基于扩展卡尔曼滤波的锂离子电池寿命预测方法  

Lithium-ion Battery Life Prediction Based on Modified Extend Kalman Filter

作　　者：王海霞 李凯勇[1] Wang Haixia;Li Kaiyong(School of Physics and Electronic Engineering,Qinghai Nationalities University,Xining 810007,China)

机构地区：[1]青海民族大学物理与电子信息工程学院

出　　处：《计算机测量与控制》2019年第8期271-275,共5页Computer Measurement &Control

基　　金：青海湟中堆绣艺术图像数字保护资源库开发（2019-GX-170）

摘　　要：锂离子电池寿命预测是掌握电源性能衰退趋势的重要手段,已成为电子系统健康管理领域的研究热点;针对锂离子电池的寿命预测问题,基于NASA艾姆斯中心的锂离子电池地面试验采集的数据,将扩展卡尔曼滤波(EKF)算法应用于锂离子电池寿命预测过程中,并针对预测过程中存在的问题,采用最优Loess平滑原理进行改进,从而提高了预测的稳定性和精确性;实验结果表明,提出的预测方法能够有效地用于锂离子电池寿命预测中,在工程应用方面具有较高的实用价值。Life prediction for Lithium-ion batteries is an important means to master the decline tendency of power performance,and the life prediction methods of Lithium-ion batteries have become the research hotspot in the electronic system field of Prognostic and Health Management.Aiming at the life prediction of Lithium-ion battery,based on the data collected from the Lithium-ion battery ground test of NASA Ames center,the extended Kalman filter(EKF)algorithm is proposed and applied to the prediction process of Lithium-ion life,and it is modified by using the optimal Loess smoothing principle,which improves the stability and accuracy of prediction.The experimental results show that the proposed prediction method can be effectively used in the life prediction of Lithium-ion batteries,and has high practical value in engineering application..

关 键 词：扩展卡尔曼滤波 最优局部加权回归平滑 锂离子电池 寿命预测 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]