四驱混合动力轿车驱动工况无迹卡尔曼车速估计

Vehicle Speed Estimation in Driving Mode for Hybrid Electric Car Using Unscented Kalman Filter

针对驱动模式复杂多变的四驱混合动力轿车,考虑其后轮毂电机驱动转矩的准确可测以及既定模式下前驱动轮转矩的可推算性,结合电子稳定程序(Electronic stability program,ESP)系统传感器信号,提出无迹卡尔曼车速估计算法。搭建四驱混合动力轿车仿真平台,其集成了驱动系统模型、非线性7自由度车辆动力学模型和统一轮胎模型。基于车辆动力学模型和轮胎模型,设计融合驱动轮转矩信息和传感器信息的车速估计算法,并将估计结果与仿真车速进行比较分析。在样车上加装转向盘转角、横摆角速度和质心加速度等传感器,采集轮转、驱动轮转矩信息,在后轮纯电驱动模式低速双纽线试验、四轮混合驱动模式双移线和蛇行试验工况下,对所设计算法进行实车道路试验。仿真和实车试验结果表明,无迹卡尔曼车速估计算法精度较高,且具有较强的工况适应性。

As for the four-wheel drive hybrid electric car with complex and changeable driving modes, unscented Kalman filter（UKF） is proposed for vehicle speed estimation in consideration of obtainable driving torque and electronic stability program（ESP） senor signals. Simulation platform is established according to the four-wheel drive hybrid electric car, which integrates power-train system model, nonlinear seven degree of freedom vehicle dynamics model and the dynamic union tire model. The estimation result of UKF algorithm is compared with the simulated real car’s velocity. After steering wheel angle sensor, yaw angular velocity sensor and acceleration sensors are all mounted in the prototype car and the signals of two front wheel angular speed are acquired as well as the torque information of driving wheel are introduced, UKF algorithm is tested on the real vehicle road experiments, which include 8-shape route driving case on pure electric drive mode, double-lane change driving case and S-shape route driving case on four wheel hybrid drive mode. Simulation and test results show that the proposed algorithm has not only high precision, but also strong adaptability.