The impact of driving conditions on light-duty vehicle emissions in real-world driving
To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix.
First published online 19 March 2020
This work is licensed under a Creative Commons Attribution 4.0 International License.
Boulter, P. G.; McCrae, I. S.; Barlow, T. J. 2007. A Review of Instantaneous Emission Models for Road Vehicles. Published Project Report 267. Transport Research Laboratory, Wokingham, UK. 64 p. Available from Internet: https://trl.co.uk/reports/PPR267
Cheng, Z.; Xue, Z.; Zhang, Z.; Xu, Y.; Li, C.; Yi, P. 2009. Study on the establishment of vehicle emission inventories, Environmental Pollution and Control (9): 76–81. (in Chinese).
Davis, N.; Lents, J.; Osses, M.; Nikkila, N.; Barth, M. 2005. Development and application of an international vehicle emissions model, Transportation Research Record: Journal of the Transportation Research Board 1939: 156–165. https://doi.org/10.1177/0361198105193900118
Demir, E.; Bektaş, T.; Laporte, G. 2011. A comparative analysis of several vehicle emission models for road freight transportation, Transportation Research Part D: Transport and Environment 16(5): 347–357. https://doi.org/10.1016/j.trd.2011.01.011
EPA. 2007. Motor Vehicle Emission Simulator Highway Vehicle Implementation (MOVES-HVI) Demonstration Version: Software Design and Reference Manual: Draft. 2007. US Environmental Protection Agency (EPA). 232 p.
Iqbal, A.; Allan, A.; Zito, R. 2016. Meso-scale on-road vehicle emission inventory approach: a study on Dhaka City of Bangladesh supporting the ‘cause-effect’ analysis of the transport system, Environmental Monitoring and Assessment 188(3): 149. https://doi.org/10.1007/s10661-016-5151-4
Gao, Y; Yu, L.; Song, G.; Zuo, Y.; Hao, Y. 2012. Quantitative modeling and simulation of traffic emissions, Journal of System Simulation (4): 887–891. (in Chinese).
Guo, D.; Gao, S.; Zou, G.-D.; Tan, D.-R.; Wang, X.-Y.; Shao, J.-J. 2012. Quantitative evaluation method of vehicle emission in urban region, Journal of Traffic and Transportation Engineering 12(1): 72–78. (in Chinese).
Guo, D.; Sun, F.; Zhao, J. 2017. Method for Estimation of Urban Area Vehicle Emission and Reduction Strategy Analysis. China Communications Press. 186 p. (in Chinese).
Guo, D.; Zhang, H.; Zheng, C.; Gao, S.; Wang, D. 2016. Analysis of the future development of Chinese auto energy saving and environmental benefits, Systems Engineering – Theory & Practice (6): 1593–1599. (in Chinese).
Kouridis, C.; Ntziachristos, L.; Samaras, Z. 2000. COPERT III: Computer Programme to Calculate Emissions from Road Transport: User Manual (Version 2.1). European Environment Agency (EEA), Copenhagen, Denmark. 46 p. Available from Internet: https://www.eea.europa.eu/publications/Technical_report_No_50
Li, J.; Zhang, J.-H. 2014. Vehicle routing problem with time windows based on carbon emissions and speed optimization, Systems Engineering – Theory & Practice (12): 3063–3072. (in Chinese).
Li, M.; Xu, J.; Dai, C. 2009. The features of emissions control technology of light-duty gasoline vehicles in different regulation stages, Automotive Engineering (8): 741–745. (in Chinese).
Li, X.-X.; Sun, G.-J.; Tian, W.-L.; Zhang, Q.-Y. 2012. Study on abatement policy of NOx emission from vehicles in Hangzhou during 12th five-year plan, China Environmental Science (8): 1416–1421. (in Chinese).
Liu, J.; Dong, J.-J.; Shi, X.-P.; Wang, H.-Z.; Yang, H.-M. 2011. Research for NOx emission of Nanjing vehicle based on IVE Model, Applied Mechanics and Materials 99–100: 1341–1345. https://doi.org/10.4028/www.scientific.net/AMM.99-100.1341
Qiu, F.; Zhang, C.; Huang, D.; Du, W.; Wang, M.; Hu, Z. 2015. Characteristics of HC emission from dual-fuel engine under transient operating conditions, Chinese Journal of Environmental Engineering (1): 312–316. (in Chinese).
Tang, W.; Yang, Q.; Huang, C.; Lu, B.; Xia, Y.; Jing, B.; Lu, Q.; Lu, J. 2018. Study on characteristics of pollutant emission from motor vehicles in Hangzhou based on large data analysis and IVE model, Acta Scientiae Circumstantiae (1): 71–78. (in Chinese).
Zheng, F.; Li, J.; Van Zuylen, H.; Lu, C. 2017. Influence of driver characteristics on emissions and fuel consumption, Transportation Research Procedia 27: 624–631. https://doi.org/10.1016/j.trpro.2017.12.142
Zi, K.; Huang, Y.; Tu, X.; Yang, R. 2006. An investigation into the total amount of pollutants emission from motor vehicle in city, Automotive Engineering (8): 707–710. (in Chinese).