Research on SOC estimation based on second-order RC model
Research on SOC estimation based on second-order RC model
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DOI：
10.11591/telkomnika.v10i7.1561
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发表时间：
2012-11
影响因子：
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通讯作者：
Tiezhou Wu;Lunan Liu;Qingmei Xiao;Q. Cao;Xieyang Wang
中科院分区：
文献类型：
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作者：
Tiezhou Wu;Lunan Liu;Qingmei Xiao;Q. Cao;Xieyang Wang
电池荷电状态(SOC)的估计精度对混合动力汽车(HEV)的发展起着至关重要的作用。准确估计电池的SOC可以防止电池过度充放电，从而提高电池的使用寿命。虽然卡尔曼滤波算法对于电流变化较快的混合动力汽车应用有较好的估计精度，但卡尔曼滤波算法对电池模型的依赖程度较高。换句话说，电池SOC估计的准确性需要精确的电池模型。此外，混合动力汽车在运行时，电池管理系统采集数据过程中产生的噪声的统计特征是未知的。这会导致卡尔曼滤波算法的估计性能降低甚至扩散。针对这一问题，本文在二阶RC电池模型的基础上，采用自适应卡尔曼滤波算法估计电池的SOC。通过MatLab仿真分析，在一定程度上提高了电池SOC的估计精度。DOI：http://dx.doi.org/10.11591/telkomnika.v10i7.1561全文：
The estimation accuracy of batteries’ State of Charge (SOC) plays an important role in the development of hybrid electric vehicle (HEV). Accurate estimation of SOC can prevent battery from overly charging and discharging, so the lifetime of batteries will be increased. Although Kalman filter algorithm has better estimation accuracy for HEV application in which the current changes fast, Kalman filter algorithm deeply relies on the battery model. In other words, the accuracy of batteries’ SOC estimation needs precise batteries models. Besides, when the HEV is running, the statistical characteristics of noise produced in the course of the battery management system collecting data are unknown. This can cause estimated performance of Kalman filter algorithm to decrease even diffuse. To solve the problem, adaptive Kalman filter algorithm is adopted to estimate battery SOC based on the second order RC battery model in this paper. Through MATLAB simulation analysis, the estimation accuracy of battery SOC is improved to some extent. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1561 Full Text: PDF