Optimization-Based Tuning of a Hybrid UKF State Estimator with Tire Model Adaption for an All Wheel Drive Electric Vehicle

Novel drivetrain concepts such as electric direct drives can improve vehicle dynamic control due to faster, more accurate, and flexible generation of wheel individual propulsion braking torques. Exact robust estimation state motion in the presence unknown disturbances, changes road conditions, is crucial for realization systems. This article shows design, tuning, implementation, test a estimator with tire model adaption drive vehicles. The dynamics are modeled using double-track an adaptive model. State-of-the-art sensors, inertial measurement unit, steering angle, speed, motor current sensors used measurements. Due nonlinearity model, Unscented Kalman Filter (UKF) simultaneous parameter estimation. To simplify difficult task UKF optimization-based method real-vehicle data utilized. implemented on electronic unit tested hardware-in-the-loop simulation. High precision even severe driving maneuvers under various conditions achieved. Nonlinear all vehicles tuning shown provide high minimal manual effort.