Barbier, Alvin (Universitat Politècnica de València), Salavert, José Miguel (Universitat Politècnica de València), Palau, Carlos Enrique (Universitat Politècnica de València), Guardiola, Carlos (Universitat Politecnica de Valencia)

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

Scheduled for presentation during the Regular Session "Vehicle autonomy and connectivity" (TuA2), Tuesday, June 17, 2025, 10:50−11:10, Jos

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

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 fleet. 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.