摘要
为了实时、准确地估计矿用电池SOC值,通过采用加权统计线性回归法实现模型函数线性化,将采样点卡尔曼滤波技术应用到矿用电池SOC估计中。针对有限的电池管理系统资源,基于电池状态观测复合模型的状态方程线性和观测方程非线性的特点,提出了将标准卡尔曼滤波和采样点卡尔曼滤波组合的非线性滤波算法;为了使得该算法具有应对突变状态的强跟踪能力和应对模型不准确的鲁棒性,引入了奇异值分解,采用特征协方差矩阵代替误差协方差矩阵,并基于强跟踪原理引入了次优渐消因子。仿真结果表明,基于改进型采样点卡尔曼滤波的矿用电池SOC估计算法兼顾估计精度和运算量,并具有跟踪突变状态和应对模型不准确的鲁棒性,完全适用于资源有限的矿用电池SOC估计;可见,该算法具有良好的实际应用价值。
In order to estimate the state of charge （SOC） for mine-used battery exactly and real-time,the weighted statistical linear regression method was used to achieve the linearization of model function,and the technique of sampling points Kalman filter was applied to the state of charge estimation for mine-used battery.Aiming at the limited resource of the battery management system,the nonlinear filtering algorithm that combined standard Kalman filter and sampling point Kalman filter was proposed based on the linear characteristic of the state equation and the nonlinear characteristic of the observation equation of the battery model.In order to achieve the strong tracing ability of forced condition and the robustness of the model inaccuracy,the singular value decomposition was introduced,and the error covariance matrix was replaced by the characteristic covariance matrix,and suboptimal fading factor was introduced based on the principle of strong tracing.The simulation results indicate that the state of charge estimation algorithm based on improved sampling point Kalman filter for mine-used battery takes the filtering accuracy and the amount of computation into account,and has the strong tracing ability to deal with the forced condition and the robustness to deal with the inaccuracy of model,and is totally applicable to the state of charge estimation for mine-used battery whose resource is limited.Therefore,the new algorithm has good practical value.
出处
《机电工程》
CAS
2014年第9期1213-1217,共5页
Journal of Mechanical & Electrical Engineering
关键词
矿用电池
荷电状态
采样点卡尔曼滤波
奇异值分解
强跟踪滤波器
mine-used battery
state of charge（SOC）
sampling point Kalman filter
singular value decomposition
strong tracing filter
