分布式驱动电动汽车横向稳定性自适应优化控制  

Adaptive optimization control for lateral stability ofdistributed drive electric vehicle

作　　者：丁宁 贝绍轶[1] 李波[1,2] 汤浩然[1] 殷国栋 DING Ning;BEI Shaoyi;LI Bo;TANG Haoran;YIN Guodong(School of Automotive and Transportation Engineering,Jiangsu University of Technology,Changzhou 213001,China;Suzhou Automotive Research Institute,Tsinghua University,Suzhou 215200,China;School of Mechanical Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]江苏理工学院汽车与交通工程学院,江苏常州213001 [2]清华大学苏州汽车研究院,江苏苏州215200 [3]东南大学机械工程学院,南京210096

出　　处：《重庆理工大学学报（自然科学）》2024年第8期64-73,共10页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(52172367);江苏省高校自然科学基金重大项目(21KJA580001);常州市国际科技合作基金项目(CZ20220031);江苏省研究生科研与实践创新计划(SJCX23_1608)。

摘　　要：为改善在极限状态下分布式驱动电动汽车的横向稳定性,提出了一种基于自适应调节模型预测控制(SAMPC)输入权重的控制方法。基于相平面失稳判断,将相平面分为稳定域、稳定边界和失稳域,设计影响因子,以备计算附加横摆力矩和转矩分配权重;考虑质心侧偏角、横摆角速度对车辆稳定性的重要影响,设计SAMPC中的输入权重,获取准确的附加横摆力矩;同时提出可以缩短优化时间的转矩分配方法,设计多目标函数用于量化车辆的稳定性和动力性。仿真结果显示:与模型预测控制(MPC)相比,采用SAMPC,在μ=0.4、v_(x)=70 km/h时,质心侧偏角的平均绝对误差减少49.4%,均方根误差减少55.3%,改善效果最佳;在μ=0.2、v_(x)=90 km/h时,质心侧偏角的平均绝对误差减少24.5%,均方根误差减少23.8%,改善效果最弱。To enhance the lateral stability of distributed drive electric vehicles under extreme conditions,this paper introduces a control methodology leveraging SAMPC Adaptive Model Predictive Control(SAMPC)input weighting.Utilizing phase plane analysis for instability assessment,the method divides the phase plane into stability,boundary,and instability regions,assigning weights for calculating additional yaw moment and torque.Recognizing the pivotal influence of side yaw angle and yaw velocity on vehicle stability,the SAMPC input weighting method is devised to precisely determine the additional yaw moment.Additionally,a torque distribution approach is proposed to expedite optimization,with multiple objective functions devised to gauge vehicle stability and power performance.Our simulation results demonstrate SAMPC significantly outperforms MPC,with a notable 49.4%reduction in average absolute error and 55.3%decrease in root-mean-square error for centroid side deflection angle inμ=0.4,v_(x)=70 km/h.Conversely,for the side deviation angle of the center of mass,the average absolute error diminishes by 24.5%,and the root-mean-square error by 23.8%,showing a comparatively weaker enhancement effect inμ=0.2,v_(x)=90 km/h.

关 键 词：极限工况 DYC系统 模型预测控制 优化分配 

分 类 号：U463.341[机械工程—车辆工程]