颗粒过滤器
非线性系统
扩展卡尔曼滤波器
赫米特多项式
卡尔曼滤波器
荷电状态
控制理论(社会学)
健康状况
高斯分布
集合卡尔曼滤波器
电池(电)
计算机科学
电子工程
工程类
算法
数学
人工智能
物理
功率(物理)
数学分析
量子力学
控制(管理)
作者
Yan Ma,Chen Yang,Xiuwen Zhou,Hong Chen
标识
DOI:10.1109/tcst.2018.2819965
摘要
This brief proposes a prediction method of remaining useful life (RUL) based on Gauss-Hermite particle filter (GHPF) in nonlinear and non-Gaussian systems of Lithium-ion batteries (LIBs). In this brief, to improve the accuracy and reduce the computational complexity of the estimation of state of health (SOH), multiscale extended Kalman filter is proposed to execute state of charge (SOC) and SOH joint estimation with dual time scales because of the slow-varying characteristic of SOH and fast-varying characteristic of SOC. Based on the estimation of SOH, a GHPF is developed to update the parameters of the capacity degradation model in real time and predict the RUL of LIBs. The simulation results show that the proposed prediction method of RUL has a better performance and higher precision than the method based on standard PF.
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