Stability control of in-wheel motor electric vehicles based on centroid sideslip-angle estimation

Abstract

In this paper, we present a high-precision, reliable super-twisting sliding mode controller (STSMC) based on the estimated centroid sideslip angle in rear-wheel drive vehicles. We use a maximum-correlation entropy-adaptive generalized high-order cubature Kalman filter (MCAGHCKF) to perform real-time estimation of the centroid sideslip angle. According to the saddle point of the dβ-β phase plane, we determine the unstable region of the centroid sideslip angle. We then propose a centroid sideslip angle stability coefficient and establish a normalized combined error equation with variable weights, allowing the combined error of the centroid sideslip angle and yaw rate to adapt to changes in the stability coefficient. We use the integral sliding-mode surface and super-twisting sliding-mode algorithm to design an STSMC. We conducted a simulation comparison and test analysis on the estimation of centroid sideslip angle, and the results show that the MCAGHCKF provided reliable estimation data of the centroid sideslip angle for stability control. When STSMC was used to control the rear-wheel drive vehicle, the simulation results show that the maximum reduction in the centroid sideslip angle and yaw rate was 36.47% and 14.2%, respectively. The test results show that the maximum reduction in the centroid sideslip angle and yaw rate was 24.84% and 30.89%, respectively. The results indicate that the STSMC based on variable-weight combined error can effectively control the stability of the vehicle.