A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information

doi:10.1109/TIV.2022.3210283

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information
	Sun, Yao 2; Hu, Yunfeng 2,3; Zhang, Hui 4,5; Wang, Feiyue 6,7; Chen, Hong 1,2
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-03-01
卷号	8 期号:3 页码:2077-2087
关键词	Combined CO2 estimation model deterioration factor OBD-independent parallel supervision system vehicle CO2 emissions
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3210283
通讯作者	Hu, Yunfeng(huyf@jlu.edu.cn) ; Zhang, Hui(huizhang285@gmail.com)
英文摘要	A parallel supervision system is built in this paper in order to accurately estimate vehicleCO(2) emissions. Only on-board diagnostics (OBD)-independent information is used, making the model capable of making predictions based on future road gradients and planned speed trajectories. Based on the parallel theory, the actual traffic environment is considered the physical world, while the combined CO2 model (which consists of physical and data-driven models) is the core part of the artificial world. The physical model uses a cascaded structure with engine speeds and torques as intermediate variables, and the data-drivenmodel relies on a modified long short-term memory (LSTM) neural network. When the historical data is sufficient in size and diversity, the data-driven model is appropriate and achieves more accurate estimations; otherwise, the physical model is preferable because of its greater robustness. Based on this combined model, the supervision system can leverage both the learning ability and physics-based knowledge. Two real-world experimental case studies have been performed to validate this system. According to the research analysis, both the physical and data-driven models achieve sufficient accuracy. The physical model indicatesmore robustness even when some primary parameters (gear ratios) are unknown, which can be used as a supplement to the data-driven model. Moreover, the deterioration factor (DF) of vehicleCO(2) emissions is considered to simulate aged vehicles. This parallel supervision system can effectively address the gap between regulatory test cycles and real-world carbon emissions.
资助项目	National Natural Science Foundation of China[62103160] ; National Natural Science Foundation of China[U1864201]
WOS关键词	MODEL
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000981348100008
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53323]
专题	多模态人工智能系统全国重点实验室
通讯作者	Hu, Yunfeng; Zhang, Hui
作者单位	1.Tongji Univ, Coll Elect & Informat Engn, Shanghai 200092, Peoples R China 2.Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130025, Peoples R China 3.Jilin Univ, Coll Commun Engn, Changchun 130025, Peoples R China 4.Beihang Univ, Sch Transportat Sci & Engn, Beijing 100091, Peoples R China 5.Beihang Univ, Ningbo Inst Technol NIT, Ningbo 315323, Peoples R China 6.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Sun, Yao,Hu, Yunfeng,Zhang, Hui,et al. A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(3):2077-2087.
APA	Sun, Yao,Hu, Yunfeng,Zhang, Hui,Wang, Feiyue,&Chen, Hong.(2023).A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(3),2077-2087.
MLA	Sun, Yao,et al."A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.3(2023):2077-2087.