摘要：Vehicle longitudinal velocity is a corner stone to many important vehicular applications, especially the electronic stability control system, and needed to be theoretically estimated; in the meantime, it also requires more high performance of real time and accuracy. In this article, we propose a new method for the estimation of vehicle longitudinal velocity based on the wheel speed signals and evaluate their confidence levels with Takagi Sugeno Kang (TSK)-fuzzy model. First, according to the normal conditions and extreme conditions of experimental vehicles with the variation of the wheel speed in the electronic stability control intervention, the vehicle driving states can be divided into three kinds of working conditions: acceleration, deceleration, and sliding. Then, we consider whatever that may happen to the fuzzy rules in the automobile travel process. Although the quantities of fuzzy rules are many, the amount of computation is well controlled at the same time. Finally, the accuracy of longitudinal velocity estimation is verified through the real vehicle tests such as double-lane change course and slalom test. The results indicate that the longitudinal velocity does not show the wrong trend with the wheel speed signals changing rapidly under the extreme working conditions. The system has outstanding real-time performance and application.
