Abstract
Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
Highlights
Electric vehicles (EVs), are a promising pattern of future transportation, which possess great advantages in fuel economy and emissions, and drawn lots of attention of both researchers and companies [1,2,3]
In the sideslip angle estimation, the longitudinal forces estimated by longitudinal force observer (LFO) are regarded as pseudo-measurements, the Luenberger observer (LO) is devised for transcendental estimation using less sensor measurements, the extended Kalman filter (EKF) is designed for a posteriori estimation with higher accuracy, the fuzzy weight which is changed with the variation of steering wheel angle and vehicle speed is applied to enhance the adaptive ability of vehicle state estimation
The estimation of estimation of sideslip angle is testified by the road test results, in which the estimated longitudinal estimation of sideslip angle is testified by the road test results, in which the estimated longitudinal sideslip angle is testified by the road test results, in which the estimated longitudinal forces by
Summary
Electric vehicles (EVs), are a promising pattern of future transportation, which possess great advantages in fuel economy and emissions, and drawn lots of attention of both researchers and companies [1,2,3]. Is introduced into the vehicle modeling process for longitudinal force estimation, the longitudinal force reconstruction equation is obtained by decoupling EDWM, and the nonlinear observer and high-order sliding mode observer [35,36,37] (HSMO) are combined to design the longitudinal force observer (LFO), and the Kalman filter (KF) is used to achieve an unbiased estimation of longitudinal force with the consideration of the in-system noise This design provides a novel way of thinking about longitudinal force estimation in 4WID-EVs. In the sideslip angle estimation, the longitudinal forces estimated by LFOs are regarded as pseudo-measurements, the Luenberger observer (LO) is devised for transcendental estimation using less sensor measurements, the extended Kalman filter (EKF) is designed for a posteriori estimation with higher accuracy, the fuzzy weight which is changed with the variation of steering wheel angle and vehicle speed is applied to enhance the adaptive ability of vehicle state estimation.
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