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

文献类型：期刊论文

作者Sun, Yao4; Hu, Yunfeng4,5; Zhang, Hui2,3; Wang, Feiyue1,6; Chen, Hong4,7
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期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
DOI10.1109/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.
WOS关键词MODEL
资助项目National Natural Science Foundation of China[62103160] ; National Natural Science Foundation of China[U1864201]
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:000981348100008
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
2.Beihang Univ, Sch Transportat Sci & Engn, Beijing 100091, Peoples R China
3.Beihang Univ, Ningbo Inst Technol NIT, Ningbo 315323, Peoples R China
4.Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130025, Peoples R China
5.Jilin Univ, Coll Commun Engn, Changchun 130025, Peoples R China
6.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
7.Tongji Univ, Coll Elect & Informat Engn, Shanghai 200092, 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.

入库方式： OAI收割

来源：自动化研究所

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