%0 Journal Article
%T 基于模型预测控制的电动汽车电液复合制动力分配策略研究<br>Research on Electro-Hydraulic Composite Braking Force Distribution Strategy of Electric Vehicle Based on Model Predictive Control
%A 张韦阳
%A 郑竹安
%A 秦亚宁
%A 张坤
%J Modeling and Simulation
%P 112-122
%@ 2324-870X
%D 2024
%I Hans Publishing
%R 10.12677/MOS.2024.131012
%X 为了提高纯电动汽车制动过程中的能量回收效率和制动的稳定性，通过对电动汽车复合制动系统结构及工作原理进行分析，设计了基于模糊算法的路面识别器，跟踪路面峰值附着系数，以获得制动器的最大制动力。提出了一种基于模型预测控制(Model Predictive Control, MPC)跟踪最佳滑移率的制动力分配的策略方法。从而达到能量回收效率的最大化，并利用AVL/CRUISE和MATLAB/Simulink仿真环境开展联合仿真分析。结果表明：所提出的控制策略与优化前的控制策略对比，保证了车辆制动稳定性的同时，制动距离更小，制动时间更短，电机制动力矩更大，电池荷电状态(State of Charge, SOC)值下降地更为缓慢，在一个FTP、New European Driving Cycle (NEDC)循环工况下电池荷电状态值分别显著提高了0.66%、0.46%。<br />
In order to improve the energy recovery efficiency and braking stability during the braking process of pure electric vehicles, a road recognition device based on a fuzzy algorithm was designed by ana-lyzing the structure and working principle of the electric vehicle composite braking system to track the peak adhesion coefficient of the road surface and obtain the maximum braking force of the brake. A strategy method based on model predictive control (MPC) was proposed to track the opti-mal slip rate of brake force distribution. In this way, energy recovery efficiency is maximized and collaborative simulation analysis is conducted using AVL/CRUISE and MATLAB/Simulink simula-tion environments. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the braking stability of the vehicle, but also has a smaller braking distance, shorter braking time, larger motor braking torque, and slower decrease in battery state of charge (SOC) value. Under FTP and new European driving cycle (NEDC) conditions, the battery charge state value significantly increased by 0.66% and 0.46%, respectively.
%K 模型预测控制，路面识别器，能量回收<br>Model Predictive Control
%K Road Identifier
%K Energy Recovery
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=78846