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Li-ion battery SOC estimation based on EKF algorithm
Li B(李博); Yuan XQ(袁学庆); Zhao L(赵林)
作者部门装备制造技术研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码1584-1588
收录类别EI ; CPCI(ISTP)
EI收录号20161402187783
WOS记录号WOS:000380502300289
产权排序1
ISSN号2379-7711
ISBN号978-1-4799-8730-6
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 EKF is improved greatly, which is significant for the performance of BMS and the host machine.
语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/17669
专题装备制造技术研究室
作者单位Equipment Manufacturing Technology Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
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GB/T 7714
Li B,Yuan XQ,Zhao L. Li-ion battery SOC estimation based on EKF algorithm[C]. Piscataway, NJ, USA:IEEE,2015:1584-1588.
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