基于自适应滤波的锂电池隔膜卷绕系统张力估计

Tension Estimate for Lithium Battery DiaphragmWinding Systems Based on Adaptive Extended Kalman Filter

锂电池隔膜卷绕系统的电机转速、放卷辊的卷材卷径和放卷张力等实时信号都带有高斯白噪声,易形成较大的滞后,从而导致控制系统的稳定性和精度降低。现以协方差匹配技术为滤波发散判据,再结合对于指数加权系数的表达式限定记忆滤波的次数,提高噪声初始值的分配权重,来保持滤波的自适应程度,提出一种基于改进型SageHusa自适应滤波估计张力的方法,实现对系统噪声协方差阵与测量噪声协方差阵的自适应变化。实验结果表明,所提出的方法不仅能更准确、稳定地估计出锂电池隔膜卷绕系统放卷张力,还能在一定范围内使其不受给定的噪声协方差阵初值影响,而且有较高的精度和较强的实时性,优于一般的扩展卡尔曼滤波算法。

The real-time signals of the motor speed of the lithium battery diaphragm winding system,the winding diameter of the unwinding roller and the unwind tension are all accompanied by Gaussian white noise,which tends to form a large lag and leads to a reduction in the stability and accuracy of the control system.In this study,a method for estimating the tension based on improved Sage-Husa adaptive filtering is proposed.In this method,the covariance matching technology is used as the filter divergence criterion.Combined with the expression of the exponential weighting coefficient to limit the number of memory filtering,the weight of the initial value of the noise is raised to maintain the degree of adaptive filtering.Finally,the adaptive change of system noise covariance matrix and measurement noise covariance matrix is realized.The experimental results show that the proposed method can estimate the unwind tension of the lithium battery diaphragm winding system more accurately and stably and eliminate the effect of the initial value of the given noise covariance matrix.It also has higher accuracy and stronger real-time performance,which is superior to the general extended Kalman filter algorithm.