AN INVESTIGATION OF FUEL-CONSUMPTION FOR HEAVY-DUTY VEHICLES BASED ON THEIR DRIVING PATTERNS
Keywords:
Fuel-consumption, Fuel-modeling, Driving patterns, Heavy-duty vehicles, RegressionAbstract
With the growing energy requirements, it is important to understand the impacts of driving patterns on fuel-consumption. This study is aimed at investigating the quantification effects of driving patterns such as distance travelled, instantaneous speed, hard braking, hard accelerating, and engine idle timings on fuel-consumption of heavy-duty vehicles. The telematics data from a leading logistics firm in Thailand was collected for three different types of vehicles (i.e., 6-W NGV, 18-W NGV, and 18-W-Diesel) over a period of two months. Stepwise regression modeling approach was employed to predict fuel-consumption. The results showed that average error between the predicted and measured fuel-consumption was 12.02%, 7.07% and 12.85% for 6-W NGV, 12-W NGV, and 12-W Diesel vehicles, respectively. This research study shows that regression modelling can be used to observe the quantification impacts of real-world driving patterns in heavy-duty vehicle on fuel-consumption.
References
Ahn, K., Rakha, H., Asce, M., Trani, A., Asce, M., and Aerde, M.Van. (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels., 128(April):182-190.
Ando, R. and Nishihori, Y. (2012). A study on factors affecting the effective eco-driving. procedia-social and behavioral scien., 54:27-36. https://doi.org/10.1016/j.sbspro.2012 .09.722
Borek, J., Groelke, B., Earnhardt, C., and Vermillion, C. (2020). Economic optimal control for minimizing fuel consumption of heavy-duty trucks in a highway environment. IEEE Transactions on Control Systems Technol., 28(5):1652-1664. https://doi.org/10.1109/ TCS T.2019.2918472
Borger, B.De. and Mulalic, I. (2012). The determinants of fuel use in the trucking industry-volume, fleet characteristics and the rebound effect $. Transport Policy., 24:284-295. https://doi.org/10.1016/j.tranpol.2012.08.011
Bratt, H. and Ericsson, E. (2000). Measuring vehicle driving patterns–estimating the influence of different measuring intervals. In: Urban Transport Systems. Proceedings From the 2Nd Kfb Research Conference in Lund. Sweden, p. 7-8
Braun, A. and Rid, W. (2017). The influence of driving patterns on energy consumption in electric car driving and the role of regenerative braking. Transportation Research Procedia., 22:174-182. https://doi.org/10.1016/j.trpro. 2017.03.024
Chi, M., Wang, H., and Ouyang, M. (2015). Effect of driving patterns on fuel-economy for diesel and hybrid electric city buses. EVS28 Int. Electric Vehicle Symposium and Exhibition., 1-6.
EIA (2012). Energy-U.S. Energy Information Administration (EIA). (n.d.). Available from https://www.eia.gov/ todayinenergy/detail .php?id= 26212. Accessed date: May 23, 2018
Ericsson, E. (2000a). Driving pattern in urban areas-descriptive analysis and initial prediction model. Bulletin 185/3000 of Lunds University., 18. http://lup.lub.lu.se/record/627123
Ericsson, E. (2000b). Variability in urban driving patterns. Transportation Research Part D: Transport and Environment., 5(5):337-354. https://doi.org/10.1016/S13 61-9209(00)00003-1
Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment., 6(5):325-345. https://doi.org/10.1016/S13 61-9209(01)00003-7
Frey, H.C., Rouphail, N.M., Zhai, H., Farias, T.L., and Gonçalves, G.A. (2007). Comparing real-world fuel consumption for diesel- and hydrogen-fueled transit buses and implication for emissions. Transportation Research Part D: Transport and Environment., 12(4):281-291. https://doi.org/10.1016/j.trd.2007.03.003
Ganti, R.K., Pham, N., Ahmadi, H., Nangia, S., and Abdelzaher, T.F. (2010). GreenGPS: A participatory sensing fuel-efficient maps application. Procee of the 8th Int. Conference on Mobile Systems, Applications, and Services., 151-164. https://doi.org/10.1145/1814433.1814 450
Groelke, B., Borek, J., Earnhardt, C., Li, J., Geyer, S., and Vermillion, C. (2018). A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks. Procee of the American Control Conference., 834-839. https://doi .org/10.23919/ACC.2018.8431050
Kamalanathsharma, R.K., and Rakha, H.A. (2016). Leveraging Connected Vehicle Technology and Telematics to Enhance Vehicle Fuel Efficiency in the Vicinity of Signalized Intersections. J. Int. Transportation Systems: Technol., Planning, and Operations., 20(1):33-44. https://doi.org/10.1080/15472450.2014.889916
Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J., and Ramsey, D. (2009). Comparing Real-World Behaviors of Drivers With High versus Low Rates of Crashes and Near-Crashes. February., 1-204.
