适用于风力发电的锂离子电池SOC估算策略  

SOC Estimation Strategy of Lithium-ion Batteries Suitable for Wind Power Generation

作　　者：彭昕 陆子秋 周星月 王聪 陆韦 Peng Xin;Lu Ziqiu;Zhou Xingyue;Wang Cong;Lu Wei(State Grid Shanghai Jiading Power Supply Company,Shanghai 201800,China)

机构地区：[1]国网上海嘉定供电公司,上海201800

出　　处：《电气自动化》2025年第1期30-32,共3页Electrical Automation

基　　金：国网上海市电力公司科技项目(520900230008)。

摘　　要：风力发电由于其不稳定性,一般会配备电池储能系统,因此准确可靠的电池荷电状态估算是非常重要的。卡尔曼滤波的衍生方法中,默认过程噪声和测量噪声的值是固定的,而实际上并不是。为了准确估算电池荷电状态,利用无迹卡尔曼滤波器的无迹变换来获得过程噪声协方差的统计量,并结合自协方差最小二乘法,通过计算测量更新中的相关性来估算测量噪声协方差。搭建了试验平台,证明了改进的算法可以实现更准确的电池荷电状态估算。Wind power generation is generally equipped with battery storage systems due to its instability,the accurate and reliable battery state of charge estimation is,therefore,very important.In the derivative methods of Kalman filtering,the default values of process noise and measurement noise are fixed,but in reality,they are not.In order to accurately estimate the battery charge state,the unscented transformation of the unscented Kalman filter was used to obtain the statistical measure of the process noise covariance,and combined with the autocorrelation least squares method,the measurement noise covariance was estimated by calculating the correlation in the measurement update.For this purpose,an experimental platform was built to demonstrate that the improved algorithm can achieve more accurate estimation of battery state of charge.

关 键 词：锂离子电池 风力发电 无迹卡尔曼滤波 荷电状态估算 

分 类 号：TM912[电气工程—电力电子与电力传动]