基于自适应滤波的电动汽车动力电池荷电状态估计方法  

Self-adaptive Filtering Based State of Charge Estimation Method for Electric Vehicle Batteries

作　　者：王军平[1] 曹秉刚[1] 陈全世[2] 

机构地区：[1]西安交通大学机械工程学院,西安710049 [2]清华大学汽车安全与节能国家重点实验室,北京100084

出　　处：《机械工程学报》2008年第5期76-79,共4页Journal of Mechanical Engineering

基　　金：国家高技术研究发展计划(863计划，2003AA501100);汽车安全与节能国家重点实验室开放基金(KF2007-04)资助项目。

摘　　要：基于卡尔曼滤波法的电池组荷电状态(State of charge,SOC)估计方法适合于电流变化比较剧烈的混合动力汽车中电池组的SOC估计,但由于电池模型以及系统噪声、量测噪声统计特性的不确定性,容易引起滤波发散。研究联邦城市行驶工况,并对电池组进行充放电试验,建立单变量的镍氢电池组的状态空间模型。将SOC作为系统的状态,由于自适应滤波算法可以抑制滤波发散,基于自适应滤波算法研究镍氢电池组的SOC估计方法。台架试验表明该方法具有较高的估计精度和可靠性,计算量小,更适用于实际应用。The state of charge （SOC） estimation of battery pack based on Kalman filtering method is suitable for estimating the SOC for hybrid electric vehicles where the current fluctuates drastically. However, the uncertainty due to battery model and statistical information of the system and measurement noise will result in filtering divergence. The self-adaptive filtering method can deal with this problem. The federal urban driving schedule is studied and the battery pack is charged and discharged, then a battery＇s state space model with single state is built. Then the SOC is taken as a state of the system, the SOC is estimated based on the self-adaptive filtering method. The bench test results show that this method is with high accuracy and reliability of estimation, less computation amount, and is quite suitable for practical application.

关 键 词：镍氢电池组 荷电状态 自适应滤波 

分 类 号：TM912.1[电气工程—电力电子与电力传动]