基于多传感器信息的汽车低速车速估计方法

Research on the Estimation of Vehicle Speed Under Low-Speed Conditions Based on Multi-sensor Information

为解决低速工况下轮速传感器测量精度低、更新周期长的问题,利用现有的底盘域传感器的信号,本文提出了一种基于多传感器信号的电驱动汽车低速车速估计方法。为准确估计车速,建立了基于多轮速脉冲信号的车速估算模型(模型I)和基于电机转速信号的车速估算模型(模型II)。在估算轮速时,模型I可以有效地避免噪声干扰,但在极低速的情况下,其更新周期较长;而模型II估算得到的轮速信息更新周期短、精度高,但其无法克服传动系统中由于齿隙所产生的冲击干扰。为充分发挥两种估算模型的优势,本文采用交互多模型融合算法对两个模型的输出结果进行加权融合,并通过实车对比测试,验证了所提出的低速车速估计算法在不同行驶路面下的准确性和可靠性。结果表明,相较于传统轮速估算方法,该方法在低速工况下具有更高的估计精度和实时性。

To solve the problem of low measurement accuracy and long update period of wheel speed sensor under low-speed conditions,a method for estimating low-speed of an electric vehicle is proposed based on multiple sensor signals by using the existing sensors located at chassis.The speed estimation models based on multiwheel speed pulse signal(model I)and motor speed signal(model II)is established respectively to accurately estimate the vehicle speed.When estimating the wheel speed,model I can effectively avoid noise interference,but its update period is longer at very low speed.In contrast model II estimates the wheel speed information with a short update period and high accuracy,but it can’t overcome the impact interference caused by backlash in the drive train.To take into full play of the advantages of the two estimation models,an interactive multi-model fusion algorithm is used in this paper to fuse the output of the two models.The accuracy and reliability of the proposed low-speed estimation algorithm under different roads are validated by actual vehicle comparison experiments.The results show that compared with the traditional algorithm,the proposed method in this paper has higher accuracy and better real-time performance at low-speed conditions.