Intelligent energy adaptive control of loader shoveling system

Loaders are often faced with various working objects during the shoveling process. The differences in working resistance and its time-varying unpredictability when shoveling different objects are the main causes of high energy consumption during the shoveling stage. In this paper, through the analysis of the shoveling process, the influence of the compacted layer on the working resistance is obtained. The constructed Discrete Element Method (DEM) simulation model is used to elucidate that the timely lifting of the boom can have a destructive effect on the compacted layer. Moreover, considering the diversity of working objects, a study was carried out on the effect of different boom lifting ranges on the destruction of the compacted layer. The loader shoveling system's intelligent Energy Adaptive Control (EAC) strategy is constructed by integrating the material recognition model based on the Back Propagation (BP) neural network algorithm. This control strategy can output the set pilot pressure according to the material type, realize the intelligent adjustment of the lifting range of the boom with the change of material type, and reduce the working resistance during the shoveling stage. The peak engine power consumed while shoveling sand, gravel, and boulders decreased by 20.6 %, 19.1 %, and 10.9 %, respectively, improving the energy utilization rate of the loader shoveling system when facing different working objects.