Vehicle Sideslip Angle Estimation Based on Strong Tracking SCKF Considering Road Inclinations

Abstract

As one of the most significant states to represent lateral stability, vehicle sideslip angle holds significant importance in vehicle dynamic control systems. Nevertheless, few previous studies on sideslip angle estimation have dealt with the impact of road inclinations and parameter uncertainty, such as the sprung mass of commercial vehicles. Therefore, this article proposes a novel sideslip angle estimation scheme based on the strong tracking square-root cubature Kalman filter (ST-SCKF) considering mass uncertainty and road inclinations including road slope and bank. The influences of road inclinations on the estimation algorithm and real sideslip angle are first analyzed. The vehicle dynamic and kinematic models are then integrated to consider road inclinations and mitigate the effects of the sprung mass, where the magic formula (MF) tire model is integrated with the vertical force estimation based on suspension dynamics to provide the necessary tire forces. Subsequently, the comprehensive sideslip angle estimation scheme based on ST-SCKF is introduced, whose observability under normal driving conditions is also proved. The simulation and hardware-in-loop (HiL) test results demonstrate that the proposed scheme has satisfying performances. Compared with the existing kinematic methods, the proposed method can deal with road inclinations and ensure observability. Compared with the existing dynamic methods, the proposed method is robust to mass uncertainty and road conditions.