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文章摘要
电动汽车动力电池荷电状态估计方法探讨
Discussion on Methods of State of Charge Estimation for Electric Vehicle Power Batteries
Received:April 09, 2014  Revised:April 14, 2014
DOI:
中文关键词: 电动汽车  动力电池  荷电状态SOC(state of charge)  估计方法
英文关键词: electric  vehicle, power  battery, state  of charge, estimation  method
基金项目:国家自然科学基金(51067002);广西科技攻关重大专项(桂科重1348003-8);广西制造系统与先进制造技术重点实验室主任课题(13-051-09-002Z)。
Author NameAffiliationE-mail
ZENG Qiu-yong Department of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guangxi Guilin,541004 429498241@qq.com 
FAN Xing-ming Department of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guangxi Guilin,541004  
ZHANG Xin* Department of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guangxi Guilin,541004 zhangxin_wt@163.com 
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中文摘要:
      准确估计电池荷电状态(SOC)是电动汽车电池管理的重要内容,SOC的准确评估对延长电池寿命和提高电动汽车整车性能具有重要意义。各国研究人员对电池SOC估计方法进行大量研究,先后提出了多种估计方法。本文介绍了电池SOC的定义及其主要影响因素,根据电池SOC估计方法的特点,按离线和在线方法对SOC估计方法进行总结和介绍,并比较了各方法的特点及实用效果。最后展望了电池SOC估计方法的两个潜在发展方向,即基于电池模型的非线性滤波方法和具有自学习能力的智能方法,为今后深入研究动力电池SOC估计方法提供借鉴。
英文摘要:
      The accurate estimation of state of charge (SOC) for battery is an important content of electric vehicle battery management, and it is also significant for extension of battery lifetime and improvement of electric vehicle performance. A lot of research on SOC estimation methods is done by researchers all over the world, and kinds of estimation methods are proposed. In this paper, the definition of battery SOC and its main influencing factors are introduced, according to the characteristics of estimation methods for battery SOC, the SOC estimation methods are summarized and recommended based on offline and online methods, the characteristics and practical effects of each method are compared. In order to give reference to the further research about SOC estimation methods for power battery in future, two potential development directions of battery SOC estimation method are prospected, they are the nonlinear filtering method based on the cell model and the intelligent method with self-learning ability.
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