A Multiple Data Fusion Approach to Wheel Slip Control for Decentralized Electric Vehicles

Abstract

Currently, active safety control methods for cars, i.e., the antilock braking system (ABS), the traction control system (TCS), and electronic stability control (ESC), govern the wheel slip control based on the wheel slip ratio, which relies on the information from non-driven wheels. However, these methods are not applicable in the cases without non-driven wheels, e.g., a four-wheel decentralized electric vehicle. Therefore, this paper proposes a new wheel slip control approach based on a novel data fusion method to ensure good traction performance in any driving condition. Firstly, with the proposed data fusion algorithm, the acceleration estimator makes use of the data measured by the sensor installed near the vehicle center of mass (CM) to calculate the reference acceleration of each wheel center. Then, the wheel slip is constrained by controlling the acceleration deviation between the actual wheel and the reference wheel center. By comparison with non-control and model following control (MFC) cases in double lane change tests, the simulation results demonstrate that the proposed control method has significant anti-slip effectiveness and stabilizing control performance.