关键词: 车辆状态参数估计, 交互多模型, 平方根容积卡尔曼滤波, 质心侧偏角, 轮胎侧向力

Abstract: In order to accurately and real time estimate the state parameters of vehicle for vehicle stability control, two vehicle models based on linear and nonlinear tire models respectively are set up. Interacting multiple models (IMM) algorithm is adopted for the switching between two models to accommodate different complicated road conditions, and squareroot cubature Kalman filter algorithm is fused into IMM one. Considering the effects of lateral acceleration and road adhesive coefficient on algorithms, fuzzy algorithm is adopted to conduct real time correction on the model transformation probability in IMM algorithm for speeding up model switching and enhancing tacking accuracy. The results of CarsimMatlab/simulink cosimulation and real vehicle test show that the algorithm proposed can achieve high tracking accuracy in vehicle state parameter estimation, speedy model switching and good robustness.

Key words: vehicle state parameter estimation, IMM, square root cubature Kalman filtering, mass center sideslip angle, tire lateral force