%0 Journal Article
%T 基于UKF和AH法的磁悬浮人工心脏泵用锂电池SOC估计复合算法<br>A compound algorithm for SOC estimation of lithium batteries for magnetic suspension artificial heart pump based on UKF and AH
%A 董满
%A 刘淑琴&lt
%A br&gt
%A DONG Man
%A LIU Shuqin
%J 山东大学学报（工学版）
%D 2018
%R 10.6040/j.issn.1672-3961.0.2017.606
%X 摘要： 针对锂电池的非线性特性,提出电池状态模型在不同循环次数、不同温度下的具体改进方法;提出安时积分法和无迹卡尔曼滤波算法结合的锂电池荷电状态(state of charge, SOC)复合估计算法,分析新算法的收敛速度、估计精度以及算法复杂度。试验表明,这种复合算法复杂度低,精度高,能快速实现锂电池SOC的准确估计,估算误差为4.036 2%,适合实时在线计算。<br>Abstract: Based on the nonlinear system, an improved state model was proposed in different cycles and different temperatures; based on unscented Kalman filter and ampere-hour(AH)integral method, a compound algorithm and its concrete implementation steps were proposed for SOC estimation; the convergence speed, estimation accuracy and complexity of the new algorithm were analyzed. The experimental results showed that the complexity of this algorithm was low and the accurate estimation of SOC could be realized quickly, the estimation error was 4.036 2%, and was suitable for real-time on-line computation
%K 无迹卡尔曼滤波
%K 安时积分法
%K 锂电池荷电状态
%K 状态模型
%K 复合算法
%K &lt
%K br&gt
%K charge state of lithium batteries
%K AH integral method
%K state model
%K unscented Kalman filter
%K compound algorithm
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2017.606