@inproceedings{c486fd297d5f4c05874540641a8e9013,
title = "Integrated control of in-wheel-motored electric vehicles using a model predictive control method",
abstract = "A model predictive control (MPC) approach for the integrated control of active front steering (AFS), direct yaw moment control (DYC) and motor torque allocation in four in-wheel driving electric vehicles (EVs) is presented. A nonlinear vehicle model is formulated with nonlinear tire characteristic for MPC method, which can predict future system dynamics in predict horizon. And a cost function of the optimal control problem is defined over a receding horizon in order to meet the multiple control requirements taking hard constraints into account. The MPC scheme is composed of two parts: a high-level reference module related to driver steering commands, and a low-level MPC control allocation computing a sequence of control outputs to improve yaw stability performance at each sample time. The proposed controller is verified effectively on eight degrees of freedom (8DOF) nonlinear EVs model platform.",
keywords = "Electric vehicles, Integrated control, Model predictive control, Multiple control requirements, Nonlinear system",
author = "Bingtao Ren and Hong Chen and Haiyan Zhao and Weiwei Jin and Hao Li",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",
year = "2015",
month = mar,
day = "2",
doi = "10.1109/WCICA.2014.7052972",
language = "英语",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "March",
pages = "1676--1681",
booktitle = "Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014",
address = "美国",
edition = "March",
}