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