基于传感时滞数据多胞滤波的锂电池运行状态估计与硬件验证
State Estimation of Lithium Battery SoC and Its Hardware Verification Based on Senor Delaying Zonotopic Filtering Algorithm

基于多胞滤波状态估计理论，提出一种含有传感时滞的多胞滤波算法，并将其应用于锂电池运行时的状态估计与监测中。首先基于锂电池的Thevenin等效电路模型，构建状态空间模型。在此基础上构建状态预测集，并将其与含时滞传感测量数据结合，基于多胞体的闵可夫斯基性质，构建含参估计集。并通过F半径多胞收缩模型，对滤波系数进行最优化，从而获得锂电池SoC的最优估计。通过锂电池充放电实验平台的实验验证，所提出的算法相较于传统ZKF算法具有更高的精度，保守性明显下降。
Based on the zonotopic filtering estimation theory, a zonotopic filtering algorithm with sensor time delay is proposed and applied to the state estimation and monitoring of lithium batteries. Firstly, a state space model is constructed based on the Thevenin circuit model of lithium batteries. On this basis, a state prediction set is constructed and combined with the time-delayed sensor measurement data. Based on the Minkowski property of zonotopes, an estimation set is constructed. By applying the F-radius contraction model, the filtering coefficient is optimized to obtain the optimal estimation of the SoC. Experimental verification on the lithium battery charge and discharge experimental platform shows that the proposed algorithm has higher accuracy and significantly reduced conservativeness compared with the conventional ZKF algorithm.