The Battery State of Charge Estimation Based Weighted Least Squares Support Vector Machine

被引：0
|
作者：
Chen, Yongqiang [1 ]
Long, Bo [1 ]
Lei, Xiao [2 ]
机构：
[1] Univ Elect Sci & Technol, Sch Mechatron Engn, Chengdu, Peoples R China
[2] China Three Gorges Corp, Eelectr & Mech E&M Dept, Yichang, Peoples R China
来源：
2011 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2011年
关键词：
SOC; electric vehicles; function estimation; WLS-SVM; VEHICLES; SYSTEMS;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
A new method to estimate the battery state of charge (SOC) in electric vehicles (EV) based on support vector machine is presented. The key of the proposed method is to establish the relationship of the SOC to the battery current, voltage and temperature by using weighted least squares support vector machine (WLS-SVM). With the goal of achieving the optimal robust estimation of the SOC, the extended Huber estimation of residual is employed instead of sum of the least square of the residual in the objective function of LS-SVM. And the iterative modeling algorithm is proposed. The result shows that the proposed estimator can stimulate the battery dynamics for the accurate estimation of SOC in EV.
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页数：4
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