Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance control systems. To improve accuracy parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with Huber method proposed. In this paper, we introduce nonlinear modified Dugoff tire model, build a three-degrees-of-freedom time-varying parametric dynamics extend mass, height center gravity, yaw moment inertia, which are significantly influenced by driving state, into vector. The observer was designed using framework. cost function introduced correct measured noise covariance in robustness observer. simulation verification double-lane change straight-line conditions at constant speed carried out Simulink/Carsim platform. results show that observation Huber-based robust (HRUKF) more realistically reflects real-time, effectively suppresses influence abnormal error noise, obtains high accuracy.