基于鲸鱼优化算法-支持向量回归的汽车运动状态估计  

Vehicle Motion State Estimation Based on WOA-SVR

作　　者：尤勇 孟云龙 吴景涛 王长青 YOU Yong;MENG Yunlong;WU Jingtao;WANG Changqing(College of Mechanical Engineering,Hebei University of Technology,Tianjin,300400;Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles,Tianjin,300131;CATARC Automotive Test Center(Tianjin)Co.,Ltd.,Tianjin,300300)

机构地区：[1]河北工业大学机械工程学院,天津300400 [2]天津市新能源汽车动力传动与安全技术重点实验室,天津300131 [3]中汽研汽车检验中心(天津)有限公司,天津300300

出　　处：《中国机械工程》2024年第6期973-981,992,共10页China Mechanical Engineering

基　　金：天津市教委科研项目(2023KJ298);国家自然科学基金(52205052)。

摘　　要：为了不依赖动力学模型精度而准确地获取车辆运动状态信息,提出一种基于鲸鱼优化算法-支持向量回归(WOA-SVR)的车辆状态估计算法。首先通过分析车辆动力学基本特性,设计了侧向速度、横摆角速度与车速分离的支持向量回归估计架构;然后对支持向量回归(SVR)模型进行多种行驶工况组成的数据集训练,在训练过程中运用鲸鱼优化算法对松弛变量中的惩罚因子c与核函数参数g进行寻优;最后对估计算法进行单移线、扫频试验虚拟仿真和实车ABS制动、双移线试验验证。结果表明,该算法有效提高了估计精度,且对车速的变化具有鲁棒性,可以实现准确的不依赖动力学模型精度的汽车运动状态估计。In order to accurately obtain vehicle motion state information without relying on the accuracy of the dynamics model,a vehicle state estimation algorithm was proposed based on WOA-SVR.Firstly,by analyzing the basic characteristics of vehicle dynamics,a SVR architecture was designed for estimating the separation of lateral velocity,yaw rate,and vehicle speed.Then,the SVR model was trained on a dataset composed of multiple driving conditions,and the WOA was used to optimize the penalty factor c and kernel function parameter g in the relaxation variables during the training processes.Finally,the estimation algorithm was validated through virtual simulation of single line shift and frequency sweep tests,as well as ABS braking and double line shift actual vehicle tests.The results show that this algorithm effectively improves estimation accuracy and is robust to changes in speed,enabling accurate estimation of vehicle motion states without relying on dynamics models.

关 键 词：车辆状态估计 动力学模型 机器学习 支持向量回归 鲸鱼优化算法 

分 类 号：U461.1[机械工程—车辆工程]