A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

被引：31

作者：

Chen, Zheng ^{[1
]}

Li, Xiaoyu ^{[1
]}

Shen, Jiangwei ^{[1
]}

Yan, Wensheng ^{[1
]}

Xiao, Renxin ^{[1
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming 650500, Peoples R China

来源：

ENERGIES | 2016年 / 9卷 / 09期

基金：

美国国家科学基金会;

关键词：

daptive extended Kalman filter (AEKF); electric vehicle (EV); state of charge (SOC); weighed coefficients; OPEN-CIRCUIT VOLTAGE; SUPPORT VECTOR MACHINE; OF-CHARGE; MANAGEMENT-SYSTEMS; GENETIC ALGORITHM; SOC ESTIMATION; KALMAN FILTER; PARAMETER; MODEL;

D O I：

10.3390/en9090710

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：

This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

引用

页数：15

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