Lattemann, F., Neiss, K., Terwen, S., and Connolly, T. (2004). The predictive cruise control-A system to reduce fuel consumption of heavy duty trucks. SAE Technical Papers, 113(May 2021)., 139-146. https://doi.org/10.4271/2004-01-2616
Ma, H., Xie, H., Huang, D., and Xiong, S. (2015). Effects of driving style on the fuel consumption of city buses under different road conditions and vehicle masses. Transportation Research Part D: Transport and Environment., 41:205-216. https://doi.org/10.1016/j.trd. 2015.10.003
Mensing, F., Bideaux, E., Trigui, R., Ribet, J., and Jeanneret, B. (2014). Eco-driving: An economic or ecologic driving style?. Transportation Research Part C: Emerging Technol., 38:110-121. https://doi.org/10.1016/j.trc.2013. 10.013
Nutramon, T. and Supachart, C. (2009). Influence of driving cycles on exhaust emissions and fuel consumption of gasoline passenger car in Bangkok. J. Env. Sci., 21(5):604-611. https://doi.org/10.1016/S10010742(08)62314-1
Rolim, C., Baptista, P., Duarte, G., Farias, T., and Shiftan, Y. (2014). Quantification of the impacts of eco-driving training and real-time feedback on Urban buses driver’s behaviour. Transportation Research Procedia, 3(July)., 70-79. https://doi.org/10.1016/j.trpro.2014.10.092
Rutty, M., Matthews, L., Andrey, J., and Matto, T.Del. (2013). Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact. Transportation Research Part D: Transport and Env., 24:44-51. https://doi.org/10. 1016/j.trd.2013.05.006
Song, Y.Y., Yao, E.J., Zuo, T., and Lang, Z.F. (2013). Emissions and fuel consumption modeling for evaluating environmental effectiveness of ITS strategies. Discrete Dynamics in Nature and Society., 2013. https://doi.org/10 .1155/2013/581945
Staubach, M., Schebitz, N., Köster, F., and Kuck, D. (2014). Evaluation of an eco-driving support system. Transportation Research Part F: Psychology and Behaviour., 27:11-21. https://doi.org/10.1016/j.trf.2014. 09.006
Stillwater, T., Kurani, K.S., and Mokhtarian, P.L. (2017). The combined effects of driver attitudes and in-vehicle feedback on fuel economy. Transportation Research Part D., 52:277-288. https://doi.org/10.1016/j.trd.2017.02.013
Turri, V., Besselink, B., and Johansson, K. H. (2017). Cooperative Look-Ahead Control for Fuel-Efficient and Safe Heavy-Duty Vehicle Platooning. IEEE Transactions on Control Systems Technol., 25(1):12-28. https://doi.org/ 10.1109/TCST.2016.2542044
Voort, M. Van Der, Dougherty, M.S., and Maarseveen, M.Van. (2001). A prototype fuel-e ciency support tool., 9:279-296.
Wang, J. and Rakha, H. A. (2017). Fuel consumption model for heavy duty diesel trucks: Model development and testing. Transportation Research Part D: Transport and Env., 55:127-141. https://doi.org/10.1016/j.trd.2017.06.011
Zhang, L. and Zhuan, X. (2013). Braking-penalized receding horizon control of heavy-haul trains. IEEE Transactions on Intelligent Transportation Systems., 14(4):1620-1628. https://doi.org/10.1109/TITS.2013.2263532
