摘要
锂离子电池作为电动汽车常用的动力源,电池荷电状态(State of Charge,SOC)直接反映了电动汽车的运行安全状态,因此准确估算电池SOC具有重要意义。通过采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法原理,建立戴维宁(Thevenin)等效电路模型,并结合Matlab算法程序的方法对动态工况下的电池SOC进行估算分析,验证了该EKF算法程序的准确性和可行性。估算结果表明,采用该方法对动态工况下的SOC估算具有很好的估算效果,估算精度平均可达97%。
Lithium-ion battery is used as the common power source of electric vehicles, the batteries＇ state of charge directly reflects the safety operation of the electric vehicles, so accurately estimation of the battery SOC is of great significance. In this paper, by the method of using the Extended Kalman Filter （EKF） algorithm principle, establishing the Thevenin equivalent circuit model, and combined with the Matlab algorithm procedure has estimated and analyzed the battery SOC when it is under a dynamic condition, and then, verified the accuracy and feasibility of the EKF algorithm procedure. Finally, the estimation results show that the method of dynamic conditions of SOC estimation has a good estimation effect, and whose estimation accuracy can be up to 97% on average.
出处
《电源世界》
2016年第8期26-28,共3页
The World of Power Supply
基金
绵阳市科技计划项目(15G-03-3)
关键词
锂离子电池
动态工况
估算精度
Lithium-ion battery
Dynamic condition
Estimation accuracy
