摘要
动力电池荷电状态(SOC)能够反映电池的剩余电量,为驾驶员提供续驶里程信息,是电池均衡控制和能量管理的基础。为提高荷电状态的估计精度,以锂离子动力电池为研究对象,建立Thevenin等效电路模型,以扩展卡尔曼滤波(EKF)算法为基础,结合开路电压法和安时积分法,提出了一种改进型扩展卡尔曼滤波算法,并与安时积分法、扩展卡尔曼滤波法做了实验对比。实验结果表明,改进EKF算法估算精度高,收敛速度快,在动态工况下SOC估计误差降低至4%,能够更好地满足工程应用的实际要求。
The state of charge of the power battery is the basis of batteries imbalance and energy management,which can reflect the remaining battery power and provide continued mileage information to drivers.In order to improve the estimation accuracy of Lithium-ion battery state of charge,Lithium-ion battery is taken as the research object,Thevenin equivalent circuit model is established,and proposes an improved extended Kalman filter algorithm based on extended Kalman filter,combined with open-circuit voltage method and ampere-hour integration method.Experimental results show that the estimation accuracy is improved,the convergence rate is accelerated under the improved EKF algorithm,and the maximum SOC estimation error is reduced to 4%under dynamic conditions,which can meet the practical requirements of engineering application.
作者
何培杰
王琪
高田
马江睿
He Peijie;Wang Qi;Gao Tian;Ma Jiangrui(School of Electronic Information Engineering,Xi′an Technological University,Xi′an710032,China;School of Electronic and Information,Northwestern Polytechnical University,Xi′an710072,China)
出处
《国外电子测量技术》
2019年第7期37-42,共6页
Foreign Electronic Measurement Technology
基金
西安市科技创新引导项目(201805042YD20CG26(8))资助
作者简介
何培杰,硕士研究生,主要研究方向为先进控制理论及应用。E-mail:493997440@qq.com
