基于多源数据结构融合的车轮滑转率测量方法

The Vehicle Wheels Slip rate Measure Method Based on Multi-source Data Structured Fusion

摘要:

滑转率是轮式车辆运动状态的重要参数,该值过大严重影响牵引效率、油量消耗以及行驶安全。实时精准检测车轮瞬时滑转率是车辆优化控制的关键技术。当车辆在被各种土质或植被覆盖的田地里或复杂地形的环境中低速作业时,传统方法难以精准测量该动态参数。文中采用新测量方法：采集卫星导航、微惯导和轮速等多源信息；依据检测点的空间结构关系,建立多源数据结构融合算法(Multi-source Data Structured Fusion, MDSF)；实时地测量各车轮的瞬时滑转率。模拟仿真与实测试验的结果表明：(1)轮向速度相对误差2.43%,轮转速相对误差0.54%,使滑转率误差小于3.00%。(2)实测数据准确地反映车辆运动中的动力学关系,实时地体现左右车轮各自的动态特性。

Abstract:

The Wheels Slip rate is important parameter of vehicle movement state, when it is larger will seriously affect the traction efficiency, fuel consumption and driving safety. Real-time accurate detection of instantaneous wheel slip rate is the key technology of vehicle optimization control. When vehicle running at low speed in the field it's surface is covered by variety of soil and vegetation or other complex landform environment, the traditional method is difficult to accurately measure this dynamic parameter. The paper use a new method for measuring: Collect satellite navigation, micro inertial navigation and wheel speed etc. multi-source information; Based on the spatial structure relations between two measurement point, established Multi-source Data Structured Fusion algorithm(MDSF); Measure the instantaneous slip rate of each wheel in real-time. Simulation and experimental results show that: (1) The wheels move speed relative error is less than 2.43%, the wheel rotation speed relative error is less than 0.54%, the wheel slip rate error is less than 3.00%. (2) The measured data accurately presents the dynamic characteristics of the vehicle in motion and reflects the dynamic states of each wheel in real time.