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Science China Technological Sciences

, Volume 60, Issue 3, pp 399–411 | Cite as

Efficient coordinated control of regenerative braking with pneumatic anti-lock braking for hybrid electric vehicle

  • YongChang Du
  • CunAn Qin
  • SiXiong You
  • HuaiCheng Xia
Article

Abstract

Urban bus has to start and stop frequently due to typical urban traffic conditions, which, however, can be put to good use by regenerative braking. Regenerative braking is a key technology which not only improves vehicle’s fuel economy in mild braking, but also ensures vehicle safety in emergency braking conditions. Because of the inherent limitations of traditional braking system in recycling energy, it is necessary to change its structure to decouple the brake pressure and the brake pedal force. To solve this problem, a compromise design combining traditional pneumatic braking system with brake-by-wire (BBW) system is adopted in this paper on parallel hybrid electric bus. With the transformed braking system, an efficient coordinated control strategy is proposed to solve the problem caused by the different response speeds of pneumatic braking and regenerative braking. The proposed control strategy is carried out, where the road condition varies and different control methods are adopted. Results show that the adopted braking system and the proposed coordinated control strategy are suitable for different roads, and effective in recovering energy and ensuring vehicle safety. At the same time, shorter braking distance and better control of slip ratio verify the performance of MPC compared with a logical threshold-based control. Therefore, this study may offer a useful theoretical reference to the choice of braking system and braking control strategy design in hybrid electric vehicle (HEV).

