Issue |
E3S Web of Conf.
Volume 536, 2024
2024 6th International Conference on Environmental Prevention and Pollution Control Technologies (EPPCT 2024)
|
|
---|---|---|
Article Number | 01009 | |
Number of page(s) | 5 | |
Section | Environmental Planning Management and Ecological Construction | |
DOI | https://doi.org/10.1051/e3sconf/202453601009 | |
Published online | 10 June 2024 |
Vehicle greenhouse gas emission factors based on multi-dimensional Bin interval mode
1 Laboratory of transport pollution control and monitoring technology, Transport planning and research institute, Ministry of transport department, Beijing, 100028, China
2 China Automotive Technology & Research Center Co., Ltd., Tianjin, 300300, China
3 CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin, 300300, China
4 School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China
* Corresponding author’s e-mail: zhanghao2023@catarc.ac.cn
The emission factors of greenhouse gases from motor vehicles are of significant importance for low-carbon and environmentally friendly initiatives. This paper focuses on greenhouse gas emission factors of heavy-duty vehicles. The multi-dimensional Bin interval mode are proposed, which considers multiple parameters such as vehicle speed, acceleration, torque, and rotational speed. Based on this, the emission characteristics of greenhouse gases from heavy-duty vehicles. are investigated under different parameter conditions. The results indicate that as the VSPA value or the engine torque increases, the CO2 emission factor also increases accordingly. The CO2 emission factor is lower at medium to high rotational speeds, while it tends to increase at low rotational speeds. The research findings provide support for monitoring and evaluating greenhouse gas emissions and offer scientific guidance for formulating specific carbon reduction policies, emission targets, and plans.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.