Remaining Useful Life Prediction of Lithium-Ion Battery Based on Gauss-Hermite Particle Filter
Remaining Useful Life Prediction of Lithium-Ion Battery Based on Gauss-Hermite Particle Filter
复制标题
基于高斯-厄米粒子滤波器的锂离子电池剩余寿命预测
DOI：
10.1109/tcst.2018.2819965
复制
发表时间：
2019-07-01
影响因子：
4.8
通讯作者：
Chen, Hong
中科院分区：
文献类型：
--
作者：
Ma, Yan;Chen, Yang;Chen, Hong
本文提出了一种基于锂离子电池 (LIB) 非线性和非高斯系统中高斯-埃尔米特粒子滤波器 (GHPF) 的剩余使用寿命 (RUL) 预测方法。摘要：为了提高健康状态（SOH）估计的精度并降低计算复杂度，针对SOH的慢变特性和SOC的快变特性，提出了多尺度扩展卡尔曼滤波器来执行双时间尺度的充电状态（SOC）和SOH联合估计。基于SOH的估计，开发了GHPF来实时更新容量退化模型的参数并预测LIB的RUL。仿真结果表明，所提出的RUL预测方法比基于标准PF的方法具有更好的性能和更高的精度。
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 Lithiumion 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.