Multirate Estimation and Control of Body Slip Angle for Electric Vehicles Based on Onboard Vision System

Abstract

A new method for vehicle body-slip-angle estimation using nontraditional sensor configuration and system model is proposed, which enables robust estimation against vehicle parameter uncertainties. In this approach, a linear vehicle bicycle model is augmented with a simple visual model. As the visual model contains few uncertain parameters and increases the observer's design freedom, the combined model-based estimator provides more accurate estimation result compared with the traditional bicycle-model-based one. However, two issues are raised by the combined vehicle and vision models: 1) image processing introduces delay in the visual measurements, and 2) the sampling time of a normal camera is much longer than that of other onboard sensors. For electric vehicles, the control period of motors is much shorter than the sampling time of a normal camera. Considering the aforementioned delay and multirate problems, a multirate Kalman filter with intersample compensation is designed, and the estimation performance can be improved during the sampling intervals of the vision system. Then, a two-degree-of-freedom controller is designed using the estimated body slip angle as feedback for reference tracking. With the proposed multirate estimator, the controller achieves better tracking performance than the singlerate method. The effectiveness of the proposed estimator and controller is demonstrated by both simulations and experiments.Index Terms-Body slip angle, electric vehicle (EV), measurement delay, multirate estimation, vehicle motion control, vision system.

Optimising Quality-of-Control for Data-Intensive Multiprocessor Image-Based Control Systems Considering Workload Variations

“…Generally, the analysis of the image processing step is limited to a measurement or estimate of the computational delay and is assumed to be negligible compared to other delays in the control loop [2][3] [20]. Control engineers tackle a long sample period of IBC systems using state estimation [12], robust design [13], predictive control [14], observer-based [1], multi-rate sampling [26] and reconfigurable pipelining methods [15]. However, these approaches do not model and consider platform constraints like resource availability and mapping, and/or workload variations in image processing [1].…”

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

“…There are several studies related with vehicle sideslip angle estimations in the literature [178][179][180][181][182][183][184][185][186][187][188][189][192][193][194]. In [179], a nonlinear tire model based observer (NTMBO) is developed for estimating the vehicle body slip angle.…”

“…In [178,184], an RLS based vehicle sideslip angle estimation algorithm is studied by using the real-time lateral tire force measurements obtained from the multisensing hub units. In [185], the vehicle body slip angle observer is designed based on the multirate Kalman filter (MKF) by using the combined vehicle and vision models. In [186], a comparison of four different observer methods to estimate the sideslip angle such as Linear Observer (LO), extended Kalman filter-based nonlinear observer (EKFNO), extended Luenberger observer (ELO) and SMO are presented.…”

A strong robust observer of distributed drive electric vehicle states based on strong tracking-iterative central difference Kalman filter algorithm

“…[6][7][8][9][10] It is commonly recognized that performance of the control systems heavily depends on 1 knowledge of vehicle states information, which characterizes longitudinal and lateral stability of vehicles, such as yaw rate, sideslip angle, and longitudinal velocity. [11][12][13][14][15] For DDEV, it is measurement of sideslip angle and longitudinal velocity that needed expensive and dedicated sensors directly. Therefore, for cost savings and practical application considerations, it is necessary to propose a soft measurement approach to estimate DDEV states with high precision based on commonly low-cost sensors of production vehicle such as accelerometer and steering angle sensor.…”