Vehicle Refueling Behavior Model Based on Spatio-Temporal Big Data Monitoring Platform

Abstract

During the actual use of vehicles, a large number of irregularities such as refueling at illegal gas station or mobile gas refueling vehicles, caused the unqualified diesel to be refilled into the vehicle, affecting the normal operation of the engine aftertreatment system, the exhaust emission result not meeting the national emission standards, and causing vehicle speed limited, penalized by Ministry of Ecological Environment. Identifying the quality of the gas is a prerequisite to ensure the normal operation of the vehicle’s aftertreatment system. This paper introduces an spatio-temporal big data calculation platform based on improved data indexing algorithm, proposes two models, that is, analysis of gas position and gas station type matching, applies three analysis rules, that is, noise data removal, data frequency reduction, and category determination, performs three categories of gas stations to complete the identification and analysis of vehicle refueling behavior in real-time monitoring data. The application in the real Internet of Vehicles (IoV) monitoring platform shows that the results obtained are of great significance for determining the cause of the engine aftertreatment system problem, defining the responsibilities, and guiding the technical upgrade of the aftertreatment system.