电池(电)
融合
算法
采样(信号处理)
计算机科学
滤波器(信号处理)
物理
语言学
哲学
功率(物理)
量子力学
计算机视觉
作者
Guangzheng Lyu,Heng Zhang,Qiang Miao
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-11
被引量:4
标识
DOI:10.1109/tim.2023.3260277
摘要
Remaining useful life (RUL) prediction of lithium-ion battery in early-cycle stage is of great significance to improve battery performance and reduce losses caused by failure. Due to complex degradation mechanism and insufficient data in early-cycle stage, current RUL prediction schemes for lithium-ion battery have trouble obtaining degradation characteristics to achieve satisfactory prediction accuracy. Aiming at this problem, this paper proposes an RUL prediction method of lithium-ion batteries in early-cycle stage based on similar sample fusion under Lebesgue sampling framework. First, a novel similarity measurement index based on fusion of Pearson correlation coefficient and Euclidean distance is proposed, and the fusion parameter is optimized by jumping spider optimization algorithm. Similar samples are selected as reference for the prediction model. Then, Lebesgue sampling theory is introduced to complete data structure transformation for similar samples, so as to ensure that the fused points of different similar samples are under the same degradation state. Finally, similar sample fusion result is transformed to Riemann sampling framework and perform a linear fitting. Fitting results are used to construct a particle filter model for capacity degradation process and RUL prediction. Experimental results and comparison studies on APR18650M1A battery dataset demonstrate the effectiveness of the proposed method.
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