@inproceedings{4a98322c82ff4733afa9d6b3c44e79ec,
title = "A Velocity Tracking Control Method for Electric Vehicle Based on Model Predictive Control",
abstract = "For the velocity tracking control of electric vehicle, the classical control algorithm is difficult to obtain high precision. Modern control algorithms can improve the control accuracy but they require accurate vehicle dynamics modeling. For this situation, a hierarchical model predictive control(MPC) strategy is proposed. Compared with traditional vehicles, electric vehicles are driven by motors, which are more suitable for model predictive control.In the proposed strategy, the controller can adaptively adjust the acceleration to track the expected velocity without accurate vehicle model.An upper MPC controller is designed to uses the expected velocity and the actual velocity to calculate the expected acceleration. The lower controller establishes the vehicle inverse dynamics model to calculate the expected opening degree of the accelerator pedal and the braking pressure through the Inverse dynamics model of vehicle. Simulation results demonstrate that the the controller has a fast response and accurate tracking performance without overshoot.",
keywords = "Electric Vehicle, Model Predictive Control, Velocity Tracking",
author = "Shanhao Feng and Liling Ma and Tao Chen and Shoukun Wang and Junzheng Wang",
note = "Publisher Copyright: {\textcopyright} 2021 Technical Committee on Control Theory, Chinese Association of Automation.; 40th Chinese Control Conference, CCC 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
year = "2021",
month = jul,
day = "26",
doi = "10.23919/CCC52363.2021.9550199",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6070--6075",
editor = "Chen Peng and Jian Sun",
booktitle = "Proceedings of the 40th Chinese Control Conference, CCC 2021",
address = "United States",
}