Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD

doi:10.1016/j.measurement.2021.110074

	Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD
	Xie, Hao 1,2; Zhang, Yujun 1; He, Ying 1; You, Kun 1; Fan, Boqiang 1,2; Yu, Dongqi 1,2; Lei, Boen 1,2; Zhang, Wangchun 1,2
刊名	MEASUREMENT
	2021-11-01
卷号	185
关键词	PEMS Time series prediction Deep learning Early warning of high emissions Outliers elimination
ISSN号	0263-2241
DOI	10.1016/j.measurement.2021.110074
通讯作者	Zhang, Yujun(yjzhang@aiofm.ac.cn)
英文摘要	Portable emission measurement system (PEMS) testing, which is the most accurate measurement method for vehicle emissions, has been included into the regulations of vehicle emission standards in various countries. However, PEMS is expensive, and in the long-term measurement process, the monitoring data will exhibit outliers, which is a drift phenomenon. In addition, as a measurement method, it cannot prevent the occurrence of high vehicle emissions. To solve the above problem, this study proposes a parallel attention-based long shortterm memory (PA-LSTM) for building an emission prediction model using PEMS and on board diagnostics (OBD). According to the characteristics of the real vehicle road test and bench test data, the PA-LSTM model adopts a parallel spatial attention coding mechanism, combined with a temporal attention decoding mechanism. Qualitative and quantitative experimental results show that the PA-LSTM model can achieve a more accurate prediction of vehicle emissions compared with other popular models, and the proposed model can eliminate outliers and restrain the offset of the zero levels in the PEMS data. The most significant thing is that the PA-LSTM model can foresee about the possible high vehicle emissions in the future and provide timely feed-back to the emission control system of the vehicle engine, so as to make corresponding control measurements in time and avoid the occurrence of high emissions.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23010204] ; Strategic Priority Research Program of the Chinese Academy of Sciences[62,033,012] ; National Natural Science Foundation of China ; Instrument and Equipment Function Development Technology Innovation of the Chinese Academy of Sciences[Y83H3y1251] ; Major Subject of Science and Technology of Anhui Province[202003a07020005]
WOS关键词	PASSENGER CARS ; NOX EMISSIONS ; FUEL USE ; DIESEL ; ARIMA
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000709473100007
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Instrument and Equipment Function Development Technology Innovation of the Chinese Academy of Sciences ; Major Subject of Science and Technology of Anhui Province
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/125810]
专题	中国科学院合肥物质科学研究院
通讯作者	Zhang, Yujun
作者单位	1.Chinese Acad Sci, Key Lab Environm Opt & Technol, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Xie, Hao,Zhang, Yujun,He, Ying,et al. Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD[J]. MEASUREMENT,2021,185.
APA	Xie, Hao.,Zhang, Yujun.,He, Ying.,You, Kun.,Fan, Boqiang.,...&Zhang, Wangchun.(2021).Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD.MEASUREMENT,185.
MLA	Xie, Hao,et al."Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD".MEASUREMENT 185(2021).