Li-ion Battery SOC Estimation Based on EKF Algorithm

被引：0

作者：

Li Bo ^{[1
]}

Yuan Xueqing ^{[1
]}

Zhao Lin ^{[1
]}

机构：

[1] Chinese Acad Sci, Shenyang Inst Automat, Equipment Mfg Technol Lab, Shenyang, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2015年

关键词：

SOC estimation; Extended Kalman Filter (EKF); Li-ion battery;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Li ion battery is more and more popular in aviation area in recent years for its high energy density no memory characteristic and long cycle life. The currently used state of charge (SOC) estimation methods based on extended Kalman filter (EKF) doesn't have a very good accuracy due to the modeling error, which could influence the performance of the battery management system (BMS) and the control of the host machine. Considering about this, 7 ranks Thevenin model is adopted in this article which has a good precision and exponential function and logarithmic function are introduced to increase modeling precision. In the experiment, the max estimation error based on the improved model declined 71.59% comparing to the original model. The experiment result shows through improving the battery model the SOC estimation accuracy basing on EKE is improved greatly, which is significant for the performance of BMS and the host machine

引用

页码：1584 / 1588

页数：5

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