Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter

被引：1

作者：

Xue Xue ^{[1
,2
]}

Wang Zhenpo ^{[1
,2
]}

机构：

[1] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China

[2] Beijing Inst Technol, Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China

来源：

CLEAN ENERGY FOR CLEAN CITY: CUE 2016 - APPLIED ENERGY SYMPOSIUM AND FORUM: LOW-CARBON CITIES AND URBAN ENERGY SYSTEMS | 2016年 / 104卷

关键词：

state estimation; distributed drive electric vehicle; Extended Kalman Filter;

D O I：

10.1016/j.egypro.2016.12.091

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

This paper researched an estimation method based on Extended Kalman Filter (EKF) for distributed drive electric vehicle states. A 7 DOF closed-loop vehicle simulation platform including driver model of preview follower method and 'Magic formula' tire model was established. A general 2-input - 1-output and 3 states estimation system was established, considering the white Gauss measurement noise. The estimation algorithm was applied to a four-motor driven vehicle during a double-lane-change process. The results showed that EKF estimator could effectively estimate the states of distributed drive electric vehicle with varying speed under simulative experimental condition. (C) 2016 The Authors, Published by Elsevier Ltd.

引用

页码：538 / 543

页数：6

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