Estimation of lateral tire-road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

Vehicle control systems require certain vehicle information (e.g., tire-road forces and vehicle sideslip angle) concerning vehicle-dynamic parameters and vehicle-road interaction, which is difficult to measure directly for both technical and economedic reasons. This paper proposes a novel method to estimate lateral tire-road forces and vehicle sideslip angle by utilizing real-time measurements. The estimation method is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adapt multiple vehicle-road system models to variable driving conditions. Based on a four-wheel nonlinear vehicle dynamics model (NVDM) considering extended roll dynamics and load transfer, the vehicle-road system model set of the IMM filter is consists of a linear tire model based NVDM and a nonlinear Dugoff tire model based NVDM. Therefore, the IMM filter can integrate the estimates from two kinds of different vehicle-road system models to improve estimation accuracy. To address system nonlinearities and un-modeled dynamics, the interacting multiple model-unscented Kalman filter (IMM-UKF) and the interacting multiple model-extended Kalman filter (IMM-EKF) are investigated and compared simultaneously. Simulation using Matlab/Simulink-Carsim is carried out to verify the effectiveness of the proposed estimation methods. The results show that the developed estimation methods can accurately estimate lateral tire-road forces and the vehicle sideslip angle.