运动学与动力学融合的车辆状态估计方法  

Vehicle State Estimation Based on Kinematic Model and Dynamic Model Merging

作　　者：李克强[1] 罗禹贡[1] 边明远[1] 戴一凡[1] 陈龙[1] Li Keqiang;Luo Yugong;Bian Mingyuan;Dai Yifan;Chen Long(Tsinghua University)

机构地区：[1]清华大学

出　　处：《科技创新导报》2016年第27期186-186,共1页Science and Technology Innovation Herald

摘　　要：汽车状态参数的准确获得是保证汽车主动安全系统有效性的重要要求。分布式电驱动车辆的新型结构为传统基于动力学的状态参数估计方法的突破提供了可能。通过分析基于运动学与动力学方法各自不同的误差特性,该文提出了对两种估计方法的估计结果进行融合处理的分布式电驱动车辆状态参数估计方法。利用理论推导,证明了该方法将能够有效的提高不同工况下的估计精度,提高估计方法的工况适应性。为验证该方法的有效性,开发了Car Sim与Simulink联合仿真试验平台。仿真结果表明,所提出的误差加权的融合状态观测方法提高了分布式电驱动车辆状态参数观测精度和鲁棒性。Vehicle state parameters are essential for active safety control.Distributed electric vehicle with a new structure brings abreakthrough for the traditional dynamics state parameter estimation method.This paper presents a novel estimation method for distributedelectric drive vehicle by analyzing different characteristics of the estimation errors of kinematics and dynamics estimation methods.Thismethod merges the results of the two estimation methods with weighting coefficients.With a mathematical deduction,it shows that thismethod can effectively improve the estimation accuracy and applicability under different conditions.A CarSim and Simulink co-simulationtest platform is developed to verify the effectiveness of the method.Simulation results show that the proposed method improves the stateestimation accuracy and robustness of distributed electric drive vehicle state parameters.

关 键 词：分布式电驱动车辆 车辆状态估计 多方法融合 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]