Abstract
Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.
Highlights
With the development of the automobile industry, the four in-wheel motor independent drive electric vehicles (4WIDEVs) have attracted increasing attention due to their contribution to energy saving and environmental protection [1,2,3]
The 4WIDEV has a lot of advantages compared with the centralized drive electric vehicles (CDEVs) [4], where the independent control of each wheel is of great benefit for the improvement of the vehicle stability control [5]
Motivated by the above review, this paper focuses on developing a novel vehicle state estimation methodology combining the unscented Kalman filter (UKF) with the ant lion optimization (ALO)
Summary
With the development of the automobile industry, the four in-wheel motor independent drive electric vehicles (4WIDEVs) have attracted increasing attention due to their contribution to energy saving and environmental protection [1,2,3]. According to the kinematic equations between the vehicle acceleration, velocity, and yaw rate, the kinematic model-based method is established to estimate the vehicle states which is difficult to be measured. Chen et al introduced a square root cubature Kalman filter into the vehicle state estimator, in which the moving window method was used to adjust the covariance of measurement noise to improve the estimation accuracy and reliability [32]. Based on the available measurement sensor signals, the 3-DOF vehicle dynamics model and nonlinear tire model are deduced and established for the estimator design. Because the noise information is obtained based on the estimation and measurement data, it is the best for the nonlinear vehicle system and driving environment at present.
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