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Estimation of Center of Gravity Position for Distributed Driving Electric Vehicles Based on Combined H∞-EKF Method

机译:基于H∞-EKF方法的分布式驱动电动车辆重心估算重心位置

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It is essential to get the accurate knowledge of the center of gravity (CG) for the vehicle dynamics control systems, especially for distributed driving electric vehicles (DDEV), whose CG positions can be affected by the loading conditions. This paper focuses on a CG position estimator for a DDEV based on the combined H∞-extended Kalman filter (H∞-EKF) approach. The designed estimator consists of two parts: an H∞ estimator is used for filtering noisy states and an EKF is employed for estimating parameters. The H∞ filter minimizes the effects of undesirable noise in the filtered states with the disturbances whose statistics are unknown. Meanwhile, the EKF uses the filtered states from the H∞ filter and takes the parameters as random walks. Simulation results show that the proposed filter is capable of estimating the CG longitudinal location and the CG height with acceptable accuracy.
机译:对于车辆动力学控制系统的重心(CG)的准确知识是必要的,特别是对于分布式驱动电动车辆(DDEV),其CG位置可能受到负载条件的影响。 本文侧重于基于H∞扩展卡尔曼滤波器(H∞-EKF)方法的DDEV的CG位置估计器。 所设计的估算器由两部分组成:H∞估计器用于过滤噪声状态,使用EKF用于估计参数。 H∞过滤器最大限度地减少了过滤状态中不良噪声的影响,其统计数据未知。 同时,EKF使用来自H∞过滤器的过滤状态,并将参数作为随机散步。 仿真结果表明,所提出的滤波器能够以可接受的精度估计CG纵向位置和CG高度。

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