Estimation of Vehicle State and Road Coefficient for Electric Vehicle through Extended Kalman Filter and RLS Approaches

DOI

10.2991/emeit.2012.491How to use a DOI?

Keywords

Electric vehicle, extended Kalman filter (EKF), estimation of vehicle state and road coefficient, recursive least squares (RLS).

Abstract

Estimation of vehicle state (e.g., vehicle velocity and sideslip angle) and road friction coefficient is essential for electric vehicle stability control. This article proposes a novel real-time model-based vehicle estimator, which can be used for estimation of vehicle state and road friction coefficient for the distributed driven electric vehicle. The estimator is realized using the extended Kalman filter (EKF) and the recursive least squares (RLS) technique. The proposed estimation algorithm is evaluated through simulation and experimental test. Results to data indicate that the proposed approach is effective and it has the ability to provide with reliable information for vehicle active safety control.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cheng LIN
AU  - Gang WANG
AU  - Wan-ke CAO
AU  - Feng-jun ZHOU
PY  - 2012/09
DA  - 2012/09
TI  - Estimation of Vehicle State and Road Coefficient for Electric Vehicle through Extended Kalman Filter and RLS Approaches
BT  - Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
PB  - Atlantis Press
SP  - 2216
EP  - 2220
SN  - 1951-6851
UR  - https://doi.org/10.2991/emeit.2012.491
DO  - 10.2991/emeit.2012.491
ID  - LIN2012/09
ER  -