颗粒过滤器
电池(电)
支持向量机
核(代数)
锂离子电池
控制理论(社会学)
希尔伯特-黄变换
计算机科学
工程类
算法
卡尔曼滤波器
滤波器(信号处理)
人工智能
功率(物理)
数学
量子力学
计算机视觉
组合数学
物理
控制(管理)
作者
Xiaofei Sun,Kai Zhong,Min Han
出处
期刊:Measurement
[Elsevier]
日期:2021-01-01
卷期号:170: 108679-108679
被引量:42
标识
DOI:10.1016/j.measurement.2020.108679
摘要
To make up the deficiencies of single methods in lithium-ion battery state of health (SOH) and remaining useful life (RUL) estimation, this paper presents a novel hybrid method using unscented particle filter (UPF) with optimized multiple kernel relevance vector machine (OMKRVM). Firstly, the errors between the initial estimation by UPF and the actual capacity are obtained. After that, the residuals are reconstructed by complementary ensemble empirical mode decomposition (CEEMD) to reduce interference. In addition, OMKRVM is adopted to provide multiple predictive abilities, and kernel parameters and weights of OMKRVM are yielded by the grid search. Finally, the initial estimation is corrected by the predicted residuals using OMKRVM to further improve prediction performance. The new method (UPF-OMKRVM) is compared with existing methods in predicting the degradation process of lithium-ion battery. The experimental results show that the UPF-OMKRVM has high prediction accuracy in lithium-ion battery SOH and RUL estimation.
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