Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother

Abstract

This paper presents a vehicle sideslip angle estimation scheme against noises and outliers in sensor measurements for a four-wheel-independent-drive electric vehicle. The proposed scheme combines a robust unscented Kalman filter estimator based on the 3-DOF vehicle dynamics model and an extended Kalman filter estimator based on the kinematic model to form a hybrid estimator through a weighting factor. The weighting factor can be dynamically adjusted in real time to optimize the overall estimation performance under different driving conditions. The main contributions of this study to the related literature lie in two aspects. Firstly, a robust unscented Kalman filter estimator was incorporated to improve the robustness of dynamics-based estimation to sensor measurement outliers. Secondly, a novel moving polynomial Kalman smoother was included to filter out the noises in sensor measurements. Co-simulations of Matlab/ Simulink and Carsim software were conducted under typical vehicle maneuvers and show that the proposed vehicle sideslip angle estimation scheme can obtain satisfied estimation results, with the moving polynomial Kalman smoother exhibiting better phase characteristics and filtering performance relative to commonly-used finite impulse response filters, and the robust unscented Kalman filter estimator being robust to sensor measurement outliers.

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

“…Statistical weights were estimated for the covariance. Based on the vehicle system derived above, the observation equations, and the principle of UT, the unscented Kalman filter procedure is as follows [21][22][23][24][25][26]:…”

Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles

“…Recently, four-wheel-independently-actuated electric vehicles (FWIA EVs) have gained tremendous attention thanks to their potential for lower electricity consumption and better vehicle handling performance [5]. Four in-wheel motors are respectively installed in each wheel hub and can be collaboratively controlled for vehicle propulsion [6,7]. This distributed powertrain structure significantly simplifies the drivetrain by eliminating the transmission shaft, differential and final drive [8].…”

A novel hyperbolic tangent sliding mode observation of vehicle lateral force fed back by longitudinal force error

“…Therefore, an accurate observation of vehicle tyre force is an indispensable part of vehicle motion control [1][2][3][4][5][6]. In general, researches on the control methods of vehicle handling and stability mainly include: Hybrid adaptive chassis control [7,8], Kalman filter algorithm [9][10][11][12][13][14], random fusion algorithm [15][16][17][18], sliding mode control algorithm [19,20], twisting sliding-mode algorithm (Derbeli, M. et al) and super-twisting sliding-mode algorithm [21,22].…”