基于六参数RC等效电路模型的锂离子电池SOC估计  

SOC Estimation of the Electric Vehicle Li-ion Battery Based on Six-Parameter RC Circuit Equivalent Model

作　　者：黄兵锋[1,2] 杨正才[1] 傅佳宏[3] 

机构地区：[1]湖北汽车工业学院汽车工程学院汽车动力传动与电子控制湖北省重点实验室,湖北十堰442002 [2]吉林大学汽车工程学院汽车仿真与控制国家重点实验室,吉林长春130022 [3]武汉科技大学信息科学与工程学院,湖北武汉430081

出　　处：《重庆交通大学学报（自然科学版）》2015年第5期170-174,共5页Journal of Chongqing Jiaotong University(Natural Science)

基　　金：湖北省重点实验室开放基金项目计划(ZDK201206)

摘　　要：为提高电动车锂离子动力电池荷电状态(SOC)的估算精度,提出了一种基于六参数RC等效电路的电池模型,采用扩展卡尔曼滤波开展对电池SOC的估算方法研究。在考虑未知干扰和环境噪声的影响下,进一步提出了在扩展卡尔曼滤波的基础上的自适应卡尔曼滤波算法,并开展了对电池SOC的在线估算。仿真结果表明:虽然扩展卡尔曼滤波和自适应卡尔曼滤波都对电池SOC有较好的估算精度,但在未知干扰和噪声的影响下自适应卡尔曼滤波具有更好的鲁棒性。In order to improve the estimation accuracy of state of charge（ SOC） for Li-ion battery commonly used in electric vehicles,a battery model was first proposed which was equivalent to a six-parameter RC circuit. The extended Kalman filter（ EKF） was then employed to estimate the SOC while dealing with the nonlinearity of the battery model. Further to cope with the disturbances caused by unknown and random noises,an adaptive extended Kalman filter（ AEKF） algorithm was introduced which estimated the statistical attributes of the noises for the purpose to adaptively adjust the SOC estimation. Simulation results show that the AEKF algorithm is more robust to the external disturbances,although both EKF and AEKF algorithms have quite accurate SOC estimation.

关 键 词：车辆工程 电动车 锂离子电池 荷电状态 自适应卡尔曼滤波 

分 类 号：U469.712[机械工程—车辆工程]