Interacting Multiple Model Filter-Based Estimation of Lateral Tire-Road Forces for Electric Vehicles

Knowledge of vehicle dynamics variables is very important for vehicle control systems that aim to improve handling characteristics and passenger safety. However for both technical and economical reasons some fundamental data (e.g., Lateral tire-road forces and vehicle sideslip angle) are difficult to measure in a standard car. This paper proposes a novel Interacting Multiple Model Filter-Based method to estimate lateral tire-road forces by utilizing real-time measurements. The estimation method of lateral tire-road forces is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adaptively adjusted multiple vehicle-road system models to match variable driving conditions. A four-wheel nonlinear vehicle dynamics model (NVDM) is built 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. 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. The Simulation using Matlab/Simulink-Carsim shows that the proposed estimation methods can accurately estimate lateral tire-road forces.