Model predictive control-based eco-driving strategy for CAV

Model predictive control-based eco-driving strategy for CAV

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In this study, an eco-driving strategy is proposed to enhance the fuel efficiency of the connected autonomous vehicle (CAV) in car-following scenarios. First, the longitudinal dynamic model and fuel-consumption model of the vehicle are established. The speed trajectory of the preceding vehicles is obtained via vehicle-to-vehicle/vehicle-to-infrastructure communication function of CAVs, which is used as the reference of the following vehicles. Second, a model predictive controller is presented to optimise fuel consumption of the following vehicle. Finally, simulations in urban and highway driving conditions demonstrate that the proposed controller enables effective tracking of the preceding vehicle in an energy-efficient way. Comparisons between the second and the third following vehicles verify the fuel-saving benefits of the proposed method.


    1. 1)
      • 1. Huang, Y., Khajepour, A., Ding, H., et al: ‘An energy-saving set-point optimiser with a sliding mode controller for automotive air-conditioning/refrigeration systems’, Appl. Energy, 2017, 188, pp. 576585.
    2. 2)
      • 2. Zhang, S., Wu, Y., Un, P., et al: ‘Modeling real-world fuel consumption and carbon dioxide emissions with high resolution for light-duty passenger vehicles in a traffic populated city’, Energy, 2016, 113, pp. 461471.
    3. 3)
      • 3. Li, L., Wang, X., Song, J.: ‘Fuel consumption optimization for smart hybrid electric vehicle during a car following process’, Mech. Syst. Signal Process., 2016, 87, (1), pp. 1729.
    4. 4)
      • 4. Wang, H., Huang, Y., Khajepour, A.: ‘Cyber-physical control for energy management of off-road vehicles with hybrid energy storage systems’, IEEE-ASME Trans. Mechatronics., 2018, DOI: 10.1109/TMECH.2018.2832019.
    5. 5)
      • 5. 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., 2017, 85, pp. 329338.
    6. 6)
      • 6. 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.
    7. 7)
      • 7. Li, C., Jing, H., Wang, R., et al: ‘Vehicle lateral motion regulation under unreliable communication links based on robust h∞ output-feedback control schema’, Mech. Syst. Signal Process., 2018, 104, pp. 171187.
    8. 8)
      • 8. Wang, R., Jing, H., Wang, J., et al: ‘Robust output-feedback based vehicle lateral motion control considering network-induced delay and tire force saturation’, Neurocomputing, 2016, 214, pp. 409419.
    9. 9)
      • 9. Van Mierlo, J., Maggetto, G., Van Burgwal, E., et al: ‘Driving style and traffic measures - influence on vehicle emissions and fuel consumption’, Proc. Inst. Mech. Eng. D., J. Automob., 2005, 218, (1), pp. 4350.
    10. 10)
      • 10. Li, S.E., Hu, X., Li, K., et al: ‘Mechanism of vehicular periodic operation for optimal fuel economy in free-driving scenarios’, IET Intell. Transp. Syst., 2015, 9, (3), pp. 306313.
    11. 11)
      • 11. Xu, S., Li, S.E., Zhang, X., et al: ‘Fuel-optimal cruising strategy for road vehicles with step-gear mechanical transmission’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (6), pp. 34963507.
    12. 12)
      • 12. Li, S.E., Peng, H.: ‘Strategies to minimize fuel consumption of passenger cars during car following scenarios’, Proc. Inst. Mech. Eng. D., J. Automob., 2011, 226, (3), pp. 21072112.
    13. 13)
      • 13. Li, S.E., Peng, H., Li, K., et al: ‘Minimum fuel control strategy in automated car following scenarios’, IEEE Trans. Veh. Technol., 201261, (3), pp. 9981007.
    14. 14)
      • 14. Huang, Y., Wang, H., Khajepour, A., et al: ‘Model predictive control power management strategies for HEVs: a review’, J. Power Source, 2017, 341, pp. 91106.
    15. 15)
      • 15. Li, L., Lu, Y., Wang, R., et al: ‘A 3-dimensional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC’, IEEE Trans. Ind. Electron., 2016, 64, pp. 33893401.
    16. 16)
      • 16. Wu, J., Cheng, S., Liu, B., et al: ‘A human–machine-cooperative-driving controller based on AFS and DYC for vehicle dynamic stability’, Energies, 2017, 10, (11), pp. 17371746.
    17. 17)
      • 17. Wu, J., Wang, X., Li, L., et al: ‘Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control’, Energy, 2018, 145, (2), pp. 301312.
    18. 18)
      • 18. Kamal, M.A.S., Mukai, M., Murata, J., et al: ‘On board eco-driving system for varying road-traffic environments using model predictive control’. IEEE Int. Conf. Control Applications, Yokohama, Japan, September 2010, pp. 16361641.
    19. 19)
      • 19. Kamal, M.A.S., Mukai, M., Murata, J., et al: ‘Ecological vehicle control on roads with up–down slopes’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (3), pp. 783794.
    20. 20)
      • 20. Hellström, E., Ivarsson, M., Âslund, J., et al: ‘Look-ahead control for heavy trucks to minimize trip time and fuel consumption’, Control Eng. Pract., 2009, 17, (2), pp. 245254.
    21. 21)
      • 21. Lim, H., Su, W., Mi, C.C.: ‘Distance-based ecological driving scheme using a two-stage hierarchy for long-term optimization and short-term adaptation’, IEEE Trans. Veh. Technol., 2017, 66, (3), pp. 19401949.
    22. 22)
      • 22. Li, S.E., Guo, Q., Xu, S., et al: ‘Performance enhanced predictive control for adaptive cruise control system considering road elevation information’, IEEE Trans. Intell. Veh., 2017, 2, (3), pp. 150160.
    23. 23)
      • 23. Yu, K., Yang, J., Yamaguchi, D.: ‘Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information’, Control Theory Technol., 2015, 13, (1), pp. 1728.
    24. 24)
      • 24. Ozatay, E., Ozguner, U., Filev, D., et al: ‘Analytical and numerical solutions for energy minimization of road vehicles with the existence of multiple traffic lights’. IEEE Conf. Decision and Control, Palazzo dei Congressi, Florence, Italy, December 2013, pp. 71377142.
    25. 25)
      • 25. Jing, J.: ‘Vehicle fuel consumption optimization using model predictive control based on V2V communication’, Master's thesis, The Ohio State University, 2014.
    26. 26)
      • 26. Masikos, M., Demestichas, K., Adamopoulou, E., et al: ‘Machine-learning methodology for energy efficient routing’, IET Intell. Transp. Syst., 8, 2013, (3), pp. 255265.
    27. 27)
      • 27. Xie, W., Bonis, I., Theodoropoulos, C.: ‘Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems’, J. Process Control, 2015, 35, pp. 5058.
    28. 28)
      • 28. Torrisi, G., Grammatico, S., Cortinovis, A., et al: ‘Model predictive approaches for active surge control in centrifugal compressors’, IEEE Trans. Control Syst. Technol., 2016, 25, (6), pp. 19471960.

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