Unscented Kalman Filter Estimator of Vehicle States and Parameters Based on Ant Lion Optimization Algorithm

To accurately acquire the states and parameters of distributed drive electric vehicles and meet the requirements of vehicle stability control systems, a state parameter estimator was proposed based on the ant lion optimization (ALO) and unscented Kalman filter (UKF) algorithms with a consideration of the uncertain noise covariance in the process of UKF. The ALO algorithm was used to find the optimal noise covariance and the singular value decomposition (SVD) was implemented to always maintain the noise covariance matrix in positive definiteness. In addition, tire cornering stiffness as the estimator input was identified by the exponential weighted least square algorithm. The parameter estimation model for distributed drive electric vehicles was established based on a MATLAB/Simulink and CarSim co-simulation platform; the co-simulation was conducted under the double lane change and sinusoidal hysteresis maneuver and a vehicle test under the double lane change was also carried out with A&D 5435 rapid prototyping platform. The simulation and test results indicate that, compared to the estimation results of the SVDUKF estimator, under the double lane change simulation condition, the root mean square error of side slip angle and yaw rate are reduced by 55.7% and 30.7% respectively. Under the sinusoidal hysteresis simulation condition, the root mean square error of the side slip angle and yaw rate are reduced by 58.1% and 85.1% respectively. Furthermore, the estimator can maintain an adequate performance despite vehicle instability. Under the double lane change test condition, the root mean square error of the yaw rate is only 0.938 4 (°)•s-1. Therefore, the proposed ALO-SVDUKF estimator of distributed drive electric vehicles can effectively improve the estimation accuracy of the side slip angle and yaw rate, providing accurate state information for vehicle stability control. © 2020, Editorial Department of China Journal of Highway and Transport. All right reserved.