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Open AccessArticle
Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC
by
Jiading Bao
Jiading Bao
Jiading Bao, male, Doctor of Engineering, Associate Professor, M.S. Supervisor, Member of Chinese of [...]
Jiading Bao, male, Doctor of Engineering, Associate Professor, M.S. Supervisor, Member of Chinese Society of Mechanical Engineering (CSME). He received his B.S. degree in 2004, M.S. degree in 2010, and Ph.D. degree in 2021. He is mainly engaged in the research of low-dimensional semiconductor materials and devices and vehicle dynamics and control.
,
Zishan Lin
Zishan Lin ,
Hui Jing
Hui Jing
Hui Jing, male, Doctor of Engineering, researcher, doctoral supervisor. He received his master’s a [...]
Hui Jing, male, Doctor of Engineering, researcher, doctoral supervisor. He received his master’s degree in engineering in 2008 and pursued his doctoral degree in Vehicle Engineering at Southeast University from 2013 to 2017. He then conducted post-doctoral research in Shanghai Jiao Tong University. Then, he was a visiting professor at Tsinghua University from 2022 to 2023. His research interests include new energy vehicles, vehicle dynamics and control, unmanned driving, and robust control.
*,
Huanqin Feng
Huanqin Feng ,
Xiaoyuan Zhang
Xiaoyuan Zhang and
Ziqiang Luo
Ziqiang Luo
Faculty of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(19), 8648; https://doi.org/10.3390/su16198648 (registering DOI)
Submission received: 3 September 2024
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Revised: 29 September 2024
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Accepted: 5 October 2024
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Published: 6 October 2024
Abstract
In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability.
Share and Cite
MDPI and ACS Style
Bao, J.; Lin, Z.; Jing, H.; Feng, H.; Zhang, X.; Luo, Z.
Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC. Sustainability 2024, 16, 8648.
https://doi.org/10.3390/su16198648
AMA Style
Bao J, Lin Z, Jing H, Feng H, Zhang X, Luo Z.
Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC. Sustainability. 2024; 16(19):8648.
https://doi.org/10.3390/su16198648
Chicago/Turabian Style
Bao, Jiading, Zishan Lin, Hui Jing, Huanqin Feng, Xiaoyuan Zhang, and Ziqiang Luo.
2024. "Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC" Sustainability 16, no. 19: 8648.
https://doi.org/10.3390/su16198648
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