http://iet.metastore.ingenta.com
1887

Implementation of velocity optimisation strategy based on preview road information to trade off transport time and fuel consumption for hybrid mining trucks

Implementation of velocity optimisation strategy based on preview road information to trade off transport time and fuel consumption for hybrid mining trucks

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Unmanned mining is one of the essential fields of mining technology at present. Therefore, optimisation with known driving speed cycle for fuel economy, emissions, driveability, and more is no longer exclusive, the optimal velocity trajectory based on road information has been studied. Given that both fuel consumption and transport time influence transport costs of mining trucks, an energy management strategy (EMS) based on velocity optimisation is proposed and illustrated on a series hybrid electric mining truck in this study. The vehicle speed and SOC are adopted as state variables. Then two-scale dynamic programming is applied to calculate optimum velocity trajectory and power distribution. Simulation results reveal that the weighting coefficients of transport time and fuel economy can be optimally distributed for the different design requirements. Compared to the results under the known driving speed cycle, the proposed EMS can enhance fuel economy by 26.59% under the guarantee of same transport time, or transport time can be reduced by 42.4% without sacrificing the fuel consumption. Therefore, the proposed velocity optimisation strategy can reduce transport costs for mining enterprises significantly.

References

    1. 1)
      • 1. Yi, T., Ma, F., Jin, C., et al: ‘A novel coupled hydro-pneumatic energy storage system for hybrid mining trucks’, Energy, 2018, 143, pp. 704718.
    2. 2)
      • 2. Wu, G., Zhang, X., Dong, Z.: ‘Powertrain architectures of electrified vehicles: review, classification and comparison’, J. Franklin Inst., 2015, 352, (2), pp. 425448.
    3. 3)
      • 3. Tang, X., Yang, W., Hu, X., et al: ‘A novel simplified model for torsional vibration analysis of a series-parallel hybrid electric vehicle’, Mech. Syst. Signal Process., 2016, 85, pp. 329338.
    4. 4)
      • 4. Tang, X., Hu, X., Yang, W., et al: ‘Novel torsional vibration modeling and assessment of a power-split hybrid electric vehicle equipped with a dual-mass flywheel’, IEEE Trans. Veh. Technol., 2018, 67, (3), pp. 19902000.
    5. 5)
      • 5. Chang, D.J., Morlok, E.K.: ‘Vehicle speed profiles to minimize work and fuel consumption’, J. Transp. Eng, 2005, 131, (3), pp. 173182.
    6. 6)
      • 6. Tribioli, L.: ‘Energy-based design of powertrain for a re-engineered post-transmission hybrid electric vehicle’, Energies, 2017, 10, (7), p. 918.
    7. 7)
      • 7. Kum, D., Peng, H., Bucknor, N.K.: ‘Optimal energy and catalyst temperature management of plug-in hybrid electric vehicles for minimum fuel consumption and tail-pipe emissions’, IEEE Trans. Control Syst. Technol, 2013, 21, (1), pp. 1426.
    8. 8)
      • 8. Liang, J., Yang, H., Wu, J., et al: ‘Shifting and power sharing control of a novel dual input clutchless transmission for electric vehicles’, Mech. Syst. Signal Process, 2018, 104, pp. 725743.
    9. 9)
      • 9. Yang, B., Zheng, R., Shimono, K., et al: ‘Evaluation of the effects of in-vehicle traffic lights on driving performances for unsignalised intersections’, IET Intell. Transp. Syst, 2017, 11, (2), pp. 7683.
    10. 10)
      • 10. Li, Z., Xu, C., Li, D., et al: ‘Comparing the effects of ramp metering and variable speed limit on reducing travel time and crash risk at bottlenecks’, IET Intell. Transp. Syst, 2018, 12, (2), pp. 120126.
    11. 11)
      • 11. Cebecauer, M., Jenelius, E., Burghout, W.: ‘Integrated framework for real-time urban network travel time prediction on sparse probe data’, IET Intell. Transp. Syst, 2017, 12, (1), pp. 6674.
    12. 12)
