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

Research Group in Transportation Engineering (GIIT)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	ECTM 2023 - 2023 IEEE 7th Ecuador Technical Chapters Meeting
Editors	David Rivas Lalaleo, Manuel Ignacio Ayala Chauvin
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350338232
DOIs	https://doi.org/10.1109/ETCM58927.2023.10308992
State	Published - 2023
Event	7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023 - Ambato, Ecuador Duration: 10 Oct 2023 → 13 Oct 2023

Publication series

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

Conference

Conference	7th IEEE Ecuador Technical Chapters Meeting, ECTM 2023
Country/Territory	Ecuador
City	Ambato
Period	10/10/23 → 13/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Neural Networks
OBD
Parametric Model
Vehicle Emissions

CACES Knowledge Areas

617A Design and Construction of Motor Vehicles, Boats and Aircraft

Access to Document

10.1109/ETCM58927.2023.10308992

Cite this

Rivera, N., Jiménez, E., & Cárdenas, J. (2023). Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. In 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",

language = "English",

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

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

editor = "Lalaleo, \{David Rivas\} and Chauvin, \{Manuel Ignacio Ayala\}",

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

address = "United States",

}

Rivera, N, Jiménez, E & Cárdenas, J 2023, Parametric Model for Estimating Pollutant Emissions in M1 Otto Cycle Vehicles with OBD-II. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In Lalaleo DR, Chauvin MIA, editors, 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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Characterization of Sustainable Mobility Models Using Machine Learning Architectures Applied to PID Signals Obtained via OBD II for the Study of Pollutant Emissions in the City of Cuenca

Cite this

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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

CACES Knowledge Areas

Access to Document

Other files and links

Projects

Characterization of Sustainable Mobility Models Using Machine Learning Architectures Applied to PID Signals Obtained via OBD II for the Study of Pollutant Emissions in the City of Cuenca

Cite this