Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II

Néstor Rivera; Edisson Jiménez; Joel Cárdenas

doi:10.1109/ETCM58927.2023.10308992

Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II

Néstor Rivera
, Edisson Jiménez
, Joel Cárdenas

Grupo de Investigación en Ingeniería del Transporte (GIIT)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

This article presents a proposed parametric model for estimating pollutant gas emissions in vehicles with Otto cycle engines. This model is based on the acquisition of on-board diagnostic data and machine learning algorithms. The data are collected through portable devices during Real Driving Emissions (RDE) road tests, and subsequent analysis allows for the training and validation of neural networks to calculate emission factors of various pollutants (CO, CO₂, THC, NOx). In addition, classification learning is considered to assess the behavior of each pollutant in each gear. The model is trained based on three vehicles that followed three different routes, complying with RDE conditions. The obtained emission factors were compared with the IVE model and values close to the latter were found. This model provides crucial information for creating an emissions inventory that reflects the real conditions of the vehicle fleet in the city of Cuenca.

Idioma original	Inglés
Título de la publicación alojada	ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting
Editores	David Rivas Lalaleo, Manuel Ignacio Ayala Chauvin
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9798350338232
DOI	https://doi.org/10.1109/ETCM58927.2023.10308992
Estado	Publicada - 2023
Evento	7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023 - Ambato, Ecuador Duración: 10 oct. 2023 → 13 oct. 2023

Serie de la publicación

Nombre	ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting

Conferencia

Conferencia	7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023
País/Territorio	Ecuador
Ciudad	Ambato
Período	10/10/23 → 13/10/23

Nota bibliográfica

Publisher Copyright:
© 2023 IEEE.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 11: Ciudades y comunidades sostenibles

Areas de Conocimiento del CACES

617A Diseño y construcción de vehículos, barcos y aeronaves motorizadas

Acceder al documento

10.1109/ETCM58927.2023.10308992

Otros archivos y enlaces

Enlace a la publicación en Scopus

Caracterización de modelos de movilidad sostenible mediante la aplicación de arquitecturas de machine learning a las señales PID obtenidas a través de OBD II para el estudio de las emisiones contaminantes sobre la ciudad de Cuenca
Rivera Campoverde, N. D. (Investigador principal), Bermeo Naula, A. K. (Investigador Secundario), Molina Campoverde, P. A. (Investigador Secundario), Vidal Suarez, J. S. (Estudiante Investigador), Jachero Bravo, B. F. (Estudiante Investigador), Semiglia Pineda, W. J. (Estudiante Investigador), Gomez Punin, K. P. (Estudiante Investigador), Idrovo Pulla, D. R. (Estudiante Investigador), Narvaez Calle, J. F. (Estudiante Investigador), Juarez Cardenas, C. A. (Estudiante Investigador), Mendoza Criollo, P. J. (Estudiante Investigador), Avila Ramon, H. P. (Estudiante Investigador), Montenegro Siguenza, J. F. (Estudiante Investigador), Angamarca Silverio, W. N. (Estudiante Investigador), Avila Puzma, J. F. (Estudiante Investigador), Guartazaca Uyaguari, J. S. (Estudiante Investigador), Vasquez Segarra, C. S. (Estudiante Investigador), Suqui Padilla, J. I. (Estudiante Investigador), Alvarez Montenegro, J. S. (Estudiante Investigador), Siavichay Neira, V. S. (Estudiante Investigador), Lucero Duran, W. M. (Estudiante Investigador), Vintimilla Leon, A. S. (Estudiante Investigador), Pacheco Auquilla, D. S. (Estudiante Investigador), Peralta Bueno, L. A. (Estudiante Investigador), Juca Guaman, J. A. (Estudiante Investigador), Ortuño Samaniego, J. I. (Estudiante Investigador), Cardenas Ormaza, J. S. (Estudiante Investigador) & Jimenez Lojano, E. J. (Estudiante Investigador)
18/05/23 → 8/01/25
Proyecto: Investigación y Desarrollo

Citar esto

Rivera, N., Jiménez, E., & Cárdenas, J. (2023). Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. En D. R. Lalaleo, & M. I. A. Chauvin (Eds.), ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting (ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ETCM58927.2023.10308992

Rivera, Néstor ; Jiménez, Edisson ; Cárdenas, Joel. / Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting. editor / David Rivas Lalaleo ; Manuel Ignacio Ayala Chauvin. Institute of Electrical and Electronics Engineers Inc., 2023. (ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting).

