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Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles

Recent investigations of the electric braking booster (E-Booster) focus on its potential to enhance brake energy regeneration. A vehicle’s hydraulic system is composed of the E-Booster and electric stability control to control the master cylinder and wheel cylinders. This paper aims to address the i...

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Autores principales: Mei, Mingming, Cheng, Shuo, Mu, Hongyuan, Pei, Yuxuan, Li, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941334/
https://www.ncbi.nlm.nih.gov/pubmed/36824984
http://dx.doi.org/10.3389/frobt.2023.1078253
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author Mei, Mingming
Cheng, Shuo
Mu, Hongyuan
Pei, Yuxuan
Li, Bo
author_facet Mei, Mingming
Cheng, Shuo
Mu, Hongyuan
Pei, Yuxuan
Li, Bo
author_sort Mei, Mingming
collection PubMed
description Recent investigations of the electric braking booster (E-Booster) focus on its potential to enhance brake energy regeneration. A vehicle’s hydraulic system is composed of the E-Booster and electric stability control to control the master cylinder and wheel cylinders. This paper aims to address the independent closed-loop control of the position and pressure as well as the maintenance of the pedal feel. To track both the reference signals related to piston displacement and the wheel cylinder pressure, an explicit model predictive control (MPC) is developed. First, the new flow model is introduced as the foundation for controller design and simulation. Next, in accordance with the operational conditions, the entire system is divided into three switchable subsystems. The three distributed MPCs are constructed based on the linearized subsystems, and a state machine is used to perform the state jump across the controllers. A linear piecewise affine control law can then be obtained by solving the quadratic program (QP) of explicit MPC. Afterwards, the non-linear extended Kalman filter including the recorded time-variant process noise is used to estimate all the state variables. The effectiveness of the explicit MPC is evidenced by the simulations compared with a single MPC in regenerative and dead-zone conditions. The proposed controller decreases the latency significantly by 85 milliseconds, which also helps to improve accuracy by 22.6%. Furthermore, the pedal feel remains consistent, even when factoring in the number of vibrations caused by the inherent hydraulic characteristic of pressure versus volume.
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spelling pubmed-99413342023-02-22 Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles Mei, Mingming Cheng, Shuo Mu, Hongyuan Pei, Yuxuan Li, Bo Front Robot AI Robotics and AI Recent investigations of the electric braking booster (E-Booster) focus on its potential to enhance brake energy regeneration. A vehicle’s hydraulic system is composed of the E-Booster and electric stability control to control the master cylinder and wheel cylinders. This paper aims to address the independent closed-loop control of the position and pressure as well as the maintenance of the pedal feel. To track both the reference signals related to piston displacement and the wheel cylinder pressure, an explicit model predictive control (MPC) is developed. First, the new flow model is introduced as the foundation for controller design and simulation. Next, in accordance with the operational conditions, the entire system is divided into three switchable subsystems. The three distributed MPCs are constructed based on the linearized subsystems, and a state machine is used to perform the state jump across the controllers. A linear piecewise affine control law can then be obtained by solving the quadratic program (QP) of explicit MPC. Afterwards, the non-linear extended Kalman filter including the recorded time-variant process noise is used to estimate all the state variables. The effectiveness of the explicit MPC is evidenced by the simulations compared with a single MPC in regenerative and dead-zone conditions. The proposed controller decreases the latency significantly by 85 milliseconds, which also helps to improve accuracy by 22.6%. Furthermore, the pedal feel remains consistent, even when factoring in the number of vibrations caused by the inherent hydraulic characteristic of pressure versus volume. Frontiers Media S.A. 2023-02-07 /pmc/articles/PMC9941334/ /pubmed/36824984 http://dx.doi.org/10.3389/frobt.2023.1078253 Text en Copyright © 2023 Mei, Cheng, Mu, Pei and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Mei, Mingming
Cheng, Shuo
Mu, Hongyuan
Pei, Yuxuan
Li, Bo
Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title_full Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title_fullStr Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title_full_unstemmed Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title_short Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles
title_sort switchable mpc-based multi-objective regenerative brake control via flow regulation for electric vehicles
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941334/
https://www.ncbi.nlm.nih.gov/pubmed/36824984
http://dx.doi.org/10.3389/frobt.2023.1078253
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