Affiliation:
1. Grupo de Investigación en Ingeniería Del Transporte (GIIT), Universidad Politécnica Salesiana, Quito 170146, 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.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference36 articles.
1. Rivera Campoverde, N., Muñoz Sanz, J., and Arenas Ramírez, B. (2022, January 22–24). Modelo de bajo costo para la estimación de emisiones contaminantes basado en GPS y aprendizaje automático. Proceedings of the XV Ibero-American Congress of Mechanical Engineering (CIBIM 2022), Madrid, Spain.
2. The evaluation of exhaust emission in RDE tests including dynamic driving conditions;Kurtyka;Transp. Res. Procedia,2019
3. A modelling tool for engine and exhaust aftertreatment performance analysis in altitude operation;Serrano;Results Eng.,2019
4. A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing;Zhou;Transp. Res. Part D Transp. Environ.,2016
5. Eco-driving technology for sustainable road transport: A review;Huang;Renew. Sustain. Energy Rev.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献