优化电池模型的自适应Sigma卡尔曼荷电状态估算

采用数学模型法对磷酸铁锂电池进行非线性建模,优化了状态模型及观测模型。模型考虑了充放电倍率、温度、老化循环寿命等因素,对电池松弛效应及极化现象影响进行建模补偿,提高了电池建模的准确度,降低了不同条件下因电池模型造成电池荷电状态(SOC)估算的误差影响。在电池模型参数辨识基础上,提出采样自适应Sigma卡尔曼算法构建SOC估算模型,按照非线性模型对状态变量的分布构建Sigma采样序列,采用模型输出残差更新噪声协方差,赋予Sigma采样序列最优估计及噪声的权值,并实现误差量的实时更新,降低计算复杂度。通过持续大电流、间断电流、变电流放电及充电实验条件下的SOC估算对比实验,验证了自适应Sigma卡尔曼算法快速收敛性,数学描述更准确,具备较高的SOC的观测准确度。

New sigma point filtering algorithms for nonlinear stochastic systems with correlated noises

New sigma point filtering algorithms, including the unscented Kalman filter （UKF） and the divided difference filter （DDF）, are designed to solve the nonlinear filtering problem under the condition of correlated noises. Based on the minimum mean square error estimation theory, the nonlinear optimal predictive and correction recursive formulas under the hypothesis that the input noise is correlated with the measurement noise are derived and can be described in a unified framework. Then, UKF and DDF with correlated noises are proposed on the basis of approximation of the posterior mean and covariance in the unified framework by using unscented transformation and second order Stirling＇s interpolation. The proposed UKF and DDF with correlated noises break through the limitation that input noise and measurement noise must be assumed to be uneorrelated in standard UKF and DDF. Two simulation examples show the effectiveness and feasibility of new algorithms for dealing with nonlinear filtering issue with correlated noises.

我国中小学生超重、肥胖检出率变化趋势的拓扑学特征

引入韦伯-费希纳定律、时间自相似和Sigma算法对2010和2014两年全国7~18岁学生超重、肥胖检出率的调研数据进行再分析。结果发现:(1)从2010到2014年,我国7~18岁学生超重、肥胖检出率在性别上无拓扑差异,即中小学男女生的超重、肥胖检出率均呈自限性过程;(2)从2010到2014年,我国7~18岁学生超重、肥胖检出率在城乡上有拓扑差异,主要表现为乡村好于城市;(3)从2010到2014年,我国7~18岁学生超重、肥胖检出率在时间上产生了拓扑相变,主要表现为城市男生的超重检出率和城市女生的超重、肥胖检出率均呈上升趋势,而城市男生和乡村男女生的肥胖检出率均维持不变。结果表明当前我国城乡中小学生群体中只有城市女生肥胖检出率呈持续上升趋势。