Xinyu Qi

Speed information is crucial for controlling distributed electric-drive loaders, especially for driving and operation. Due to complex working conditions, the wheels of the loader often experience different conditions, leading to inaccurate speed estimation. To solve this, this paper proposes a multi-sensor fusion speed estimation method based on a Finite State Machine (FSM). The method uses the FSM to identify the wheel states and adaptively switches between the weighted average method and integration method to estimate the vehicle’s speed accurately. When all wheels are slipping, the acceleration integration method is used, starting from the latest trustworthy speed estimate. When the wheels are not slipping, the speed is estimated using the weighted average of the trustworthy wheels. Additionally, the method addresses the relative motion between the front and rear vehicle bodies caused by articulated steering by using an articulated steering projection method to ensure accurate wheel state estimation from IMU signals. Simulation and hardware-in-the-loop experiments show that the proposed method can accurately estimate vehicle speed under various road conditions. Specifically, under low-adhesion road conditions with all four wheels in a slipping state, it improves speed estimation accuracy by over 75% compared to traditional methods such as simple averaging, selective averaging, and pure integration.