CAJ | 학술논문

为实现高机动工况下车辆状态的可靠估计，提出了一种基于改进的扩展卡尔曼滤波的车辆运行状态估计方法．首先建立基于非线性车辆动力学的系统状态模型，该模型分别以低成本的车载轮速和方向盘转角传感器信息作为系统的观测量和外部输入量；然后通过改进的卡尔曼滤波递推算法高精度地推算出汽车的关键运行状态．仿真试验表明，所提出的方法既可适应一般机动环境也可适应较高机动环境．此外，该方法可显著提高直测量的精度，并可实现对质心侧偏角、侧向速度等难以直测量的准确估计，质心侧偏角估计误差小于3×10-3 rad，速度估计精度小于0．1 m/s．
위실현고궤동공황하차량상태적가고고계，제출료일충기우개진적확전잡이만려파적차량운행상태고계방법．수선건립기우비선성차량동역학적계통상태모형，해모형분별이저성본적차재륜속화방향반전각전감기신식작위계통적관측량화외부수입량；연후통과개진적잡이만려파체추산법고정도지추산출기차적관건운행상태．방진시험표명，소제출적방법기가괄응일반궤동배경야가괄응교고궤동배경．차외，해방법가현저제고직측량적정도，병가실현대질심측편각、측향속도등난이직측량적준학고계，질심측편각고계오차소우3×10-3 rad，속도고계정도소우0．1 m/s．
To realize the reliable estimation of vehicle state in critical driving maneuvers, an estima-tion method based on improved extended Kalman filter is developed.First, the system state model is established based on the nonlinear vehicle dynamics, and the information determined by the low-cost wheel speed sensors and that by the steering wheel angle sensor are used as the observation and the system input, respectively.Then through improving the extended Kalman filtering recursion algo-rithm, the key states of a vehicle are determined.The simulation results demonstrate that the proposed method can adapt to both common and critical driving-maneuvers situations.Besides, it can signifi-cantly improve the accuracy of the states that can be directly measured and can also accurately estimate the states which are difficult to measure directly such as sideslip angle and lateral speed.The estima-tion errors of sideslip angle and velocity are less than 3 ×10-3 rad and 0.1 m/s, respectively.