基于自适应滤波的电动车纵向滑移率识别方法

Electric Vehicle Longitudinal Slip Identification Method Based on Self-adaptive Filter

根据路面附着状况实时地选择最优的滑移率控制目标是电动车驱动防滑控制策略的关键。针对四轮驱动电动车的特点,研究利用自适应Kalman滤波获得车速信息和轮胎驱动力信息的方法。利用该方法实时估计车速和驱动力等参数,并计算附着系数-滑移率曲线的斜率k,以对路面附着状况进行精确估计。通过将计算得到的斜率与设定的最优滑移率所对应的斜率之差作为控制参考量,及时地调整驱动电机的输出转矩,提高电动车的道路附着系数利用率,以获得更好的电动车驱动防滑控制效果。仿真结果表明:车辆在高、低附路面行驶时,该算法均可有效地估计滑移率的k值。

To choose the optimized slip control targets for the various road conditions in real time is the key of the effect of traction control strategy for electric vehicle （EV）. Based on the traits of the four-wheel drive EV, a selfadaptive Kalman filter was applied to gain the information of vehicle velocity and traction forces on four wheels. With the estimated vehicle velocity and the driving forces on four wheels, the slope k of the curves of adhesion coefficient versus slip rate could be calculated and consequently the road adhesion condition could be accurately identified. Through calculating the difference between the estimated slope k with the optimized slope k as a control reference, the output torques of the motors were adjusted instantaneously to improve the using rate of the adhesion as well as the rationality of the traction control strategy. Simulation results illustrate that whether on high adhesion or low adhesion roads the vehicle is passing, the strategy can estimate the k effectively.