【摘要】 混合动力汽车(HEV)电池管理系统工作于恶劣工况环境中,采用常规卡尔曼滤波算法估计电池荷电状态(SOC)时,量测噪声统计特性随实际工况条件剧烈变化,会导致估测不准,甚至滤波发散。采用基于模糊自适应卡尔曼滤波的氢镍动力电池SOC估算算法,通过监视理论残差与实际残差的比值,对量测噪声协方差阵进行递推在线修正,使其逐渐逼近真实噪声水平,从而使滤波器执行最优估计,提高估算精度。仿真结果表明,这种算法对随机的量测噪声具有较强的抑制能力。更多还原

【Abstract】 Hybrid electric vehicle(HEV) battery management system works in bad condition environment,when using the conventional Kalman filtering algorithms estimating SOC(state of charge) of batteries,the acute changes of the statistical properties of measurement noise will lead to inaccurate estimates,even divergent filtering.The estimation algorithm for SOC of Ni-MH batteries based on the fuzzy adaptive Kalman filter was used in this paper.By monitoring the filter residual and actual residual,this algorithm modified recursively the measurement noise covariance of Kalman filtering online using the fuzzy inference system(FIS) to make the covariance close to real measurement covariance gradually.Accordingly,the accuracy of the estimate system was improved.Matlab simulation and experiments were carried out.The comparison indicates that the fuzzy adaptive Kalman filtering performs well when disturbance happens.更多还原