面向混合动力汽车液压再生能量控制策略研究  

Research on Hydraulic Regenerative Energy Control Strategy for Hybrid Electric Vehicles

作　　者：郭丹丹[1] 黄玉萍 GUO Dandan;HUANG Yuping(School of Automobile and Transportation,Yancheng Industrial Vocational and Technical College,YanchengJiangsu,224005;School of Mechanical and Electronic Engineering,Nanjing Forestry University,NanjingJiangsu,210037)

机构地区：[1]盐城工业职业技术学院汽车与交通学院,江苏盐城224005 [2]南京林业大学机械电子工程学院,江苏南京210037

出　　处：《液压与气动》2024年第8期93-103,共11页Chinese Hydraulics & Pneumatics

基　　金：江苏省高等学校自然科学研究面上项目(19KJB 210003)。

摘　　要：针对混合动力汽车制动能量回收控制,提出了一种基于模糊Q学习的液压再生制动系统能量回收效率优化算法。建立了基于并联液压混合动力系统的汽车动力学模型,通过模糊Q学习法对汽车再生制动控制策略进行了优化。在此基础上建立了试验平台,验证液压再生制动仿真模型的准确性。与基于专家经验的模糊控制策略、动态规划算法相比,结果表明,经模糊Q学习算法优化的汽车节能率分别提高了9.62%和8.91%。A fuzzy Q-learning based optimization algorithm for energy recovery efficiency of hydraulic regenerative braking system is proposed for hybrid electric vehicle braking energy recovery control.Firstly,a vehicle dynamics model based on parallel hydraulic hybrid power system was established,and the regenerative braking control strategy of the vehicle was optimized using fuzzy Q-learning method.On this basis,an experimental platform was established to verify the accuracy of the hydraulic regenerative braking simulation model.Compared with the fuzzy control strategy and dynamic programming algorithm based on expert experience,the vehicle energy-saving rate optimized by the fuzzy Q-learning algorithm has increased by 9.62% and 8.91%,respectively.

关 键 词：混合动力汽车 模糊Q学习 并联液压 液压再生制动 能量回收 

分 类 号：TH137[机械工程—机械制造及自动化] U27[机械工程—车辆工程] U461.3[交通运输工程—载运工具运用工程]