Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification
Energy saving and emission reduction have become the consensus of the global development. Electric construction machinery has drawn more and more attentions due to its zero emission and high efficiency. However, because of the installed capacity of the battery, the complex working conditions and the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9966601/ |
| _version_ | 1828117412714643456 |
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| author | Zhongshen Li Tianliang Lin Qifa Gao |
| author_facet | Zhongshen Li Tianliang Lin Qifa Gao |
| author_sort | Zhongshen Li |
| collection | DOAJ |
| description | Energy saving and emission reduction have become the consensus of the global development. Electric construction machinery has drawn more and more attentions due to its zero emission and high efficiency. However, because of the installed capacity of the battery, the complex working conditions and the time-varying load of construction machinery, the working time of electric construction machinery is hard to estimate. It is important to accurately predict the remaining working time of the whole machine to ensure that the driver can reasonably arrange the operation time. In this paper, the electric loader is studied. To improve the estimation accuracy of the working time of electric loader, the typical working conditions are analyzed. The data of V-type working mode cycles of the actual experimental prototype, which provides the basis for the segmentation of working conditions and the extraction of characteristic parameters are analyzed. The fuzzy C-means clustering algorithm is used, an estimation method of operation energy consumption based on working condition identification is proposed. The results show that the energy consumption estimation method based on the motor average torque proposed in this paper has better estimation accuracy than the traditional estimation method based on the latest unit time energy consumption. |
| first_indexed | 2024-04-11T13:14:51Z |
| format | Article |
| id | doaj.art-7939614af6db475a8d32548f6a2b4950 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T13:14:51Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7939614af6db475a8d32548f6a2b49502022-12-22T04:22:27ZengIEEEIEEE Access2169-35362022-01-011012746112746810.1109/ACCESS.2022.32256819966601Energy Consumption Prediction of Electric Construction Machinery Based on Condition IdentificationZhongshen Li0Tianliang Lin1https://orcid.org/0000-0002-8571-7988Qifa Gao2College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, ChinaCollege of Mechanical Engineering and Automation, Huaqiao University, Xiamen, ChinaCollege of Mechanical Engineering and Automation, Huaqiao University, Xiamen, ChinaEnergy saving and emission reduction have become the consensus of the global development. Electric construction machinery has drawn more and more attentions due to its zero emission and high efficiency. However, because of the installed capacity of the battery, the complex working conditions and the time-varying load of construction machinery, the working time of electric construction machinery is hard to estimate. It is important to accurately predict the remaining working time of the whole machine to ensure that the driver can reasonably arrange the operation time. In this paper, the electric loader is studied. To improve the estimation accuracy of the working time of electric loader, the typical working conditions are analyzed. The data of V-type working mode cycles of the actual experimental prototype, which provides the basis for the segmentation of working conditions and the extraction of characteristic parameters are analyzed. The fuzzy C-means clustering algorithm is used, an estimation method of operation energy consumption based on working condition identification is proposed. The results show that the energy consumption estimation method based on the motor average torque proposed in this paper has better estimation accuracy than the traditional estimation method based on the latest unit time energy consumption.https://ieeexplore.ieee.org/document/9966601/Electric loaderworking timecondition identificationfuzzy C-means clusteringenergy consumption |
| spellingShingle | Zhongshen Li Tianliang Lin Qifa Gao Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification IEEE Access Electric loader working time condition identification fuzzy C-means clustering energy consumption |
| title | Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification |
| title_full | Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification |
| title_fullStr | Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification |
| title_full_unstemmed | Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification |
| title_short | Energy Consumption Prediction of Electric Construction Machinery Based on Condition Identification |
| title_sort | energy consumption prediction of electric construction machinery based on condition identification |
| topic | Electric loader working time condition identification fuzzy C-means clustering energy consumption |
| url | https://ieeexplore.ieee.org/document/9966601/ |
| work_keys_str_mv | AT zhongshenli energyconsumptionpredictionofelectricconstructionmachinerybasedonconditionidentification AT tianlianglin energyconsumptionpredictionofelectricconstructionmachinerybasedonconditionidentification AT qifagao energyconsumptionpredictionofelectricconstructionmachinerybasedonconditionidentification |