Fuel Consumption Using OBD-II and Support Vector Machine Model. (25th January 2020)

• Record Type: Journal Article
• Authors: Abukhalil, Tamer; AlMahafzah, Harbi; Alksasbeh, Malek; Alqaralleh, Bassam A. Y.
• Other Names: Pennock Gordon R. Academic Editor.
• Is Part Of: Journal of robotics. Volume 2020(2020)
• Publication Date: 2020-01-25
• Subjects: Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1155/2020/9450178
• Languages: English
• ISSNs: 1687-9600

Abstract

Abstract : This paper presents a method to estimate gasoline fuel consumption using the onboard vehicle information system OBD-II (Onboard Diagnoses-II). Multiple vehicles were used on a test route so that their consumption can be compared. The relationships between fuel consumption and both of the engine speed are measured in RPM (revolutions per minute), and the throttle position sensor (TPS). The relationships are expressed as polynomial equations. The method which is composed of an SVM (support vector machine) classifier combined with Lagrange interpolation, is used to define the relationship between the two engine parameters and the overall fuel consumption. The relationship model is plotted using a surface fitting tool. In the experimental section, the proposed method is tested using the vehicles on a major highway between two cities in Jordan. The proposed model gets its sample data from the engine's RPM, TPS, and fuel consumption. The method successfully has given precise fuel consumption with square root mean difference of 2.43, and the figures are compared with the values calculated by the conventional method.