Work

Predicting NOx emissions during sensor light-off by leveraging sensor layout diversity in connected fleets

Year: 2025

Type: article

Abstract: This paper reflects on a concept that leverages diverse sensor configurations across a fleet of connected vehicles to enhance their emissions monitoring and diagnostics. In this vision, the vehicles of a same family are equipped with different sensor layouts and grades, and share data to support the monitoring of the entire feet. Multiple applications within this framework are outlined, and a specific use case consisting in predicting the emissions during the light-off of the tailpipe NOx sensor with artificial neural networks is discussed, demonstrating the benefits of the proposed architecture.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.095

Authors: Alvin Barbier, José Miguel Salavert, Carlos E. Palau, Carlos Guardiola

Institution: Universitat Politècnica de València

Language: English

Topic: Vehicle emissions and performance

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: European Commission