Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.
For vehicle state estimation, conventional Kalman filters work well under Gaussian assumptions. Still, they are likely to degrade dramatically in the practical non-Gaussian situation (especially the noise is heavy-tailed), showing poor accuracy and robustness. This article presents an estimation technique based on the Maximum Correntropy Criterion (MCC) combined with an adaptive extended Kalman filter (AEKF), and an extended Kalman filter (EKF) based on the MCC has also been studied. A lateral-longitudinal coupled vehicle model is developed, while an observer containing the state vectors such as yaw rate, sideslip angle, vehicle velocity and tire cornering stiffness is designed using easily available in-vehicle sensors and low-cost GPS. After analyzing the algorithmic complexity, the proposed algorithm is validated by Sine Steering Input and Double Lane Change driving scenarios. Finally, it is found that MCC combined with AEKF/EKF (MCAEKF/MCEKF) has stronger robustness and better estimation accuracy than AEKF/EKF in dealing with non-Gaussian noise for vehicle state estimation.
To research the influence of liquid sloshing on the driving stability of high-clearance sprayers, this paper builds an equivalent liquid sloshing mechanical model and obtains the stochastic acceleration excitation of the rectangular spray tank using the Adams kinetic model, thus obtaining the relationship between the impact force, moment, and the stochastic acceleration using Fluent numerical simulation analysis. This paper makes further calculations with MATLAB/Simulink system models, and the result from comparing these two calculations shows that the equivalent strategy proposed in this paper has a better consistency. Based on the consideration of the acting forces of the additional moment due to lateral movement of the center of mass of the liquid and the dynamic pressure due to liquid sloshing in the tank, this paper builds a mathematical model of the sprayer and researches the influence of the filling ratio and vehicle velocity on the vehicle stability through stochastic acceleration excitation. The results show that, in the case of different speeds, the liquid sloshing has a small influence on the overall roll angle; in the case of different filling ratios, the liquid sloshing has a big influence on the overall roll angle, the slip angle of the center of mass, and the yaw angular velocity; the filling ratio k = 0.85 and the speed u = 1 m/s−2 m/s are safe operation parameters of the sprayer. This research provides reference solutions for the stability control and optimization problems of the high-clearance sprayer and semitrailer.
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