基于改进Thevenin模型锂电池SOC估算方法  

SOC estimation algorithm of lithium-ion battery based on improved Thevenin model

作　　者：张廷[1] 胡社教[1] 

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009

出　　处：《电源技术》2015年第11期2400-2402,2496,共4页Chinese Journal of Power Sources

摘　　要：针对纯电动汽车锂离子电池荷电状态(SOC)在环境温度和放电电流变化较大的情况下估算精度较低的问题,采用了一种基于改进Thevenin模型的扩展卡尔曼滤波算法(EKF)。根据电池性能模型,把电池容量作为状态变量,把影响SOC估算精度的环境温度和放电电流作为修正量,采用扩展卡尔曼滤波算法提高SOC估算精度。实验结果表明,该方法提高了SOC估算精度,可用于电动汽车电池管理系统。According to the problem of low precision in SOC estimation of lithium-ion battery for electric vehicle under condition of higher fluctuation of environment temperature and discharging current, an extended Kalman filtering method based on Thevenin model was introduced. On the basis of battery performance model, the battery capacity was taken as state variables and the influencing environment temperature and discharge current were taken as correction. Extended Kalman filtering method was used to improving the accuracy of SOC estimation of battery. The experiment results show that the SOC estimation accuracy is improved, and it can be used in electric vehicle battery management system.

关 键 词：电动汽车 锂离子电池 荷电状态 Thevenin模型 扩展卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]