电驱动车辆的整车质量与路面坡度估计

Vehicle mass and road slope estimates for electric vehicles

针对现有整车质量观测方法受路面坡度影响较大、待标定量较多、需传感设备较多、估计实时性较差等问题,利用电驱动车辆纵向驱动力准确的特点,将质量与坡度解耦,提取了驱动力信号和纵向行驶加速度信号的高频部分,用递归最小二乘法得到了整车质量。在获得比较准确的质量估计结果后,采用了运动学和动力学方法对路面坡度进行了多方法联合观测,通过运动学方法和动力学方法的协调互补,解决了坡度估计严重依赖于车辆模型精度、受加速度传感器静态误差影响较大的缺点。该方法在多种工况下能够对坡度作出迅速有效估计。动力学方法考虑了坡度的时变特性,利用带有遗忘因子的递推最小二乘法对坡度进行估计;运动学方法则利用了坡度与传感器静态偏差强相关的特点,直接得到当前坡度。实车实验结果表明:所提出的整车质量与路面坡度估计方法鲁棒性好,收敛速度快,估计准确。

This paper presents a vehicle mass estimation method based on high-frequency information extraction to improve existing mass estimation methods that are susceptible to the influence of road slope with poor real-time performance.Accurate estimates are needed to accurately predict the driving force provided by the electrical drive systems.A high-pass filter is used to extract high-frequency longitudinal driving force and acceleration information.Then,a recursive least squares algorithm estimates the vehicle mass.Then,the road slope is estimated based on a combined kinematic and dynamic model.This method solves the problem that road slope estimates require an accurate vehicle dynamic model and are susceptible to acceleration sensor bias.The algorithm combines the dynamic method in a recursive least squares algorithm with a factor to neglect some previous information to estimate the road slope and a kinematic method that uses the relationship between the longitudinal vehicle acceleration and the acceleration sensor to calculate the road slope.Experimental tests show that this method is robust and can accurately estimate the vehicle mass and road slope in real-time.