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

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


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.


Random forest OBD II Fuel consumption Eco drive K-means S-Golay 



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.


  1. 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. 2.
    International Organization for Standardization, Road vehicles, Diagnostic systems, Keyword Protocol 2000 (1999)Google Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)MathSciNetCrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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. 7.
    Barkenbus, J.N.: Eco-driving: an overlooked climate change initiative. Energy Policy 38(2), 762–769 (2010)CrossRefGoogle Scholar
  8. 8.
    Hooker, J.N.: Optimal driving for single-vehicle fuel economy. Transp. Res. A Gen. 22(3), 183–201 (1988)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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. 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. 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. 16.
    Damström, J., Gerlitz, C.: Classification of Power Consumption Patterns for Swedish Households Using K-means. K4. Power Consumption Analysis (2016)Google Scholar
  17. 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. 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. 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. 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. 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. 22.
    ISO 17359: Condition monitoring and diagnostics of machines (2018)Google Scholar
  23. 23.
    Google Maps. Accessed 01 July 2019
  24. 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. 25.
    Aparicio, F., Vera, C., Díaz, V.: Teoría de los vehículos automóviles. Universidad Politécnica de Madrid, Madrid, pp. 279–285 (1995)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Automotive Mechanical Engineering: GIIT Transport Engineering Research GroupSalesian Polytechnic UniversityCuencaEcuador

Personalised recommendations