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| 基于V-R模型与卡尔曼滤波器的蓄电池SOC估计 | |
| 引用本文： | 黄世回,蔡启仲,王汝钢. 基于V-R模型与卡尔曼滤波器的蓄电池SOC估计[J]. 广西工学院学报, 2012, 23(3): 49-55 |
| 作者姓名： | 黄世回 蔡启仲 王汝钢 |
| 作者单位： | 1. 广西工学院电气与信息工程学院,广西柳州545006 深圳市普禄科智能检测设备有限公司,广东深圳518067 2. 广西工学院电气与信息工程学院,广西柳州,545006 3. 深圳市普禄科智能检测设备有限公司,广东深圳,518067 |
| 基金项目： | 广西自然科学基金，北京三一重工盾构机基础研发资助 |
| 摘 要： | 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. |
| 关 键 词： | 阀控铅酸电池 卡尔曼滤波器 荷电状态(SOC) 电池模型 不间断电源系统 |
Battery SOC estimation based onV-R model and Kalman filter |
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| HUANG Shi-hu,CAI Qi-zhong,WANG Ru-gang. Battery SOC estimation based onV-R model and Kalman filter[J]. Journal of Guangxi University of Technology, 2012, 23(3): 49-55 | |
| Authors: | HUANG Shi-hu CAI Qi-zhong WANG Ru-gang |
| Affiliation: | 1. College of Electronic and Information Engineering, Guangxi University of Technology, Liuzhou 545006, China; 2. Shenzhen Pluke Intelligent Test Equipment Co., Ltd., Shenzhen 518067, China) |
| Abstract: | 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. |
| Keywords: | VRLA battery Kalman filter SOC (State of charge) battery model UPS |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |