Combined State and Parameter Observation of Distributed Drive Electric Vehicle via Dual Unscented Kalman Filter

In order to estimate vehicle different states such as vehicle sideslip angle and vehicle inertia parameters such as vehicle mass and yaw moment of inertia in distributed drive electric vehicle, the uncertain load parameters (passenger or cargo loaded)-based four-wheeled vehicle nonlinear dynamics estimation model including longitudinal, lateral, and yaw motions is developed to deal with sensitive challenges of vehicle inertia parameters estimation due to load parameter changes. Based on vehicle multi-sensor data fusion from the hub torque and other measurements, the unscented Kalman filter that can adapt to the strong nonlinear system is introduced, and then the nonlinear observer with the dual unscented Kalman filter is designed, where the first unscented Kalman filter observes vehicle different states and the other parallel unscented Kalman filter is used to observe the vehicle inertia parameters. Simulations for double lane change and sine manoeuvres are carried out to evaluate the feasibility and effectiveness of observer under different load parameters in CarSim/Matlab environment. The results show that the proposed observation system can observe vehicle states and inertia parameters in real time, and it has high observation accuracy even under large load condition. © 2019 Journal of Mechanical Engineering.