基于卡尔曼滤波的动力电池荷电状态的估算  

Estimation of Power Battery SOC Based on Kalman Filtering

作　　者：赵钢[1] 翟世欢[1] 黄孙伟[1] 

机构地区：[1]天津理工大学,天津300384

出　　处：《华东电力》2013年第5期973-976,共4页East China Electric Power

基　　金：国家高技术研究发展计划(2011AA11A279)~~

摘　　要：动力电池荷电状态(SOC)是电动汽车行驶及充电过程中重要的信息之一,能够为驾驶者提供电池剩余电量以及续驶里程等信息。完成了基于动力电池Thevenin模型的扩展卡尔曼滤波法SOC估算算法的研究与设计。通过电池的HPPC实验,识别电池模型参数,并在MATLAB中进行仿真验证,介绍了实验结果。State of charge （SOC） of power battery is one of the most important information during EV driving and charging, which can inform drivers of the remaining battery power and continued driving mileage. This thesis com- pletes the research and design of extended Kalman filtering SOC estimation based on power battery＇s Thevenin model. The battery model parameters are identified through battery HPPC experiment, and simulated in MATLAB. The ex- perimental outcome is finally discussed.

关 键 词：动力电池 Thevenin模型 卡尔曼滤波 SOC 

分 类 号：U482.3[交通运输工程—载运工具运用工程]