通过利用联网车队中传感器布局的多样性来预测传感器点火期间的氮氧化物排放

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.07.095

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

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引用次数: 0

摘要

本文反映了一个概念，即利用联网车辆的不同传感器配置来增强其排放监测和诊断。在这个愿景中，同一家族的车辆配备了不同的传感器布局和等级，并共享数据以支持对整个脚的监控。概述了该框架内的多种应用，并讨论了一个特定的用例，包括使用人工神经网络预测排气管NOx传感器点火期间的排放量，展示了所提出架构的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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