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Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

文献类型：期刊论文

作者Ding, Yan4; Ji, Zhe2,3,4; Liu, Peng1; Wu, Zhiqiang1; Li, Gang4; Cui, Dingsong1; Wu, Yizhong1; Xu, Sha5
刊名ENERGIES
出版日期2021-12-01
卷号14
关键词gas stations recognition oil quality evaluation heavy-duty vehicles DBSCAN clustering CART algorithm real-world data
DOI10.3390/en14238011
通讯作者Ji, Zhe(jizhe@vecc.org.cn) ; Cui, Dingsong(dingsong_cui1994@163.com)
英文摘要With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.
WOS关键词EMISSION CHARACTERISTICS ; ANOMALY DETECTION
资助项目State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation[VECS2021S03] ; Research and Demonstration of RPTR(remote sensing-period testing insection-telematics-roadside inspection) Integrated Mobile Source Monitoring System in Tangshan[19150259E] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23010202]
WOS研究方向Energy & Fuels
语种英语
WOS记录号WOS:000735637200001
出版者MDPI
资助机构State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation ; Research and Demonstration of RPTR(remote sensing-period testing insection-telematics-roadside inspection) Integrated Mobile Source Monitoring System in Tangshan ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/127173]  
专题中国科学院合肥物质科学研究院
通讯作者Ji, Zhe; Cui, Dingsong
作者单位1.Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Beijing 100081, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China
3.Univ Sci & Technol China, Hefei 230026, Peoples R China
4.Chinese Res Inst Environm Sci, State Environm Protect Key Lab Vehicle Emiss Cont, Beijing 100012, Peoples R China
5.Beijing Bitnei Corp Ltd, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Ding, Yan,Ji, Zhe,Liu, Peng,et al. Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles[J]. ENERGIES,2021,14.
APA Ding, Yan.,Ji, Zhe.,Liu, Peng.,Wu, Zhiqiang.,Li, Gang.,...&Xu, Sha.(2021).Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles.ENERGIES,14.
MLA Ding, Yan,et al."Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles".ENERGIES 14(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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