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电动汽车蓄电池荷电状态的卡尔曼滤波估计
引用本文:杨朔,何莉萍,钟志华. 电动汽车蓄电池荷电状态的卡尔曼滤波估计[J]. 贵州工业大学学报(自然科学版), 2004, 33(1): 99-102
作者姓名:杨朔  何莉萍  钟志华
作者单位:湖南大学,机械与汽车工程学院,湖南,长沙,410082
基金项目:德国国际合作局合作项目(CHN01/602)
摘    要:对电动汽车剩余里程的预测需要一个准确的蓄电池荷电状态(SOC)值,但目前任何方法都不能精确地测量蓄电池的剩余电量,以计算电动汽车蓄电池的荷电状态(SOC),在对目前常用的剩余电量计量方法分析的基础上,提出了一种基于电流的测量,然后利用卡尔曼滤波估计递推算法对蓄电池SOC进行实时估计,并在MATLB下进行了仿真。

关 键 词:电动汽车 蓄电池 荷电状态 卡尔曼滤波估计 内阻法
文章编号:1009-0193(2004)01-0099-04

A Real-time the state of Charge of Storage Battery Estimation Based on the Kalman Filtering Theory for Electric Vehicle
YANG Shuo,HE Li-ping,ZHONG Zhi-hua. A Real-time the state of Charge of Storage Battery Estimation Based on the Kalman Filtering Theory for Electric Vehicle[J]. Journal of Guizhou University of Technology(Natural Science Edition), 2004, 33(1): 99-102
Authors:YANG Shuo  HE Li-ping  ZHONG Zhi-hua
Abstract:The accurate state of charge (SOC) is required for the battery of electric vehicles. The various estimation methods for the SOC of the storage battery have been proposed. However, any method can not accurately predict the residual capacity. A new estimation method of SOC on the storage battery is proposed in the paper. This method is based on the current method and utilize Kalman filter to real time estimate the SOC of battery. This method can minimize the estimation error. We imitate the SOC measurement system by the MATLAB simulation tool.
Keywords:electric vehicle  storage battery  state-of-charge  Kalman filter  
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