控制理论(社会学)
扭矩
控制器(灌溉)
粒子群优化
缓速器
动态制动
计算机科学
再生制动器
行驶循环
能量(信号处理)
工程类
汽车工程
控制(管理)
制动器
电动汽车
人工智能
数学
算法
功率(物理)
统计
物理
量子力学
生物
农学
热力学
作者
Ruijun Zhang,Wanzhong Zhao,Chunyan Wang,Kang Tai
出处
期刊:Energy
[Elsevier]
日期:2024-02-07
卷期号:293: 130568-130568
标识
DOI:10.1016/j.energy.2024.130568
摘要
Since the existing control strategy for electro-hydraulic composite braking (EHB) system is essentially "vehicle-centered", it is liable to cause incompatibility with current driving behavior, torque fluctuation and inconsistent braking feeling occur, which affects braking safety. In view of the above issue, a personalized MPC control strategy in accordance with the design methodology of characteristic E is presented. To do this, the data collection platform for characterizing driving behavior is constructed under typical vehicle-following conditions. Then, a generalized radial basis function (GRBF) neural network is adopted to accurately identify braking intensity of different driving behaviors. Next, an optimization model for the maximum energy recovery of EHB system is established in terms of required braking torque and motor speed, the distribution coefficient of braking torque is optimized by applying the adaptive particle swarm optimization (APSO) algorithm. Finally, the proposed personalized MPC control strategy is verified under different driving behaviors, the results display that: (1) the personalized MPC controller possess superiority of acquiring stable braking feeling, the torque tracking error is decreased by 96.8 %; (2) energy recovery for EHB system with optimized torque distribution is increased by 34.47 % under FTP-75 cycle conditions, and the response amplitude of braking feeling is increased by 5.6 %.
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