Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation

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

Buy article PDF
(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
Your details
Why are you recommending this title?
Select reason:
IET Electrical Systems in Transportation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, a double fuzzy control strategy for the parallel hybrid electric vehicle (HEV) is proposed, then based on the genetic algorithm (GA) to get better simulation results, and the results are verified by dynamic programming (DP) optimisation. First, the energy management strategy is established by fuzzy control theory. On this basis, considering braking energy recovery, this study designs a double fuzzy vehicle energy control strategy. A simulation analysis of the above two control strategies is carried out in urban dynamometer driving schedule, and the comparison with the work efficiency of the engine and fuel economy performance, respectively, is made; the simulation results show that the double fuzzy control strategy can effectively improve the HEV performance. In order to make rule base more accurate, this study also uses a GA to optimise the fuzzy control rules of the fuzzy controller. Then the DP is used to optimise the energy control strategy and obtain optimal results. The results verified that the design of fuzzy controllers is correct, and the optimised fuzzy control strategy by GA can improve the work efficiency of the engine and fuel consumption.


    1. 1)
      • 1. Bayer, J., Koplin, M., Butcher, J.A., et al: ‘Optimizing the University of Wisconsin's parallel hybrid-electric aluminum intensive vehicle’. SAE Technical Papers, 2000.
    2. 2)
      • 2. Caratozzolo, P., Serra, M., Riera, J.: ‘Energy management strategies for hybrid electric vehicles’. IEEE Electric Machines and Drives Conf., 2007, vol. 2, pp. 41248.
    3. 3)
      • 3. Schouten Niels, T., Salman Mutasim, A., Kheir Naim, A.: ‘Fuzzy logic control for parallel hybrid vehicle’. IEEE Trans. Control Syst. Technol., 2002, 10, pp. 460468.
    4. 4)
      • 4. Lai, L., Ehsani, M.: ‘Dynamic programming optimized constrained engine on and off control strategy for parallel HEV’. IEEE Vehicle Power and Propulsion Conf., 2013, pp. 422426.
    5. 5)
      • 5. Tribioli, L., Barbieri, M., Capata, R., et al: ‘A real time energy management strategy for plug-in hybrid electric vehicles based on optimal control theory’, Energy Procedia, 2014, 45, pp. 949958.
    6. 6)
      • 6. He, X., Parten, M., Maxwell, T.: ‘Energy management strategies for a hybrid electric vehicle’. 2005 IEEE Vehicle Power and Propulsion Conf., 2005, pp. 390394.
    7. 7)
      • 7. Donghyunl, K., Hyunsool, K.: ‘Vehicle stability control with regenerative braking and electronic brake force distribution for a four-wheel drive hybrid electric vehicle’, J. Automob. Eng., 2006, 6, pp. 683693.
    8. 8)
      • 8. Yusuf, G., Alireza, K., Ali, E.: ‘Energy management strategies for a hybrid electric vehicle’. 2010 IEEE Vehicle Power and Propulsion Conf., 2010, pp. 16.
    9. 9)
      • 9. Arash, Z., Behazad, A.: ‘A fuzzy – genetic algorithm approach for finding a new HEV control strategy idea’. 2010 IEEE PEDSTC, 2010, pp. 224229.
    10. 10)
      • 10. Japjeet, K., Piyush, S., Prerna, G.: ‘Genetic algorithm based speed control of hybrid electric vehicle’. 2013 IEEE Int. Career and College Counseling (IC3), 2013, pp. 6569.
    11. 11)
      • 11. Pérez, L.V., Bossio, G.R., Moitre, D., et al: ‘Optimization of power management in an hybrid electric vehicle using dynamic programming’. Math. Comput. Simul., 2006, 73, pp. 244254.
    12. 12)
      • 12. Li Y., Lu, X., Kar N, .: ‘Rule-based control strategy with novel parameters optimization using NSGA-II for power-split PHEV operation cost minimization’, IEEE Trans. Veh. Technol., 2014, 63, pp. 30513061.
    13. 13)
      • 13. Hu, F., Zhao, Z.: ‘Optimization of control parameters in parallel hybrid electric vehicles using a hybrid genetic algorithm’. IEEE Vehicle Power and Propulsion Conf., 2011, pp. 16.
    14. 14)
      • 14. Tan, G., Lin, C., Bai, Y., et al: ‘Multi-objective optimization of HEV transmission system parameters based on immune genetic algorithm’. IEEE Int. Conf. Communication Problem-Solving IEEE, 2015, pp. 426431.
    15. 15)
      • 15. Masood, S., Micheal, S.M., Quintin, G., et al: ‘Pareto front of energy storage size and series HEV fuel economy using bandwidth-based control strategy’, IEEE Trans. Transp. Electrif., 2016, 2, pp. 3651.
    16. 16)
      • 16. Emmanuel, V., Vincent, R., Rochdi, T.: ‘Global optimized design of an electric variable transmission for HEVs’, IEEE Trans. Veh. Technol., 2016, 65, pp. 67946798.
    17. 17)
      • 17. Kaur, J., Saxena, P., Gaur, P.: ‘Genetic algorithm based speed control of hybrid electric vehicle’. IEEE Int. Conf. Contemporary Computing, 2013, pp. 6569.
    18. 18)
      • 18. