Abstract
The effect of vehicle active safety systems is subject to the accurate knowledge of vehicle states. Therefore, it is of great importance to develop a precise and robust estimation approach so as to deal with nonlinear vehicle dynamics systems. In this paper, a planar vehicle model with a simplified tire model is established first. Two advanced model-based estimation algorithms, an unscented Kalman filter and a moving horizon estimation, are developed for distributed drive electric vehicles. Using the proposed algorithms, vehicle longitudinal velocity, lateral velocity, yaw rate as well as lateral tire forces are estimated based on information fusion of standard sensors in today’s typical vehicle and feedback signals from electric motors. Computer simulations are implemented in the environment of CarSim combined with Matlab/Simulink. The performance of both estimators regarding convergence, accuracy, and robustness against an incorrect initial estimate of longitudinal velocity is compared in detail. The comparison results demonstrate that both estimation approaches have favourable coincidence with the corresponding reference values, while the moving horizon estimation is more accurate and robust, and owns faster convergence.
Highlights
Safety and energy conservation are two eternal themes for automotive design [1]
Vehicle is moving on a flat horizontal plane; Vertical, roll and pitch dynamics are omitted; longitudinal acceleration, lateral acceleration and the yaw rate is measured with white Gauss noise; INS sensors are mounted on the vehicle center of gravity (COG)
In this study, the embedded vehicle model equipped with vehicle stability controller (VSC) in CarSim serves as a real vehicle, providing control input, reference vehicle states and measured signals, while the estimation algorithms are built in Matlab/Simulink
Summary
Safety and energy conservation are two eternal themes for automotive design [1]. Over the past few decades, active safety control has become one of the most effective accident avoidance technologies [2]. DMB approaches include specific and detailed vehicle mathematical models as well as physical parameters such as mass, yaw moment inertia, COG (center of gravity) position and so on. Based on the piecewise linear tire model, Ren et al [24] achieved vehicle state estimation with UKF. To further explore the potential application of UKF and MHE, we compare their performance regarding to convergence, accuracy as well as robustness, for the DDEV state estimation. Equipped with In vehicle stability controller (VSC), which is application believed toofbeUKF accurate enough inspecttheir the this study, to further explore the potential and MHE, we to compare precision of the presented approaches. The accidental error of estimated results is taken performance regarding to convergence, accuracy as well as robustness, for the DDEV state estimation. Simulation results are analyzed in terms of tracking accuracy and convergence behavior
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