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Study on Analysis of Real Road Driving Characteristics of Heavy-Duty Gas Delivery Tractor

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Abstract

Recently, there has been increasing interest in reducing hazardous and CO2 emissions from vehicles. For emission reduction, it is necessary to analyze operating conditions of vehicles by using real on-road data and to improve their effective performance. In particular, for heavy-duty vehicles, technical improvements and operational management are critical in terms of air quality management, for although the number of vehicles is small, the duration and level of emissions are relatively high. However, research data obtained from analyses of driving characteristics of heavy-duty domestic delivery vehicles are relatively insufficient. Technically, there are many difficulties in setting driving conditions for such analysis, such as measuring driving resistance and determining variations in the vehicle mass with the amount of freight. In this study, on the basis of the speed and engine output data of a heavy-duty gas delivery tractor obtained during real road driving, the driving characteristics of the vehicle was analyzed, its weight was estimated, and the effect of the vehicle’s weight on the driving energy and vehicle output was investigated.

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Abbreviations

A:

vehicle front area, m2

CI:

cycle index

C1 :

test coefficients 1, N (regression coefficients)

C2 :

test coefficients 2, N/(km/h)(regression coefficients)

C3 :

test coefficients 3, N/(km/h)2(regression coefficients)

Etrac :

trip vehicle traction energy, J

Eeng :

trip engine energy, J

K:

effective mass coefficient

Peng :

gross engine power, kW

Ptrac :

vehicle traction power, kW

PKE:

positive kinetic energy, m/s2

RCS:

relative cubic speed, m2/s2

RGE:

relative gradient

V:

vehicle speed, km/h

a:

vehicle acceleration, m/s2

crr :

tire rolling resistance coefficient

cd :

aero drag coefficient

d:

distance, m

dl:

vehicle driving distance during time duration, dt, m

m:

vehicle mass, kg

g:

gravitiational acceleration, m/s2

β:

angle of vehicle travel relative to gravity normal direction

ρ air :

air density, kg/m2

η tr :

transmission efficiency

normal:

normalized value

max:

maximum value

mean:

mean(averaged) value

References

  • Ates, M. and Matthews, R. D. (2012). Coastdown coefficient analysis of heavy-duty vehicles and application to the examination of the effects of grade and other parameters on fuel consumption. SAE Technical Paper No. 2012-01-2051.

  • Barlow, T. J., Latham, S., McCrae, I. S. and Boulter, P. G. (2009). A reference book of driving cycles for use in the measurement of road vehicle emissions. TRL Published Project Report.

  • Cho, B., Kees, D., Shah, N. and D’Urbal, V. (2018). A methodology of real-world fuel consumption estimation: Part 1. Drive Cycles. SAE Technical Paper No. 2018-01-0644.

  • Cho, S., Ko, S. and Lee, D. (2019). Assessment of greenhouse gas simulation tools for heavy duty vehicles. Korean Society of Automotive Engineers Autumn Conf. Gyeongju, Korea, 330–334.

  • Fontaras, G., Grigoratos, T., Savvidis, D., Anagnostopoulos, K., Luz, R., Rexeis, M. and Hausberger, S. (2016). An experimental evaluation of the methodology proposed for the monitoring and certification of CO2 emissions from heavy-duty vehicles in Europe. Energy, 102, 354–364.

    Article  Google Scholar 

  • Kim, J., Park, J., Cheon, M., Lee, D. and Ko, S. (2019) Development of reduced test cycle at chassis dynamometer to evaluate the characteristics of real driving emissions. Korean Society of Automotive Engineers Autumn Conf. Gyeongju, Korea, 285–291.

  • Kwon, S., Kwon, S., Kim, H. J., Seo, Y., Park, S. and Chon, M. S. (2016). A study on the characteristics of simulated real driving emissions by using random driving cycle. Trans. Korean Society of Automotive Engineers 24, 4, 454–462.

    Article  Google Scholar 

  • LaClair, T. J. (2012). Application of a tractive energy analysis to quantify the benefits of advanced efficiency technologies for medium-and heavy-duty trucks using characteristic drive cycle data. SAE Int. J. Commercial Vehicles 5, 2012-01-0361, 141–163.

    Article  Google Scholar 

  • Lee, B., Oh, K. and Kim, D. (2019a). A comparative CO2 emissions of LPG/CNG Bi-fuel vehicle using simulated real-world driving cycles. Trans. Korean Society of Automotive Engineers 27, 6, 479–486.

    Article  Google Scholar 

  • Lee, B., Yun, B., Jung, J., Kim, D., Cha, W., Lee, S. and Kim, I. (2018). Study on NOx emission characteristics of diesel light duty vehicles by analyzing massive driving data. Trans. Korean Society of Automotive Engineers 26, 5, 684–692.

    Article  Google Scholar 

  • Lee, I., Seo, D., Kim, S., Ko, S., Chun, Y. and Cho, S. (2015). A study on the impact of fuel economy as tactive resistance calculation methods on HD chassis dynamometer for medium-heavy duty vehicle. Trans. Korean Society of Automotive Engineers 23, 3, 307–314.

    Article  Google Scholar 

  • Lee, J., Choi, S., Park, J. and Lee, J. (2013). Development of a fuel economy test cycle for heavy duty vehicles. Korean Society of Automotive Engineers Spring Conf., 258–260.

  • Lee, S. H., Kim, M. H. and Lee, S. (2019b). Development of an energy prediction model based on driving data for predicting the driving distance of an electric vehicle. Int. J. Automotive Technology 20, 2, 389–395.

    Article  Google Scholar 

  • Park, S. (2019). Global trend of greenhouse gas regulation of heavy-duty vehicles. J. Korean Society of Automotive Engineers 41, 6, 58–61.

    Google Scholar 

  • Phlips, P. (2015). Analytic engine and transmission models for vehicle fuel consumption estimation. SAE Int. J. Fuels and Lubricants 8, 2, 423–440.

    Article  Google Scholar 

  • Zacharof, N., Tansini, A., Rujas, I. P., Grigoratos, T. and Fontaras, G. (2019). A generalized component efficiency and input-data generation model for creating fleet-representative vehicle simulation cases in VECTO. SAE Technical Paper No. 2019-01-1280.

  • Zhang, D., Ivanco, A. and Filipi, Z. (2015). Model-based estimation of vehicle aerodynamic drag and rolling resistance. SAE Int. J. Commercial Vehicles 8, 2015-01-2776, 433–439.

    Article  Google Scholar 

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Acknowledgement

This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) through the National Innovation Cluster R&D program (P040200001, Development of Key Components and Remodeling Technology for Heavy Hydrogen Fuel Cell Truck). It was also supported by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Evaluation Institute of Industrial Technology (KEIT) through the Technology Innovation Program (20002762, Development of RDE DB and Application Source Technology for Improvement of Real Road CO2 and Particulate Matter).

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Correspondence to Jae Woo Chung.

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Chung, J.W., Lee, B.H., Lee, S.W. et al. Study on Analysis of Real Road Driving Characteristics of Heavy-Duty Gas Delivery Tractor. Int.J Automot. Technol. 22, 1735–1742 (2021). https://doi.org/10.1007/s12239-021-0149-5

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