电动汽车蓄电池荷电状态的卡尔曼滤波估计

A Real-time the state of Charge of Storage Battery Estimation Based on the Kalman FilteringTheory for Electric Vehicle

对电动汽车剩余里程的预测需要一个准确的蓄电池荷电状态(SOC)值,但目前任何方法都不能精确地测量蓄电池的剩余电量,以计算电动汽车蓄电池的荷电状态(SOC).在对目前常用的剩余电量计量方法分析的基础上,提出了一种基于电流的测量,然后利用卡尔曼滤波估计递推算法对蓄电池SOC进行实时估计,并在MATLAB下进行了仿真。

The accurate state of charge (SOC) is required for the battery of electric vehicles. The various estimation methods for the SOC of the storage battery have been proposed. However, any method can not accurately predict the residual capacity. A new estimation method of SOC on the storage battery is proposed in the paper. This method is based on the current method and utilize Kalman filter to real time estimate the SOC of battery. This method can minimize the estimation error. We imitate the SOC measurement system by the MATLAB simulation tool.