Wenli Wang
A new vehicle specific power method based on internally observable variables: Application to CO2 emission assessment for a hybrid electric vehicle
Wang, Wenli; Bie, Jing; Yusuf, Abubakar; Liu, Yiqiang; Wang, Xiaofei; Wang, Chengjun; Zheng Chen, George; Li, Jianrong; Ji, Dongsheng; Xiao, Hang; Sun, Yong; He, Jun
Authors
Jing Bie
Abubakar Yusuf
Yiqiang Liu
Xiaofei Wang
Chengjun Wang
Professor of Electrochemical Technologies GEORGE CHEN GEORGE.CHEN@NOTTINGHAM.AC.UK
Professor of Electrochemical Technologies
Jianrong Li
Dongsheng Ji
Hang Xiao
Yong Sun
Jun He
Abstract
As an important vehicle activity recognition method, vehicle specific power (VSP) has been widely used for on-road traffic emission modelling since its introduction in 1999. The conventional VSP (VSP_veh) is calculated from externally observable variables (EOVs) on the vehicle level and represents the power that a running vehicle needs to overcome. However, for hybrid electric vehicles (HEVs) with two power sources, vehicle activity is not always directly related to engine emissions. This study introduces the engine level VSP (VSP_eng), which estimates engine power from internally observable variables (IOVs) obtained from the vehicle's on-board electronic control unit (ECU). An engine bench test is first implemented to validate the estimation algorithm for VSP_eng. A real-world driving emission (RDE) test is then conducted with a HEV in Ningbo city of China to evaluate the performance of VSP_veh and VSP_eng in emission estimation. The results show a strong correlation between emission and VSP_eng (R2 = 0.9783), while a much weaker correlation was found between emission and VSP_veh (R2 = 0.4216). Further analysis indicates that this strong correlation between emission and VSP_eng applies to all driving conditions (urban, rural and highway). The differences between VSP_veh and VSP_eng are then highlighted by a combined correlation analysis where the four work modes of HEV can be graphically identified. Lastly, this study discusses the feasibility and potential benefits of the intelligent and remote vehicle emissions monitoring through the upcoming vehicle to everything (V2X) network.
Citation
Wang, W., Bie, J., Yusuf, A., Liu, Y., Wang, X., Wang, C., …He, J. (2023). A new vehicle specific power method based on internally observable variables: Application to CO2 emission assessment for a hybrid electric vehicle. Energy Conversion and Management, 286, Article 117050. https://doi.org/10.1016/j.enconman.2023.117050
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 12, 2023 |
Online Publication Date | Apr 21, 2023 |
Publication Date | Jun 15, 2023 |
Deposit Date | Apr 24, 2023 |
Publicly Available Date | Apr 24, 2023 |
Journal | Energy Conversion and Management |
Print ISSN | 0196-8904 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 286 |
Article Number | 117050 |
DOI | https://doi.org/10.1016/j.enconman.2023.117050 |
Keywords | Vehicle specific power; Hybrid electric vehicle; CO2 emission; Real-world driving emission; Hybrid working mode |
Public URL | https://nottingham-repository.worktribe.com/output/19993987 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S0196890423003965?via%3Dihub |
Files
new vehicle specific power method
(5.6 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Electro-deposition and re-oxidation of carbon in carbonate containing molten salts
(2014)
Journal Article
Achieving low voltage half electrolysis with a supercapacitor electrode
(2013)
Journal Article
Selecting the power electronic interface for a supercapattery based energy storage system
(-0001)
Presentation / Conference Contribution
Redox electrolytes in supercapacitors
(2015)
Journal Article
Cell voltage versus electrode potential range in aqueous supercapacitors
(2015)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search