A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles. (15th May 2017)

• Record Type: Journal Article
• Authors: Lin, Cheng; Gong, Xinle; Xiong, Rui; Cheng, Xingqun
• Is Part Of: Applied energy. Volume 194(2017)
• Page Start: 609
• Page End: 616
• Publication Date: 2017-05-15
• Subjects: H∞ filter -- Extended Kalman filter (EKF) -- Center of gravity (CG) position -- Electric vehicles (EVs); Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.05.040
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A vehicle state estimation method considering noise uncertainty is proposed. Comparison of two filtering algorithms for state estimation is conducted. A longitudinal acceleration model based road slope estimation method is proposed. A joint H∞ –EKF algorithm is proposed for estimating the center of gravity position. Results indicate that the proposed approach shows good estimation performance. Abstract: In order to ensure the safety and reliability of electric vehicles (EVs), the accurate center of gravity (CG) position estimation is of great significance. In this study, a novel approach based on combined H∞ –extended Kalman filter (H∞ –EKF) is proposed. Utilizing the characteristics of the wheel torque controlled independently, the estimation method only requires the longitudinal stimulus of vehicles and avoids other possible disadvantageous stimulus, such as the vehicle yaw or roll motion. Furthermore, additional parameters (suspension parameters, tire parameters, etc.) are unessential. To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation considering of the front wheel speed, the rear wheel speed and the longitudinal velocity of the vehicle. The designed estimator consists of two layers: the H∞ estimator is employed to filter states by means of minimizing the influence of unexpected noise whose statistics are unknown. Simultaneously, the other EKF estimator uses the states derived by the former filter to identify the CG position of the vehicle. Results indicate that the performance of the H∞ filter is superior to the standard KF and the proposed synthetic estimation algorithm is able to estimate the longitudinal location and the height of CG with acceptable accuracy.