Devi, A.S., Britto, M.M.J., Fang, Z. et al. (5 more authors) (2024) Internet-of-Vehicles Network for CO2 Emission Estimation and Reinforcement Learning-Based Emission Reduction. IEEE Access, 12. pp. 110681-110690. ISSN 2169-3536
Abstract
The escalating impact of vehicular Carbon Dioxide (CO 2 ) emissions on air pollution, global warming, and climate change necessitates innovative solutions. This paper proposes a comprehensive Internet-of-Vehicles (IoV) network for real-time CO 2 emissions estimation and reduction. We implemented and tested an on-board device that estimates the vehicle’s emissions and transmits the data to the network. The estimated CO 2 emissions values are close to the standard emissions values of petrol and diesel vehicles, accounting for expected discrepancies due to vehicles’ age and loading. The network uses the aggregate emissions readings to inform the Reinforcement Learning (RL) algorithm, enabling the prediction of optimal speed limits to minimize vehicular emissions. The results demonstrate that employing the RL algorithm can achieve an average CO 2 emissions reduction of 11 kg/h to 150 kg/h.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC-ND 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Emission estimation, CO2 emissions, Internet-of-Vehicles, Emission reduction, reinforcement learning, traffic management |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Aug 2024 10:50 |
Last Modified: | 04 Dec 2024 15:51 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/access.2024.3441949 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216067 |
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Licence: CC-BY-NC-ND 4.0