Skip to main content
SpringerLink
Log in

Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II

  • Conference paper
  • First Online:
Applied Technologies (ICAT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1194))

Included in the following conference series:

Abstract

In this paper a driving mode estimation model based in machine learning architecture is presented. With the statistic method, Random Forest, the highest inference of driving variables is determined through the best attributes for a training model based in OBD II data. Engine sensors variables are obtained with the aim of explaining the behavior of the PID signals in relation to the driving mode of a person, according to specific consumption and engine performance, characterizing the signals behavior in relation to the different driving modes. The investigation consists of 4 power tests in the dynamometer bank at 25%, 50%, 75% and 100% throttle valve opening to determine the relationship between engine performance and normal vehicle circulation, through the engine most influential variables like MAP, TPS, VSS, Ax and each the transmission ratio infer in the fuel consumption study and engine performance. In this study Random Forest is used achieving an accuracy rate of 0.98905.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Meseguer, J., Toh, C.K., Calafate, C.T., Cano, J., Manzoni, P.: Assessing the impact of driving behavior on instantaneous fuel consumption. In: 12th Annual IEEE Consumer Communications and Networking Conference (CCNC) (2015)

    Google Scholar 

  2. International Organization for Standardization, Road vehicles, Diagnostic systems, Keyword Protocol 2000 (1999)

    Google Scholar 

  3. Meseguer, J., Toh, C.K., Calafate, C., Cano, J.Y., Manzoni, P.: DrivingStyles: a mobile platform for driving styles and fuel consumption characterization. J. Commun. Netw. 19(2), 162–168 (2017)

    Article  Google Scholar 

  4. Lv, C., et al.: Cyber-physical system based optimization framework for intelligent powertrain control. SAE Int. J. Commer. Veh. 10(1), 254–264 (2017)

    Article  MathSciNet  Google Scholar 

  5. Mohd, T.A.T., Hassan, M., Aris, I., Azura, C., Ibrahim, B.S.K.K.: Application of fuzzy logic in multi-mode driving for a battery electric vehicle energy management. Int. J. Adv. Sci. Eng. Inf. Technol. 7, 284–290 (2017)

    Article  Google Scholar 

  6. Dia, H., Panwai, S.: Impact of driving behavior on emissions and road network performance. In: IEEE International Conference on Data Science and Data Intensive Systems (2015)

    Google Scholar 

  7. Barkenbus, J.N.: Eco-driving: an overlooked climate change initiative. Energy Policy 38(2), 762–769 (2010)

    Article  Google Scholar 

  8. Hooker, J.N.: Optimal driving for single-vehicle fuel economy. Transp. Res. A Gen. 22(3), 183–201 (1988)

    Article  Google Scholar 

  9. Sivak, M., Schoettle, B.: Eco-driving: strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy. Transp. Policy 22, 96–99 (2012)

    Article  Google Scholar 

  10. Andrieu, C., Saint Pierre, G.: Comparing effects of eco-driving training and simple advices on driving behavior. Procedia – Soc. Behav. Sci. 54, 211–220 (2012)

    Article  Google Scholar 

  11. Beckx, C., Panis, L.I., De Vlieger, I., Wets, G.: Influence of gear changing behavior on fuel-use and vehicular exhaust emissions. Highw. Urban Environ. 12, 45–51 (2007)

    Article  Google Scholar 

  12. McIlroy, R., Stanton, N., Godwin, L., Wood, A.: Encouraging eco-driving with visual, auditory, and vibrotactile stimuli. IEEE Trans. Hum.-Mach. Syst. 47(5), 661–672 (2017)

    Article  Google Scholar 

  13. Lapuerta, M., Armas, O., Agudelo, J., Sánchez, C.: Study of the altitude effect on internal combustion engine operation. Part 1: performance. Technol. Inf. 17(5), 21–30 (2006)

    Google Scholar 

  14. Rivera, N., Chica, J., Zambrano, I., García, C.: Estudio del comportamiento de un motor ciclo otto de inyección electrónica respecto de la estequiometría de la mezcla y del adelanto al encendido para la ciudad de cuenca. Revista Politécnica 40(1), 59–67 (2017)

    Google Scholar 

  15. Zhou, Y., Guo, J., Fu, L., Liang, T.: Research on aero-engine maintenance level decision based on improved artificial fish-swarm optimization random forest algorithm. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (2018)

    Google Scholar 

  16. Damström, J., Gerlitz, C.: Classification of Power Consumption Patterns for Swedish Households Using K-means. K4. Power Consumption Analysis (2016)

    Google Scholar 

  17. Corcoba, V., Muñoz, M.: Eco-driving: energy saving based on driver behavior. In: XVI Jornadas de ARCA sobre Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental (JARCA) (2015)

    Google Scholar 

  18. Pereira, A., Alves, M., Macedo, H.: Vehicle driving analysis in regard to fuel consumption using fuzzy logic and OBD-II devices. In: 2016 8th Euro American Conference on Telematics and Information Systems (EATIS) (2016)

    Google Scholar 

  19. Oñate, J.A., Christian M. Quintero, G., Pérez, J.M.: Intelligent erratic driving diagnosis based on artificial neural networks. In: IEEE ANDESCON (2010)

    Google Scholar 

  20. Chen, S., Lin, R., Liu, W., Tsai, J.: The semi-supervised classification of petrol and diesel passenger cars based on OBD and support vector machine algorithm. In: 2017 International Conference on Orange Technologies (ICOT) (2017)

    Google Scholar 

  21. Corcoba, V., Muñoz, M.: Artemisa: using an android device as an eco-driving assistant. Cyber J.: Multidiscip. J. Sci. Technol. J. Sel. Areas Mechatron. (JMTC), June Edition, 3–7 (2011)

    Google Scholar 

  22. ISO 17359: Condition monitoring and diagnostics of machines (2018)

    Google Scholar 

  23. Google Maps. https://www.google.com/maps. Accessed 01 July 2019

  24. Pang, C.K., et al.: Intelligent energy audit and machine management for energy-efficient manufacturing. In: IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS) (2011)

    Google Scholar 

  25. Aparicio, F., Vera, C., Díaz, V.: Teoría de los vehículos automóviles. Universidad Politécnica de Madrid, Madrid, pp. 279–285 (1995)

    Google Scholar 

Download references

Acknowledgment

To Mr. Nestor Diego Rivera Campoverde, for his direction and unconditional collaboration in the realization of the paper with his contributions and his knowledge throughout the entire process, in addition to the GIIT Transportation Engineering Research Group for his support for the completion of the paper.

Author information

Authors and Affiliations

  1. Automotive Mechanical Engineering: GIIT Transport Engineering Research Group, Salesian Polytechnic University, Cuenca, Ecuador

    Juan José Molina Campoverde

Authors
  1. Juan José Molina Campoverde
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan José Molina Campoverde .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

  3. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  4. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  5. Universidad Politécnica Salesiana, Guayaquil, Ecuador

    Guillermo Pizarro Vásquez

  6. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Benjamin Durakovic

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Molina Campoverde, J.J. (2020). Driving Mode Estimation Model Based in Machine Learning Through PID’s Signals Analysis Obtained From OBD II. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-42520-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42519-7

  • Online ISBN: 978-3-030-42520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation