Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter

Xue Xue; Wang Zhenpo

doi:10.1016/j.egypro.2016.12.091

Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter

Xue Xue^*, Wang Zhenpo

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	538-543
Number of pages	6
Journal	Energy Procedia
Volume	104
DOIs	https://doi.org/10.1016/j.egypro.2016.12.091
Publication status	Published - 2016
Event	Applied Energy Symposium and Forum: Low - Carbon Cities and Urban Energy Systems, CUE 2016 - Jinan, China Duration: 13 Jun 2016 → 15 Jun 2016

Keywords

Extended Kalman Filter
distributed drive electric vehicle
state estimation

Access to Document

10.1016/j.egypro.2016.12.091

Cite this

@article{44d1d482078244d3af8718ee6db0f4a3,

title = "Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter",

abstract = "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.",

keywords = "Extended Kalman Filter, distributed drive electric vehicle, state estimation",

author = "Xue Xue and Wang Zhenpo",

note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier Ltd.; Applied Energy Symposium and Forum: Low - Carbon Cities and Urban Energy Systems, CUE 2016 ; Conference date: 13-06-2016 Through 15-06-2016",

year = "2016",

doi = "10.1016/j.egypro.2016.12.091",

language = "English",

volume = "104",

pages = "538--543",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter

AU - Xue, Xue

AU - Zhenpo, Wang

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Extended Kalman Filter

KW - distributed drive electric vehicle

KW - state estimation

UR - http://www.scopus.com/inward/record.url?scp=85013072481&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2016.12.091

DO - 10.1016/j.egypro.2016.12.091

M3 - Conference article

AN - SCOPUS:85013072481

SN - 1876-6102

VL - 104

SP - 538

EP - 543

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Forum: Low - Carbon Cities and Urban Energy Systems, CUE 2016

Y2 - 13 June 2016 through 15 June 2016

ER -

Distributed Drive Electric Vehicle State Estimation based on Extended Kalman Filter

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this