In this paper, a real-time estimator, based on an extended Kalman filter (EKF), for the position of vehicle center of gravity (CG) is proposed. Accurate knowledge of the CG longitudinal location and the CG height in the vehicle frame is helpful to the control of vehicle motions, especially for lightweight vehicles (LWVs), whose CG positions can be substantially varied by freight goods or passengers onboard. The proposed estimation method, unlike many existing ones, extracts signals only from vehicle longitudinal maneuvers in which road course elevation may exist. A three-state vehicle dynamic model, including the longitudinal velocity, the front-wheel angular speed, and the rear-wheel angular speed of the vehicle, is employed in the EKF formulation. With the help of the GPS altitude measurement, the road grade, which provides excitation for the estimation of the CG height, can also be obtained using a typical Kalman filter. Simulation studies based on a CarSim® vehicle model show that the proposed estimator is capable of accurately estimating both the CG longitudinal location and the CG height without a priori knowledge of the tire-road contact condition. Moreover, though the performance of the CG height estimation largely depends on the road grade variations, the CG longitudinal location can always be accurately estimated, even on a horizontal road.
- Dynamic Systems and Control Division
EKF-Based Vehicle Center of Gravity Position Real-Time Estimation in Longitudinal Maneuvers With Road Course Elevation
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Huang, X, & Wang, J. "EKF-Based Vehicle Center of Gravity Position Real-Time Estimation in Longitudinal Maneuvers With Road Course Elevation." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 665-672. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8530
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