Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI 10.5937/jaes17-22106
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions. 
Creative Commons License

Volume 17 article 599 pages: 217 - 223

Marko Stokic*
University of Belgrade - Faculty of Transport and Traffic Engineering Serbia
Vladan Momcilovic
University of Belgrade - Faculty of Transport and Traffic Engineering Serbia
Davor Vujanovic

University of Belgrade - Faculty of Transport and Traffic Engineering Serbia

It is well known that fuzzy logic is a processing tool in circumstances lacking of clear linguistic information, as well as making conclusions based on imprecise assertions and rough data. Eco-driving rules that the drivers should comply with are not always made of concrete values (exact acceleration / deceleration rates, torque or headway / distance kept from the vehicle ahead, etc.), but often linguistically expressed and subjective (e.g. soft acceleration, mid-range engine speed, soft deceleration, sufficient distance, etc.). Therefore, the authors recognized fuzzy logic potentials as an efficient tool to overcome all mentioned barriers and thus to increase vehicle energy eficiency and reduce emissions of harmful gases which are main goals of eco-driving. The primary objective of this paper is to raise the awareness on the potentials and efficiency of fuzzy logic systems’ use in eco-driving as a tool for achieving more ecologically & economi-cally sustainable road transport. The rules that drivers should follow in order to achieve and maintain eco-driving goals, as well as the parameters to be monitored to evaluatedriver’s behaviour i.e. the compliance with eco-driving rules are presented in the paper. The authors propose a driver rating system based on the fuzzy logic model constructed within MatLab. Within the proposed model the input parameters are actual acceleration/deceleration rates, engine speed and accelerator pedal pressure(APP) and while model output are driver ratings (scores ranging from 0 to 10 points) after completed a driving cycle. A real-world example based on data collected via vehicle OBDII connector by a TEXA logging device in realistic vehicle operation conditions. The consequent actual results of drivers’ behaviour rating tool based on the proposed model are presented in the paper.

View article

The paper is partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia through project TR36010. Also the authors wish to express their gratitude to the company „Marinković HOFMANN“ for the support in the research and for providing the TEXA MATRIX device for real-time data collection in real-world conditions.

  1. Fors, C., Kircher, K., Ahlstrom, C.(2015).Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance. Transportation Research Part C, vol. 58, 706-720, DOI:10.1016/j.trc.2015.03.035
  2. Beloufa, S., Cauchard, F., Vedrenne, J., Vailleau, B., Kemeny, A., Merienne, F., Boucheix, J. M.(2017). Learning eco-driving behaviour in a driving simulator: Contribution of instructional videos and interactive guidance system. Transportation Research Part F, vol. 61, 201-216,DOI: 10.1016/j.trf.2017.11.010
  3. Momčilović, V., Dimitrijević, B., Stokić, M.(2017). Eco-driving – potentials and opportunities within green logistics. Proceedings of the 3rd Logistics International Conference LOGIC 2017, Belgrade, Serbia, 222-227
  4. Momčilović, V., Cvetković, M.(2011).Koncept obuke vozača za ekološku vožnju. Proceedings of conference“ Ka održivom transportu 2011”, 79-90
  5. Young, M.S., Birrell, S.A., Stanton, N.A.(2011). Safe Driving in green world: A review of driver performance benchmarks and technologies to support “smart” driving. Applied Ergonomics, vol. 42, no. 4, 533-539, DOI: 10.1016/j.apergo.2010.08.012
  6. Barkenbus, J.N.(2010). Eco-driving: An overlooked climate change initiative. Energy Policy, vol. 38, no.2, 762-769, DOI: 10.1016/j.enpol.2009.10.021
  7. Mendel, J.M.(2017). Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions 2nd Edition.Springer International Publishing, DOI:10.1007/978-3-319-51370-6
  8. Zadeh, L. (2015). Fuzzy logic – a personal perspective. Fuzzy Sets and Systems, vol. 281, 4-20, DOI:10.1016/j.fss.2015.05.009
  9. Teodorović, D., Šelmić, M. (2012).Računarska inteligencija u saobraćaju.University of Belgrade, Faculty of Transport and Traffic Engineering
  10. Oblak, L., Kuzman, M.K., Grošelj, P.(2017). A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company. Journal of Applied Engineering Science, vol. 15, no. 3, 258-271, DOI: 10.5937/jaes15-13399
  11. Nunes, I.L.(2012). Fuzzy systems to support industrial engineering management. Journal of Applied Engineering Science, vol. 10, no. 3, 143-146, DOI:10.5937/jaes10-2510
  12. Araujo, R., Igreja, A., de Castro, R., Araujo, R. E. (2012). Driving Coach: a Smartphone Application to Evaluate Driving Efficient Patterns.Proceedings of 2012 Intelligent Vehicle Symposium, Spain1005-1010
  13. Pozueco, L., Pañeda, X. G., Tuero, A. G., Díaz, G., García, R., Melendi, D., Pañeda, A. G., Sánchez, J. A.(2017). A methodology to evaluate driving efficiency for professional drivers based on a maturity model. Transportation Research Part C, vol. 85,
    148-167, DOI: 10.1016/j.trc.2017.09.017
  14. Chou, W-Y., Lin, Y-C., Lin, Y-H., Chen, S-Y. (2012). Intelligent eco-driving suggestion system based on vehicle loading model. Proceedings of 12th International Conference on ITS Telecommunications, 558-562, DOI: 10.1109/ITST.2012.6425241
  15. Massoud, R., Poslad, S., Bellotti, F., Berta, R., Mehran, K., De Gloria, A. (2018). A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games. International Journal of Serious Games, vol. 5, no. 4, 45-62,
  16. Sanguinetti, A., Kurani, K., Davies, D.(2017). The many reasons your mileage may vary: Toward a unifying typology of eco-driving behaviors. Transportation Research Part D, vol. 52, 73-84, DOI: 10.1016/j.trd.2017.02.005
  17. Beusen, B., Broekx, S., Denys, T., Beckx, C., Degraeuwe, B., Gijsbers, M., Scheepers, K., Govaerts, L., Torfs, R., Panis, L. I.(2009). Using on-board logging devices to study the longer-term impact of an eco-driving course. Transportation Research Part D, vol.14, no. 7, 514-520, DOI: 10.1016/j.trd.2009.05.009
  18. Gilman, E., Keskinarkaus, A., Tamminen, S., Pirttikangas, S., Roning, J., Riekki, J.(2015).Personalised assistance for fuel-efficient driving. Transportation Research Part C, vol. 58, 681-705, DOI: 10.1016/j.trc.2015.02.007
  19. Díaz-Ramirez, J., Giraldo-Peralta, N., Flórez-Ceron, D., Rangel, V., Mejía-Argueta, C., Huertas, J. I., Bernal, M.(2017).Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case. Transportation Research Part D, vol. 56, 258-270, DOI: 10.1016/j.trd.2017.08.012
  20. Cvetković, M., Momčilović V., Dimitrijević, B.(2015). Performance Indicators For Professional Drivers’ Evaluation In Supply Chain.Proceedings of 2nd Logistics International Conference LOGIC 2015, Belgrade, Serbia, 253-258