Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction
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 trajector...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7659 |
| _version_ | 1797592105963487232 |
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| author | Yanhui Chen Gang Shi Cheng Tan Zhiwen Wang |
| author_facet | Yanhui Chen Gang Shi Cheng Tan Zhiwen Wang |
| author_sort | Yanhui Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:46:44Z |
| format | Article |
| id | doaj.art-0d2ecd2d4ffa44b4a0a76fccf16ddc38 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:44Z |
| publishDate | 2023-06-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0d2ecd2d4ffa44b4a0a76fccf16ddc382023-11-18T16:09:36ZengMDPI AGApplied Sciences2076-34172023-06-011313765910.3390/app13137659Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption ReductionYanhui Chen0Gang Shi1Cheng Tan2Zhiwen Wang3Department of Mechanical and Electrical Engineering, Guangxi Vocational College of Water Resources and Electric Power, Nanning 530023, ChinaSchool of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaDepartment of Mechanical and Electrical Engineering, Guangxi Vocational College of Water Resources and Electric Power, Nanning 530023, ChinaSchool of Computer Science and Telecommunication Engineering, Guangxi University of Science and Technology, Liuzhou 545006, ChinaThe 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.https://www.mdpi.com/2076-3417/13/13/7659trajectory optimizationmachine learningwheel loaderfuel consumption |
| spellingShingle | Yanhui Chen Gang Shi Cheng Tan Zhiwen Wang Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction Applied Sciences trajectory optimization machine learning wheel loader fuel consumption |
| title | Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction |
| title_full | Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction |
| title_fullStr | Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction |
| title_full_unstemmed | Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction |
| title_short | Machine Learning-Based Shoveling Trajectory Optimization of Wheel Loader for Fuel Consumption Reduction |
| title_sort | machine learning based shoveling trajectory optimization of wheel loader for fuel consumption reduction |
| topic | trajectory optimization machine learning wheel loader fuel consumption |
| url | https://www.mdpi.com/2076-3417/13/13/7659 |
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