自放电修正的锂动力电池SCKF-STF的SOC估算策略  

Research on state of charge estimation of Li-ion battery based on SCKF-STF

作　　者：于智龙[1,2] 郭艳玲[1] 王海英[2] 

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150040 [2]哈尔滨理工大学自动化学院,黑龙江哈尔滨150080

出　　处：《电机与控制学报》2013年第10期70-76,84,共8页Electric Machines and Control

基　　金：国家973计划课题(2009CB210107)

摘　　要：为了准确估算EV用锂动力电池的荷电状态,利用Map图法对电池自放电电流进行建模,通过自放电模型得到静置状态下电池自放电电流数值,通过电流时间累积得到静置状态下电池SOC的衰减数值,对电池SOC的初值进行了修正,分析了锂动力电池等效电路模型的不确定性因素,利用EKF与SCKF-STF算法对低温及常温下电池模拟工况进行了实验验证以及对比分析。实验结果表明,SCKF-STF算法能够很好的消除模型不确定性所带来的影响,低温下和常温下算法SOC估算误差比EKF算法分别提升了0.53%和3.8%。In order to accurately estimate Lithium power battery SOC （state of charge） used on electric vehicle, the battery self-discharge current model was built by using the Map diagram. Resting battery self- discharge current values were obtained through self-discharge model. The attenuation values of resting battery SOC in current working conditions were obtained through current and time integral. The bat- tery SOC initial values were corrected, and uncertainties of lithium battery equivalent circuit model were analyzed. Experimental verification and comparative analysis of the battery working conditions under low temperature and room temperature were conducted by using the EKF （extended Kalman filter） with SCKF-STF （square cubature Kalman filter） algorithm. Experimental results show that SCKF-STF algo- rithm can well eliminate effects of the model uncertainty. Estimation error of SOC under low temperature and room temperature are improved 0.53% and 3.8% than that of EKF algorithm.

关 键 词：电动汽车 锂动力电池 电荷状态 参数估计 平方根容积卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]