分裂电池模型的在线参数辨识  

Online parameter identification of split battery model

作　　者：吴铁洲[1] 余文山 郝山 常春[1] WU Tie-zhou;YU Wen-shan;HAO Shan;CHANG Chun(Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control in Hubei Province,Hubei University of Technology,Wuhan Hubei 430068,China)

机构地区：[1]湖北工业大学,太阳能高效利用及储能运行控制湖北省重点实验室,湖北武汉430068

出　　处：《电源技术》2020年第5期727-731,共5页Chinese Journal of Power Sources

基　　金：国家重点研发计划(2016YFC0401702)。

摘　　要：分裂电池模型(SBM)是一种可消除状态变量间相互干扰的新型模型,但该模型的参数辨识一般采用传统的最小二乘法(RLS),无法实时跟踪模型参数,且RLS会出现数据饱和,导致辨识精度低,影响电池荷电状态(SOC)估算精度。针对这一问题,提出基于分裂电池模型的带遗忘因子的递推最小二乘法在线参数识别方法,该方法能够实现模型参数的在线识别并提高辨识精度,基于辨识的模型参数利用无迹卡尔曼滤波(UKF)算法估算SOC,从而提高电池SOC估算精度。实验结果表明,采用带遗忘因子的递推最小二乘法可提高模型参数的估算精度,并有效改善SOC的估算效果。The split cell model(SBM)is a new model which can eliminate the interference between state variables.However,the traditional least square method(RLS)is generally used to identify the parameters of the model,which can not track the parameters in real time,and the RLS will have data saturation,resulting in low identification accuracy and affecting the SOC estimation accuracy of the battery.To solve this problem,a recursive least square method with forgetting factor based on split cell model was proposed.On-line identification of model parameters could be realized and the identification accuracy could be improved by this method.Unscented Kalman filter(UKF)algorithm was used to estimate SOC by the identification based model parameters,thus the estimation accuracy of battery SOC was improved.The experimental results show that the recursive least square method with forgetting factor can improve the estimation accuracy of model parameters and the estimation effect of SOC.

关 键 词：荷电状态 分裂电池模型 在线参数识别 无迹卡尔曼 

分 类 号：TM912[电气工程—电力电子与电力传动]