Abstract:

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 multi-wheel 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.

Key words: wheel low-speed estimation, multi-sensor fusion, Kalman filter, interactive multi-model fusion