Non-invasive, plug-and-play pollution detector for vehicle on-board instantaneous CO2 emission monitoring

Abstract

Autonomous driving has gained momentum in recent years. To help create a sustainable future, an autonomous vehicle should take into account its carbon footprint. This work proposes a new, portable, cost-effective carbon dioxide (CO2) emissions sensor installed in a Light Duty Vehicle (LDV), as a novel enabler towards carbon footprint aware autonomous driving. This work achieves the ability to report real-time metrics about the instantaneous CO2 emissions whilst satisfying non-invasive procedures (weight <1 kg) by means of Unified Diagnostic Services (UDS) compliant interrogators. The proposed solution is validated in compliance with European Union’s official emissions test procedures, and compared with other commercial off-the-shelf solutions. During the test, a 50 km trip divided in 3 driving cycles is performed to obtain the instantaneous CO2 emission, achieving a measuring rate similar to other commercial, non-portable and expensive alternatives.

Publication DOI: https://doi.org/10.1016/j.iot.2023.100755
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
Aston University (General)
Funding Information: This work is funded in part by the EU H2020 project ”5G INDUCE: Open Cooperative 5G Experimentation Platforms for The Industrial Sector NetApps” (Grant Agreement Number 101016941), EU H2020 project “ARCADIAN-IoT: Autonomous Trust, Security and Privacy Man
Additional Information: Copyright © 2023 The Authors. Published by Elsevier B.V. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.
Uncontrolled Keywords: CO2 emissions,PEMS,CAN bus,vehicle pollution
Publication ISSN: 2542-6605
Last Modified: 18 Apr 2025 07:25
Date Deposited: 11 Apr 2025 08:13
Full Text Link:
Related URLs: https://www.sci ... 0781?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2023-07
Published Online Date: 2023-03-13
Accepted Date: 2023-03-12
Authors: Tena-Gago, David
Wang, Qi
Alcaraz-Calero, Jose M. (ORCID Profile 0000-0002-2654-7595)

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