Abstract
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.
Highlights
International agreements such as the Paris Agreement call for significant reductions in carbon emissions to mitigate global warming [1,2,3]
The data collection process of the platform is provided as follows: (1) Collecting vehicle OBD information and engine emission data based on multiple sensors, such as fuel tank level, diesel particulate filter (DPF) pressure difference, selective catalytic reduction (SCR) inlet/outlet temperature, etc.; (2) Collecting the data to the onboard T-BOX; (3) All data of T-BOX will be transferred to the storage server of the platform via wireless network (4G or 5G), which obeys the transmission protocol named by the limits and measurement methods for emissions from heavy-duty diesel vehicles (HDDVs) (China VI)
To ensure the accuracy of the recognition of gas stations, based on the DBSCAN clustering algorithm to realize the identification of gas station location, thethe real-time information of onboard sensors, considering the two dimensions of vehicle fuel gas amount and other information are accumulated and compared with the location data consumption and nitrogen oxide emissions, a quantitative evaluation of the oil quality filed by legal gas of stations screen out suspicious gasfuel stations
Summary
International agreements such as the Paris Agreement call for significant reductions in carbon emissions to mitigate global warming [1,2,3]. The current method for detecting fuel/urea refueling behavior mainly includes setting up vehicle identification and verification devices at gas stations and using wireless communication to transmit vehicle fuel/urea refueling information to a remote server. The high cost, including the timely update rate and reading and writing storage data structure, has caused it to be unable to provide useful information to detect unregistered gas/urea stations and their related fuel or urea refueling behaviors. Morteza et al [12] developed an ARIMAbased anomaly detection framework to identify abnormal states of the vehicles based on the multiple-channel operating time-series data. This study fully utilizes the vehicle trajectory data and provides the possibility of data-driven gas station location identification, fuel/urea refill behavior, and oil quality analysis. Energies 2021, 14, 8011 recognition method for the spatiotemporal characteristics of mobile vehicle fuel/urea refueling behavior in mobile source big data scenarios.
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