Estimation of pollutant emissions in real driving conditions based on data from OBD and machine learning

Néstor Diego Rivera-Campoverde, José Luis Muñoz-Sanz, Blanca Del Valle Arenas-Ramirez

Research output: Contribution to journalArticlepeer-review

Abstract

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

Original languageEnglish
Article number6344
JournalSensors
Volume21
Issue number19
DOIs
StatePublished - 1 Oct 2021

Bibliographical note

Funding Information:
Machine-Engineering Division, Mechanic Engineering Department, Universidad Polit?cnica de Madrid.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Emission model
  • Internal combustion engine
  • Machine learning
  • OBD emissions model
  • Real driving emissions

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