numbers obeying the Stable distribution are generated as the random fluctuation part of LIBs. Finally, the Monte Carlo simulation is used to predict the probability density distribution of the RUL of LIBs. The paper is verified by the LIBs’ public dataset provided by the University of Maryland. The experimental results show that the predicted RMSE of the proposed method is lower than 0.6%.
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