基于电驱动系统的农业车辆牵引负荷车设计与试验

针对传统农业车辆牵引负荷车机械结构复杂、存在加载死区导致无法实现全范围加载,采集系统功能单一无法实时评估被试车辆牵引性能的问题,设计了一种基于电驱动系统的农业车辆牵引负荷车。负荷车以最大加载牵引力150 kN为设计目标,结合对驱动轮的受力分析,完成了其整机关键部件的选型设计,采用集成发动机-电动桥的电驱动系统为核心单元,使用转向牵引架实现前桥平台的自动跟随转向。在LabVIEW RIO架构基础上,通过FPGA搭建高算力、高性能的测控系统,实现对电驱动系统电流、电压、被试车辆牵引力、油耗等多种信息的采集、无线传输与存储,并使用模糊自适应PID控制算法对牵引力加载进行闭环控制。最后开展整机性能验证试验,负荷车实现了0～150 kN范围内的负荷加载,加载系统最大响应时间为3.6 s,最大超调量为1.61%,实际加载牵引力与目标牵引力最大误差为4.5%。整机性能验证试验表明,负荷车具备良好的牵引负荷加载性能,其测控系统可实现被试车辆牵引性能多参数的实时准确监测,能够完成对农业车辆牵引性能的全面评估。

Design and Test of Agricultural Vehicle Traction Load Vehicle Based on Electric Drive System

In response to the problems of complex mechanical structures and dead zones in loading, as well as a single-function collection system that cannot evaluate traction performance in real-time for traditional agricultural vehicles, an electric-driven agricultural vehicle traction load vehicle was developed. Combining the targeted design of a maximum loading traction force of 150kN with a force analysis of the driving wheel, key components of the load vehicle were selected and designed. An integrated engine-electric bridge electric drive system was used as the core unit, and a steering traction frame was innovatively used to achieve automatic steering of the front platform. Based on the LabVIEW RIO architecture, a high-performance measurement and control system was built using FPGA, enabling the collection, wireless transmission, and storage of multiple information, such as electric current, voltage, traction force, and fuel consumption. The fuzzy adaptive PID control algorithm was used for closed-loop control of traction force loading. Finally, the load vehicle was subjected to performance verification testing, achieving a range of 0~150kN load loading, with maximum response time of 3.6s, maximum overshoot of 1.61%, and maximum error of 4.5% between actual and targeted traction forces. The test results showed that the load vehicle had good traction load performance and the measurement and control system can accurately monitor multiple parameters of traction performance in real-time, providing comprehensive evaluation of agricultural vehicle traction performance.