Science China Technological Sciences

, Volume 60, Issue 3, pp 425–433 | Cite as

Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm

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Abstract

Improvements in fuel consumption and emissions of hybrid electric vehicle (HEV) heavily depend upon an efficient energy management strategy (EMS). This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle employing a quantum chaotic pigeon-inspired optimization (QCPIO) algorithm. In this approach, the torque of the engine and the motor is assigned by a fuzzy torque distribution controller which is based on the battery state of charge (SoC) and the required torque of the hybrid powertrain. The rules and membership functions of the fuzzy torque distribution controller are optimized simultaneously through the use of QCPIO algorithm. The simulation ground on ADVISOR demonstrates that this EMS improves fuel economy more effectually than original fuzzy and PSO_Fuzzy EMS.

Keywords

parallel hybrid electric vehicles (parallel HEV) energy management strategy (EMS) fuzzy controller pigeon-inspired optimization (PIO) algorithm quantum evolution chaotic search 

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References

  1. 1.
    Johnson V H, Wipke K B, Rausen D J. HEV control strategy for realtime optimization of fuel economy and emissions. McCarthy, 2000, 1: 1543–1557Google Scholar
  2. 2.
    Park J. Development of equivalent fuel consumption minimization strategy for hybrid electric vehicles. Int J Automotive Tech, 2012, 13: 835–843CrossRefGoogle Scholar
  3. 3.
    Patil R M, Filipi Z, Fathy H K. Comparison of supervisory control strategies for series plug-in hybrid electric vehicle powertrains through dynamic programming. IEEE T Contr Syst T, 2014, 22: 502–509CrossRefGoogle Scholar
  4. 4.
    Zou Y, Liu T, Sun F, et al. Comparative study of dynamic programming and Pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle. Energies, 2013, 6: 2305–2318CrossRefGoogle Scholar
  5. 5.
    Borhan H, Vahidi A, Phillips A M, et al. MPC-based energy management of a power-split hybrid electric vehicle. IEEE T Contr Syst T, 2012, 20: 593–603CrossRefGoogle Scholar
  6. 6.
    Xia C, Zhang C. Power management strategy of hybrid electric vehicles based on quadratic performance index. Energies, 2015, 8: 12458–12473CrossRefGoogle Scholar
  7. 7.
    Lee H D, Sul S K. Fuzzy-logic-based torque control strategy for parallel- type hybrid electric vehicle. IEEE T Indust Electr, 1998, 45: 625–632CrossRefGoogle Scholar
  8. 8.
    Pu J H, Yin C L, Zhang J W. Fuzzy torque control strategy for parallel hybrid electronic vehicles. Int J Automotive Tech, 2005, 6: 529–536Google Scholar
  9. 9.
    Xing J, He H W, Zhang X W. Genetic-fuzzy HEV control strategy based on driving cycle recognition. High Tech Lett, 2010, 16: 39–44Google Scholar
  10. 10.
    Zhou M L, Lu D K, Li W M, et al. Optimized fuzzy logic control strategy for parallel hybrid electric vehicle based on genetic algorithm. Appl Mech Mater, 2013, 274: 345–349CrossRefGoogle Scholar
  11. 11.
    Wu J, Zhang C H, Cui N X. Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition. Int J Automotive Tech, 2012, 13: 1159–1167CrossRefGoogle Scholar
  12. 12.
    Su Y X, Chi R. Multi-objective particle swarm-differential evolution algorithm. Neural Comput Appl, 2015, doi: 10.1007/s00521-015-2073-yGoogle Scholar
  13. 13.
    Murphey Y L, Chen Z H, Kiliaris L, et al. Intelligent power management in a vehicular system with multiple power sources. Power Sources, 2011, 196: 835–846CrossRefGoogle Scholar
  14. 14.
    Sun Y, Chen Z, Yan B J, et al. A learning method for energy optimization of the plug-in hybrid electric bus. Sci China Tech Sci, 2015, 58: 1242–1249CrossRefGoogle Scholar
  15. 15.
    Wu J, Zhang C H, Cui N X. PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. Int J Automotive Tech, 2008, 9: 53–69CrossRefGoogle Scholar
  16. 16.
    Duan H B, Qiao P. Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning. Int J Intell Comput Cybern, 2014, 7: 24–37MathSciNetCrossRefGoogle Scholar
  17. 17.
    Guilford T, Roberts S, Biro D. Positional entropy during pigeon homing II: Navigational interpretation of Bayesian latent state models. J Theor Biol, 2004, 227: 25–38MathSciNetCrossRefGoogle Scholar
  18. 18.
    Mora C, Davison C, Wild J, et al. Magnetoreception and its trigeminal mediation in the homing pigeon. Nature, 2004, 432: 508–511CrossRefGoogle Scholar
  19. 19.
    AI A N. New dimensions in non-classical neural computing, part II: Quantum, nano, and optical. Int J Intell Comput Cybern, 2008, 2: 513–573MathSciNetGoogle Scholar
  20. 20.
    Jeong Y W, Park J B, Jang S H, et al. A new quantum-inspired binary PSO: Application to unit commitment problems for power systems. IEEE T Power Syst, 2010, 25: 1486–1495CrossRefGoogle Scholar
  21. 21.
    Liao X, Zhou J, Ouyang S, et al. An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling. Int J Elect Power, 2013, 53: 34–42CrossRefGoogle Scholar
  22. 22.
    Tavazoei M S, Haeri M. Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Mat Comput, 2007, 187: 1076–1085MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Yuan X, Wang P, Yuan Y, et al. A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem. Energy Conv Manag, 2015, 100: 1–9CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of AutomationWuhan University of TechnologyWuhanChina

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