基于V-R模型与卡尔曼滤波器的蓄电池SOC估计

Battery SOC estimation based onV-R model and Kalman filter

蓄电池组广泛应用于UPS系统中,荷电状态(SOC)是表征蓄电池状态的重要参数之一.在线准确估算蓄电池SOC,有利于开展对蓄电池的状态诊断、维护,保证电池组安全供电.通过对阀控铅酸电池作了大量的充放电试验,根据试验数据应用最小二乘法进行辨识,获得蓄电池SOC的端电压-电阻的计算模型,运用卡尔曼滤波器算法,对SOC做最优估计.经实验验证和仿真,得到了蓄电池SOC最优估计结果,具有很好的精确度,表明该方法能够在工程上用来估算蓄电池的SOC.

Battery string is widely used in UPS system. The state of charge （SOC） is one of the important parameter of the batery status. Online estimation of battery SOC contributes to the battery condition diagnosis, maintenance, and ensures the battery string power supply safety. With VRLA batteries charge and discharge experiments, battery SOC voltage - resistance calculation model is proposed by the least squares method, and the optimal estimation of SOC is obtained by the Kalman filter algorithm. The battery SOC optimal estimation results of the experiment and simulation are very accurate, which shows that this method can be used to estimate the battery SOC in engineering field.