      • 12. Schwarzkopf, A.B., Leipnik, R.B.: ‘Control of highway vehicles for minimum fuel consumption over varying terrain’, Transp. Res, 1977, 11, (4), pp. 279286.
    13. 13)
      • 13. Ngo, D.V., Hofman, T., Steinbuch, M., et al: ‘An optimal control-based algorithm for hybrid electric vehicle using preview route information’. inAm. Control. Conf. (ACC), 2010, Baltimore, MD, USA, 2010, pp. 58185823.
    14. 14)
      • 14. Sun, C., Sun, F., He, H.: ‘Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles’, Appl. Energy, 2017, 185, pp. 16441653.
    15. 15)
      • 15. Mensing, F., Trigui, R., Bideaux, E.: ‘Vehicle trajectory optimization for hybrid vehicles taking into account battery state-of-charge’, in2012 IEEE Veh. Power. Propul. Conf., IEEE, 2012, pp. 950955.
    16. 16)
      • 16. Qi, X., Wu, G., Hao, P., et al: ‘Integrated connected eco-driving system for phevs with co-optimization of vehicle dynamics and powertrain operations’, IEEE Trans. Intell. Veh., 2017, 2, (1), pp. 213.
    17. 17)
      • 17. Sun, C., Guanetti, J., Borrelli, F., et al: ‘Robust eco-driving control of autonomous vehicles connected to traffic lights’, 2018..
    18. 18)
      • 18. Rousseau, A., Shen, D., Karbowski, D.: ‘Fuel efficient speed optimization for real-world highway cruising’, 2018..
    19. 19)
      • 19. Asadi, B., Vahidi, A.: ‘Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time’, IEEE Trans. Control Syst. Technol, 2011, 19, (3), pp. 707714.
    20. 20)
      • 20. Sun, C., Moura, S.J., Hu, X., et al: ‘Dynamic traffic feedback data enabled energy management in plug-in hybrid electric vehicles’, IEEE Trans. Control Syst. Technol, 2015, 23, (3), pp. 10751086.
    21. 21)
      • 21. Boriboonsomsin, K., Barth, M.: ‘Impacts of road grade on fuel consumption and carbon dioxide emissions evidenced by use of advanced navigation systems’, Transp. Res. Rec. J. Transp. Res. Board, 2009, 2139, pp. 2130.
    22. 22)
      • 22. Park, S., Rakha, H.: ‘Energy and environmental impacts of roadway grades’, Transp. Res. Rec. J. Transp. Res. Board, 2006, 1987, pp. 148160.
    23. 23)
      • 23. Hu, J., Shao, Y., Sun, Z., et al: ‘Integrated optimal eco-driving on rolling terrain for hybrid electric vehicle with vehicle-infrastructure communication’, Transp. Res. Part C Emerg. Technol, 2016, 68, pp. 228244.
    24. 24)
      • 24. Zhang, C., Vahidi, A., Pisu, P., et al: ‘Role of terrain preview in energy management of hybrid electric vehicles’, IEEE Trans. Veh. Technol, 2010, 59, (3), pp. 11391147.
    25. 25)
      • 25. Huang, W., Bevly, D.M., Li, X., et al: ‘3D road geometry based optimal truck fuel economy’, inVolume 16: Transp. Syst.ASME, 2007, pp. 6370.
    26. 26)
      • 26. van Keulen, T., De Jager, B., Serrarens, A., et al: ‘Optimal energy management in hybrid electric trucks using route information’, Oil Gas Sci. Technol. del'Institut Français du Pétrole, 2010, 65, (1), pp. 103113.
    27. 27)
      • 27. Ganji, B., Kouzani, A.Z.:‘A study on look-ahead control and energy management strategies in hybrid electric vehicles’, inControl Autom. (ICCA), 2010 8th IEEE Int. Conf. on, 2010, pp. 388392.
    28. 28)
      • 28. Yang, W.: ‘Investigation of a novel coaxial power-split hybrid powertrain for mining trucks’, Energies, 2018, 11, (1), p. 172.
    29. 29)
      • 29. Zhang, C., Vahidi, A.: ‘Route preview in energy management of plug-in hybrid vehicles’, IEEE Trans. Control Syst. Technol, 2012, 20, (2), pp. 546553.
    30. 30)
      • 30. Sun, D., Lin, X., Qin, D., et al: ‘Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus’, Chinese J. Mech. Eng, 2012, 25, (6), pp. 11611170.
    31. 31)
      • 31. Wong, J.Y.: ‘Theory of ground vehicles’ (John Wiley & Sons, New York, NY, USA, 2008).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5054
Loading

Related content

content/journals/10.1049/iet-its.2018.5054
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address