Low-Cost Model for the Estimation of Pollutant Emissions Based on GPS and Machine Learning

Abstract

AbstractThis paper presents a novel method for estimating pollutants emitted by vehicles powered by internal combustion engines in real driving, without the need for extensive measurement campaigns or the use of instrumentation in the vehicle for long periods of time; for which it is based on the positioning and speed signals generated by the GPS (Global Positioning System) and the machine learning application. To obtain the training data and validation of the model, two road tests are carried out using the Euro 6 directives for the estimation of pollutants through RDE (Real Driving Emissions), in which a portable emission measurement system is used, and a recorder that stores data from OBD (On Board Diagnostics) and GPS. Based on the data obtained in the first route, the vehicle’s performance is determined and, through automatic learning, the model that estimates polluting emissions is generated, which is validated with the data from the second route. When comparing the results generated by the model against those measured in the RDE, relative errors (%) of 0.0976, −0.2187, 0.2249 and −0.1379 are obtained in the emission factors of CO2, CO, HC and NOx respectively. Finally, the model is fed with data obtained in 1218 km of random driving, obtaining similar results to models based on OBD and closer to the real driving conditions generated by models such as the IVE (International Vehicle Emissions).