Dynamic Loading System of an Agricultural Vehicle Power Wagon Based on Complex Loading

Abstract

A dynamic loading system of a power wagon is studied to solve the problems of low loading precision, slow response speed, and poor adaptive capability of a power wagon in an agricultural vehicle traction test. The load feature of an engine is analyzed under different gear locations, governor handle positions, and vehicle speeds. Meanwhile, the load feature of an eddy current retarder is examined based on a backpropagation (BP) neural network proportional-integral-derivative (PID) control algorithm. The mathematical model for the loading system is established. The time domain model for traction is refactored by a white noise filter according to the power spectral density function. To improve the dynamic loading performance of the eddy current retarder, the BP neural network PID control algorithm is used to change its exciting voltage to achieve an accurate simulation of the actual working load. The effectiveness and practicability of this control algorithm are validated by comparing its system output response dynamic characteristics with those of the traditional PID control algorithm. Simulation results show that the output error is less than 2.8% and the maximum delay is 56.5 ms when the BP neural network PID control is used. On the basis of this result, the traction property of a tractor with several wheels is tested on the road. The test and analysis results indicate that the output loads of the loading system accurately simulate the field actual load of the tractor. The maximum output load value of the system is 42-48 kN and the applicable speed range is 0-25 km/h. The output error is less than 3.5% and the maximum delay is 85.6 ms.