In the automatic shoveling operation of wheel loaders, the shovel trajectory has a significant influence on the operation’s performance. In order to obtain a suitable shovel trajectory and optimize the automatic shovel performance of the loader, we developed a test platform for the operational performance of loaders. Nine parallel shoveling trajectories of different depths were designed according to the coordination shoveling method. The formula for calculating the operational performance is established. The automatic shoveling test is performed according to the designed trajectory to obtain the real-time shoveling parameters, which are then combined with the calculation formula to calculate the operating parameters of the loader. Finally, the actual range of operational performance parameters is calculated by the normal distribution. The test results show that the trajectory with a shovel depth of 400 mm is the optimal trajectory. It was also verified by comparing manually controlled shoveling with it. With only a 1% difference in the full bucket rate, the operation time of automatic shoveling was 15.3% less than manually controlled shoveling, fuel consumption was 4.7% less, the energy consumption of practical work performed was 10.7% more, and maximum operation resistance was 20.5% lower. Therefore, the operational performance of the loader following this trajectory for shoveling meets the actual requirements.
Insertion resistance is the resistance caused by a pile to a wheel loader when the latter inserts into the pile. It is significant to clarify the insertion resistance to avoid wheel slippage, increase additional energy consumption, and protect the wheel loader during the insertion process. To address the problem that current methods cannot accurately obtain the insertion resistance magnitude and insertion resistance variation trend, we propose a composite model based on the particle swarm optimization (PSO) algorithm and the long short-term memory (LSTM) neural network. Firstly, the Pearson correlation coefficient method is used to test the parameters related to insertion resistance. Following this, the hyperparameters in the LSTM are optimized by PSO. Finally, different proportions of training sets are set in PSO-LSTM and compared with LSTM. The experimental data are selected from gravel sample groups and sand sample groups consisting of insertion depths of 600 mm, 800 mm, and 1000 mm. The results show that PSO-LSTM has higher prediction accuracy, better robustness, stability, and generalization ability compared with LSTM. In PSO-LSTM, when the proportion of the training set is 80%, the average relative errors are 2.28%, 1.57%, and 1.53% for the gravel sample group and 1.14%, 0.71%, and 0.60% for the sand sample group.
The difference in fuel consumption of wheel loaders can be more than 30% according to different shoveling trajectories for shoveling operations, and the optimization of shoveling trajectories is an important way to reduce the fuel consumption of shoveling operations. The existing shoveling trajectory optimization method is mainly through theoretical calculation and simulation analysis, which cannot fully consider the high randomness and complexity of the shoveling process. It is difficult to achieve the desired optimization effect. Therefore, this paper takes the actual shoveling operation data as the basis. The factors that have a high impact on the fuel consumption of shoveling are screened out through Kernel Principal Component Analysis. Moreover, the mathematical model of fuel consumption of shoveling operation is established by Support Vector Machine and combined with the Improved Particle Swarm Optimization algorithm to optimize the shoveling trajectory. To demonstrate the generalization ability of the model, two materials, gravel, and sand, are selected. Meanwhile, the influence of different engine speeds on the shoveling operation is considered. We optimize the shoveling trajectories for three different engine speeds. The optimized trajectories are verified and compared with the sample data and manually controlled shoveling data. The results show that the optimized trajectory can reduce the fuel consumption of shoveling operation by 27.66% and 24.34% compared with the manually controlled shoveling of gravel and sand, respectively. This study provides guidance for the energy-efficient operation of wheel loaders.
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