基于采样交通量与卡尔曼滤波的增程式电动汽车续航里程估算研究

为了提高增程式电动汽车续驶里程估算精度,消除驾驶员因电动汽车续驶里程估计不准确产生的忧虑心理,提出一种新的基于采样交通量与卡尔曼滤波的增程式电动汽车里程估算方法。依据不同道路等级,通过指数回归模型对增程式电动汽车交通量进行估算。介绍了卡尔曼滤波原理,给出卡尔曼滤波方法状态方程与观测方程,利用最小二乘法对卡尔曼滤波公式进行推导,获取状态向量预测值和状态向量测试方差矩阵。介绍了单位能耗行驶里程计算方法。结合采样交通量和剩余续驶里程计算结果,通过卡尔曼滤波方法获取下一时刻增程式电动汽车的续驶里程,实现对增程式电动汽车续驶里程的估算。经实验验证,所提方法估算准确性高。

Estimation of mileage of electric vehicle based on sampling traffic volume and Calman filter

In order to improve the reev mileage estimation accuracy, eliminate the driver for electric vehicle mileage estimates do not correctly produce anxiety, a new method is proposed to estimate the sampling volume of traffic and Calman filter e-revs based on mileage. According to different road grades, the traffic volume of the electric vehicle is estimated by means of exponential regression model. Calman introduced the filtering principle, gives Calman a filtering method of state equation and observation equation, to derive the Calman filtering formula by using least squares method, obtain the predictive value of state vector and state vector test variance matrix. The calculation method of unit energy consumption mileage is introduced. Combined with the sampling traffic volume and the remaining mileage, the mileage of the electric vehicle is obtained by the Calman filtering method at the next time, and the mileage of the extended electric vehicle is estimated. The experimental results show that the proposed method has high estimation accuracy.