Intelligent Computing for Extended Kalman Filtering SOC Algorithm of Lithium-Ion Battery

被引：13
|
作者：
Zhang, Lanyong [1 ,2 ,3 ]
Zhang, Lei [1 ]
Papavassiliou, Christos [2 ]
Liu, Sheng [1 ]
机构：
[1] Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China
[2] Imperial Coll, Dept Elect & Elect Engn, London SW7 2AZ, England
[3] State Key Lab Millimeter Waves, Nanjing 210096, Jiangsu, Peoples R China
来源：
WIRELESS PERSONAL COMMUNICATIONS | 2018年 / 102卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Extended Kalman filtering algorithm; The estimation of SOC; Kalman filtering correction; The Thevenin model; The battery characteristics; STATE-OF-CHARGE; ELECTRIC VEHICLES; FRAMEWORK;
D O I：
10.1007/s11277-018-5257-9
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
The accurate estimation of battery state of charge (SOC) is an important function of the battery management system, and the precise state of battery is estimated makes for the stability of the system. Based on the working characteristics of lithium-ion batteries, the article which used intelligent computing method establishes the mathematical model of the lithium-ion power battery by using the Thevenin model to accurately estimate the battery SOC. Besides, this paper adopts extended Kalman filtering algorithm based on an ampere-hour integral method and the open circuit voltage method for the estimation of battery SOC. Finally in simulation and hardware, the algorithm is verified. The simulation results show that the intelligent computing model can well reflect dynamic and static characteristics of the battery and the extended Kalman filtering algorithm has better estimation accuracy and can meet the system requirements. Similar to the simulation, hardware experiments also show that the algorithm has the high precision and a good anti-jamming ability.
引用
收藏
页码：2063 / 2076
页数：14
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