Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador

Abstract

In Ecuador, according to data from the Ministry of Energy, the internal combustion engine is the largest consumer of fossil fuels. For this reason, it is important to identify and develop proposals in the literature that enable the prediction of vehicle fuel consumption in both the laboratory and on the road. To accomplish this, real driving emissions (RDEs) need to be contrasted against the development of an algorithm that characterizes forces that oppose such proposals. From experimental tests, fuel consumption information was collected through a flow meter connected to the fuel line and the engine’s characteristic curves were obtained through a chassis dynamometer. Then, from the parameter identification data (PID), the most important predictors were established through an ANOVA analysis. For the acquired variables, a neural network was implemented that could predict 99% of the estimates and present a relative error lower than 5% compared to common methods. Additionally, an algorithm was developed to calculate fuel consumption as a function of the gear, inertial forces, rolling resistance, slope, and aerodynamic force.

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 1: Methodology and Similarity Conditions Studies

“…The aspect of energy management in vehicles was also the research subject of Hegde et al [20], which reduced fuel consumption (by approximately 1%) in the WLTC test exclusively through vehicle energy management. Molina Campoverde [21] used artificial neural networks to predict fuel consumption in the RDE test using WLTC test data. Using the created algorithm, the author obtained a vehicle fuel consumption error of 1.4% compared to the actual value obtained in real tests.…”