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References

  1. 1.
    Wang Q, Yu S, Li C, et al. Theoretical design and FPGA-based implementation of higher-dimensional digital chaotic systems. IEEE Trans Circuits Syst I, 2016, 63: 401–412MathSciNetCrossRefGoogle Scholar
  2. 2.
    Liu K X, Wu L L, Lü J H, et al. Finite-time adaptive consensus of a class of multi-agent systems. Sci China Tech Sci, 2016, 59: 22–32CrossRefGoogle Scholar
  3. 3.
    Yang C, Jiao X H, Li L, et al. Robust coordinated control for hybrid electric bus with single-shaft parallel hybrid powertrain. IEEE IET Control Theory Appl, 2015, 9: 270–282CrossRefGoogle Scholar
  4. 4.
    Zhang L P, Qi B N, Zhang R S, et al. Powertrain design and energy management of a novel coaxial series-parallel plug-in hybrid electric vehicle. Sci China Tech Sci, 2016, 59: 618–630CrossRefGoogle Scholar
  5. 5.
    Gao D, Jin Z, Zhang J, et al. Comparative study of two different powertrains for a fuel cell hybrid bus. J Power Sources, 2016, 319: 9–18CrossRefGoogle Scholar
  6. 6.
    Li L, Zhang Y, Yang C, et al. Model predictive control-based efficient energy recovery control strategy for regenerative braking system of hybrid electric bus. Energ Conv Manage, 2016, 111: 299–314CrossRefGoogle Scholar
  7. 7.
    Yang C, Song J, Li L, et al. Economical launching and accelerating control strategy for a single-shaft parallel hybrid electric bus. Mech Syst Signal Process, 2016, 76-77: 649–664CrossRefGoogle Scholar
  8. 8.
    Li L, Yang C, Zhang Y H, et al. Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus-route. IEEE Trans Veh Tech, 2015, 64: 2792–2803Google Scholar
  9. 9.
    Yang C, Jiao X H, Li L, et al. Electromechanical coupling driving control for single-shaft parallel hybrid powertrain. Sci China Tech Sci, 2014, 57: 541–549CrossRefGoogle Scholar
  10. 10.
    Li Y S, Zeng Q L, Wang C L, et al. Research on control strategy for regenerative braking of a plug-in hybrid electric city public bus. In: Proceedings of International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. 842–845Google Scholar
  11. 11.
    Zhang J Z, Chen X, Zhang P J. Integrated control of braking energy regeneration and pneumatic anti-lock braking. Proc Inst Mech Eng Part D-J Automob Eng, 2010, 224: 587–610CrossRefGoogle Scholar
  12. 12.
    Ko J, Ko S, Son H, et al. Development of brake system and regenerative braking cooperative control algorithm for automatic-transmission- based hybrid electric vehicles. IEEE Trans Veh Tech, 2015, 64: 431–440CrossRefGoogle Scholar
  13. 13.
    Zhang J, Lü C, Yue X, et al. Development of the electrically-controlled regenerative braking system for electrified passenger vehicle. Sae Technical Papers No. 2013-01-1463, 2013Google Scholar
  14. 14.
    Zhou Z, Mi C, Zhang G. Integrated control of electromechanical braking and regenerative braking in plug-in hybrid electric vehicles. IJVD, 2012, 58: 223–239CrossRefGoogle Scholar
  15. 15.
    Kumar C N, Subramanian S C. Cooperative control of regenerative braking and friction braking for a hybrid electric vehicle. Proc Inst Mech Eng Part D-J Automob Eng, 2016, 230: 103–116CrossRefGoogle Scholar
  16. 16.
    Zhang J, Lv C, Qiu M, et al. Braking energy regeneration control of a fuel cell hybrid electric bus. Energ Conv Manage, 2013, 76: 1117–1124CrossRefGoogle Scholar
  17. 17.
    Zhang J, Kong D, Chen L, et al. Optimization of control strategy for regenerative braking of an electrified bus equipped with an anti-lock braking system. Proc Inst Mech Eng Part D-J Automob Eng, 2012, 226: 494–506CrossRefGoogle Scholar
  18. 18.
    Bera T K, Bhattacharya K, Samantaray A K. Bond graph model-based evaluation of a sliding mode controller for a combined regenerative and antilock braking system. Proc Inst Mech Eng Part I-J Syst Control Eng, 2011, 225: 918–934Google Scholar
  19. 19.
    Li L, Li X, Wang X, et al. Transient switching control strategy from regenerative braking to anti-lock braking with a semi-brake-by-wire system. Vehicle Syst Dyn, 2016, 54: 231–257CrossRefGoogle Scholar
  20. 20.
    Li L, Zhang Y, Yang C, et al. Hybrid genetic algorithm-based optimization of powertrain and control parameters of plug-in hybrid electric bus. J Franklin Inst, 2015, 352: 776–801CrossRefMATHGoogle Scholar
  21. 21.
    Zhang L P, Li L, Qi B N, et al. Parameters optimum matching of pure electric vehicle dual-mode coupling drive system. Sci China Tech Sci, 2014, 57: 2265–2277CrossRefGoogle Scholar
  22. 22.
    Zhu H, Li L, Jin M, et al. Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control. Proc Inst Mech Eng Part D-J Automob Eng, 2013, 227: 1431–1445CrossRefGoogle Scholar
  23. 23.
    Tan S, Lu J, Hill D J. Towards a theoretical framework for analysis and intervention of random drift on general networks. IEEE Trans Automat Contr, 2015, 60: 576–581MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zhang L, Yu L, Pan N, et al. Cooperative control of regenerative braking and friction braking in the transient process of anti-lock braking activation in electric vehicles. Proc Inst Mech Eng Part D-J Automob Eng, 2016, 230: 1459–1476CrossRefGoogle Scholar
  25. 25.
    Li L, Yang K, Jia G, et al. Comprehensive tire-road friction coefficient estimation based on signal fusion method under complex maneuvering operations. Mech Syst Signal Process, 2015, 56-57: 259–276CrossRefGoogle Scholar
  26. 26.
    Pacejka H B, Bakker E. The magic formula tyre model. Vehicle Syst Dyn, 1992, 21: 1–18CrossRefGoogle Scholar
  27. 27.
    Sari A, Espanet C, Hissel D. Particle swarm optimization applied to the co-design of a fuel cell air circuit. J Power Sources, 2008, 179: 121–131CrossRefGoogle Scholar
  28. 28.
    Gokasan M, Bogosyan S, Goering D J. Sliding mode based powertrain control for efficiency improvement in series hybrid-electric vehicles. IEEE Trans Power Electron, 2006, 21: 779–790CrossRefGoogle Scholar
  29. 29.
    Chen Y, Dong H, Lu J, et al. A super-twisting-like algorithm and its application to train operation control with optimal utilization of adhesion force. IEEE Trans Intell Transp Syst, 2016, 17: 3035–3044CrossRefGoogle Scholar
  30. 30.
    Smith D E, Starkey J M. Effects of model complexity on the performance of automated vehicle steering controllers: Model development, validation and comparison. Vehicle Syst Dyn, 1995, 24: 163–181CrossRefGoogle Scholar
  31. 31.
    Satzger C, De Castro R, Bunte T. A model predictive control allocation approach to hybrid braking of electric vehicles. In: Proceedings of Intelligent Vehicles Symposium. Dearborn: IEEE, 2014. 286–292Google Scholar
  32. 32.
    Imura A, Takahashi T, Fujitsuna M, et al. Improved PMSM model considering flux characteristics for model predictive-based current control. IEEJ Trans Elec Electron Eng, 2015, 10: 92–100Google Scholar
  33. 33.
    Bolognani S, Bolognani S, Peretti L, et al. Combined speed and current model predictive control with inherent field-weakening features for PMSM drives. In: Proceedings of Electrotechnical Conference. IEEE, 2008. 472–478Google Scholar
  34. 34.
    Imura A, Takahashi T, Fujitsuna M, et al. Instantaneous-current control of PMSM using MPC: Frequency analysis based on sinusoidal correlation. In: Proceedings of 37th Annual Conference on IEEE Industrial Electronics Society. Melbourne: IEEE, 2011. 3551–3556Google Scholar
  35. 35.
    Li Y, Wu X, Lu J, et al. Synchronizability of duplex networks. IEEE Trans Circuits Syst II, 2016, 63: 206–210CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • YongChang Du
    • 1
  • CunAn Qin
    • 1
  • SiXiong You
    • 1
  • HuaiCheng Xia
    • 2
  1. 1.State Key Laboratory of Automotive Safety and EnergyTsinghua UniversityBeijingChina
  2. 2.College of Vehicle and Energy EngineeringYanshan UniversityQinhuangdaoChina

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