@inproceedings{786107e89599413a8c3c127e829f5479,

title = "Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II",

abstract = "This article presents a proposed parametric model for estimating pollutant gas emissions in vehicles with Otto cycle engines. This model is based on the acquisition of on-board diagnostic data and machine learning algorithms. The data are collected through portable devices during Real Driving Emissions (RDE) road tests, and subsequent analysis allows for the training and validation of neural networks to calculate emission factors of various pollutants (CO, CO2, THC, NOx). In addition, classification learning is considered to assess the behavior of each pollutant in each gear. The model is trained based on three vehicles that followed three different routes, complying with RDE conditions. The obtained emission factors were compared with the IVE model and values close to the latter were found. This model provides crucial information for creating an emissions inventory that reflects the real conditions of the vehicle fleet in the city of Cuenca.",

keywords = "Neural Networks, OBD, Parametric Model, Vehicle Emissions",

author = "N{\'e}stor Rivera and Edisson Jim{\'e}nez and Joel C{\'a}rdenas",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023 ; Conference date: 10-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/ETCM58927.2023.10308992",

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series = "ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Rivera, N, Jiménez, E & Cárdenas, J 2023, Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. En DR Lalaleo & MIA Chauvin (eds.), ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting. ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting, Institute of Electrical and Electronics Engineers Inc., 7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023, Ambato, Ecuador, 10/10/23. https://doi.org/10.1109/ETCM58927.2023.10308992

Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. / Rivera, Néstor; Jiménez, Edisson; Cárdenas, Joel.
ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting. ed. / David Rivas Lalaleo; Manuel Ignacio Ayala Chauvin. Institute of Electrical and Electronics Engineers Inc., 2023. (ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

TY - GEN

T1 - Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II

AU - Rivera, Néstor

AU - Jiménez, Edisson

AU - Cárdenas, Joel

PY - 2023

Y1 - 2023

N2 - This article presents a proposed parametric model for estimating pollutant gas emissions in vehicles with Otto cycle engines. This model is based on the acquisition of on-board diagnostic data and machine learning algorithms. The data are collected through portable devices during Real Driving Emissions (RDE) road tests, and subsequent analysis allows for the training and validation of neural networks to calculate emission factors of various pollutants (CO, CO2, THC, NOx). In addition, classification learning is considered to assess the behavior of each pollutant in each gear. The model is trained based on three vehicles that followed three different routes, complying with RDE conditions. The obtained emission factors were compared with the IVE model and values close to the latter were found. This model provides crucial information for creating an emissions inventory that reflects the real conditions of the vehicle fleet in the city of Cuenca.

AB - This article presents a proposed parametric model for estimating pollutant gas emissions in vehicles with Otto cycle engines. This model is based on the acquisition of on-board diagnostic data and machine learning algorithms. The data are collected through portable devices during Real Driving Emissions (RDE) road tests, and subsequent analysis allows for the training and validation of neural networks to calculate emission factors of various pollutants (CO, CO2, THC, NOx). In addition, classification learning is considered to assess the behavior of each pollutant in each gear. The model is trained based on three vehicles that followed three different routes, complying with RDE conditions. The obtained emission factors were compared with the IVE model and values close to the latter were found. This model provides crucial information for creating an emissions inventory that reflects the real conditions of the vehicle fleet in the city of Cuenca.

KW - Neural Networks

KW - OBD

KW - Parametric Model

KW - Vehicle Emissions

UR - https://www.scopus.com/pages/publications/85179511250

U2 - 10.1109/ETCM58927.2023.10308992

DO - 10.1109/ETCM58927.2023.10308992

M3 - Conference contribution

AN - SCOPUS:85179511250

T3 - ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting

BT - ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting

A2 - Lalaleo, David Rivas

A2 - Chauvin, Manuel Ignacio Ayala

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023

Y2 - 10 October 2023 through 13 October 2023

ER -

Rivera N, Jiménez E, Cárdenas J. Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. En Lalaleo DR, Chauvin MIA, editores, ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting. Institute of Electrical and Electronics Engineers Inc. 2023. (ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting). doi: 10.1109/ETCM58927.2023.10308992

Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

ODS de las Naciones Unidas

Areas de Conocimiento del CACES

Acceder al documento

Otros archivos y enlaces

Proyectos

Caracterización de modelos de movilidad sostenible mediante la aplicación de arquitecturas de machine learning a las señales PID obtenidas a través de OBD II para el estudio de las emisiones contaminantes sobre la ciudad de Cuenca

Citar esto