Abstract
This work analyzed the driving style influence on total polluting emissions emitted by an internal combustion vehicle. In this research, the most sold sedan vehicle in Ecuador was used. Parameters used to define the driving style were speed and acceleration; with these information, two styles were classified: normal and aggressive. There are no formal studies in the media about the relationship between pollutant emissions and driving style. Output variables used were vehicle fuel consumption and CO2, CO, HC and NOX pollutant emissions, and as input variables, driving parameters: intake manifold absolute pressure, throttle position, engine speed, speed and the acceleration of the vehicle. It was identified the most important variables such as MAP, TPS, VSS, and RPM with a determination index of 0.97519. Information was acquired by a data logger device, and post-processed using automatic learning techniques was verified a direct relationship between driving style and the polluting emissions, as well as fuel consumption. Therefore, it was verified that a normal driving style can reduce pollutant emissions by up to 22%, for which it is recommended that drivers should avoid sudden acceleration and sudden braking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
C. Sancan, D. Gregorio, Vehículos híbridos, una solución interina para bajar los niveles de contaminación del medio ambiente causados por las emisiones provenientes de los motores de combustión interna. Hybrid vehicles, an interim solution to lower levels of environmental pollution caused by emissions from internal combustion engines, dic. 2017. https://doi.org/10.33890/innova.v2.n12.2017.527
European Commission Amending regulation (EC) No 7152007 of the European Parliament and of the Council and Commission regulation (EC) No 6922008 as regards emissions from light passenger and commercial vehicles (Euro 6) O J Eur Union, vol 142 , pp. 16–24 (2012)
H. Dia, S. Panwai, Impact of driving behaviour on emissions and road network performance», in 2015 IEEE International Conference on Data Science and Data Intensive Systems, dic. 2015, pp. 355–361. https://doi.org/10.1109/DSDIS.2015.68
X. Zheng, Y. Wu, S. Zhang, L. He, J. Hao, Evaluating real-world emissions of light-duty gasoline vehicles with deactivated three-way catalyst converters. Atmos. Pollut. Res. 9(1), 126–132 (2018). https://doi.org/10.1016/j.apr.2017.08.001
Y. Huanga, E.C.Y. Ng, J.L. Zhoua, N.C. Surawskia, E.F.C. Chan, G. Hong, Eco-driving technology for sustainable road transport: a review. Renew. Sustain. Energy Rev. (93), 596–609 (2018)
C.F. Yeh, L.T. Lin, P.J. Wu., C.C.Huang, Using on-board diagnostics data to analyze driving behavior and fuel consumption, in Y. Zhao, T.Y. Wu, T.H. Chang, J.S. Pan., L. Jain (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol. 128. Springer, Cham (2019)
Y. Xu, H. Li, H. Liu, M.O. Rodgers, R.L.Guenslerk, Eco-driving for transit: An e_ective strategy to conserve fuel and emissions. Appl. Energy (2016). https://doi.org/10.1016/j.apenergy.2016.09.101
A. Rionda, X.G. Paneda, R. Garcia, G. Diaz, D. Martinez, M. Mitre, D. Arbesu, I. Marin, Blended learning system for efficient professional driving. Computers & Education, vol 78, pp. 124-139 (2014)
M. Sivak, B. Schoettle, Eco-driving: strategic, tactical, and operational decisions of the driver that inuence vehicle fuel economy. Transp. Policy 22, 96–99 (2012)
A. Pereira, M. Alves, H. Macedo, Vehicle driving analysis in regards to fuel consumption using Fuzzy Logic and OBD-II devices», en 2016 8th Euro American Conference on Telematics and Information Systems (EATIS), abr. 2016, pp. 1–4. https://doi.org/10.1109/EATIS.2016.7520160
E. Erikcsson, Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transp. Res. Part D: Transport, 325–345J (2001)
J.E. Meseguer, C.K. Toh, C.T. Calafate, J.C. Cano, P. Manzoni, Drivingstyles: a mobile platform for driving styles and fuel consumption characterization. J. Commun. Netw. 19(2), 162–168, abr. 2017. https://doi.org/10.1109/JCN.2017.000025
V. Corcoba Magaña, M. Muñoz Organero, Eco-driving: Energy Saving Based on Driver Behavior. Juan Antonio Ortega Ramírez, Alejandro Fernández-Montes, Juan Antonio Álvarez (2015)
N. Karginova, S. Byttner, M. Svensson, Data-driven methods for classification of driving styles in buses. SAE International, Warrendale, PA, SAE Technical Paper 2012–01–0744, abr. 2012. https://doi.org/10.4271/2012-01-0744
D. Dörr, D. Grabengiesser, F. Gauterin, Online driving style recognition using fuzzy logic, en 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), oct. 2014, pp. 1021–1026. https://doi.org/10.1109/ITSC.2014.6957822
W. Dong, J. Li, R. Yao, C. Li, T. Yuan, Y.L. Wang, Characterizing driving styles with deep learning, ArXiv160703611 Cs, Oct. 2016, Accedido: oct. 29, 2020. [En línea]. Disponible en: http://arxiv.org/abs/1607.03611
J.J. Molina Campoverde, Driving mode estimation model based in machine learning through PID’s signals analysis obtained from OBD II, in Applied Technologies, pp. 80–91. Cham (2020). https://doi.org/10.1007/978-3-030-42520-3_7
R.C. Néstor Diego, P.A. Molina Campoverde, G.P. Quirola Novillo, A.K. Naula Bermeo, Development of an Algorithm Capable of Classifying the Starting, Gear Change and Engine Brake Variables of a Vehicle by Analyzing OBD II Signals, in M. Botto-Tobar, W. Zamora, J. Larrea Plúa, J. Bazurto Roldan, A. Santamaría Philco (eds) Systems and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol. 1273. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-59194-6_11
P.A. Molina Campoverde, N.D. Rivera Campoverde, G.P. Novillo Quirola, N.A.K. Bermeo, Characterization of braking and clutching events of a vehicle through OBD II signals, in M. Botto-Tobar, W. Zamora, J. Larrea Plúa, J. Bazurto Roldan, A. Santamaría Philco (eds.) Systems and Information Sciences. ICCIS 2020. Advances in Intelligent Systems and Computing, vol. 1273. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-59194-6_12
AEADE: Anuario 2018. http://www.aeade.net/wp-content/uploads/2019/03/Anuario%202018.pdf. Accesed 1 Nov 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rivera, N.D., Molina, P.A., Bermeo, A.K., Bermeo, O.E., Figueroa, J.L. (2022). Driving Style Analysis by Studying PID’s Signals for Determination of Its Influence on Pollutant Emissions. In: Rocha, Á., López-López, P.C., Salgado-Guerrero, J.P. (eds) Communication, Smart Technologies and Innovation for Society . Smart Innovation, Systems and Technologies, vol 252. Springer, Singapore. https://doi.org/10.1007/978-981-16-4126-8_30
Download citation
DOI: https://doi.org/10.1007/978-981-16-4126-8_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4125-1
Online ISBN: 978-981-16-4126-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)