CAJ | 학술논문

本文中对分布式驱动电动汽车的状态估计进行研究。首先利用龙伯格状态观测器实时观测对车辆的状态估计影响较大的路面坡度，接着，提出了采用扩展卡尔曼滤波算法，以车辆ESP传感器所获取的数据信息作为观测值，对分布式驱动电动汽车的动力学状态变量进行估计。最后进行Carsim和MATLAB联合仿真。结果表明，基于扩展卡尔曼滤波和龙伯格观测器的车辆状态估计算法能较好的估算出车辆的相关动力学状态值，算法可行，收敛速度较快。
본문중대분포식구동전동기차적상태고계진행연구。수선이용룡백격상태관측기실시관측대차량적상태고계영향교대적로면파도，접착，제출료채용확전잡이만려파산법，이차량ESP전감기소획취적수거신식작위관측치，대분포식구동전동기차적동역학상태변량진행고계。최후진행Carsim화MATLAB연합방진。결과표명，기우확전잡이만려파화룡백격관측기적차량상태고계산법능교호적고산출차량적상관동역학상태치，산법가행，수렴속도교쾌。
A study on vehicle state estimation is conducted for a distributed-drive electric vehicle in this paper. Firstly Luenberger observer (LO) is adopted to observe the road slope, which has significant effects on vehi-cle state estimation. Then extended Kalman filter ( EKF) algorithm is used with the data information obtained from ESP sensor as observed value, the dynamics state variables of distributed-drive electric vehicle are estimated. Final-ly a Carsim-Matlab co-simulation is performed. The results show that the proposed vehicle state estimation algorithm based on LO and EKF is feasible and can well estimate the relevant dynamics state variables of vehicle with a rather high convergence speed.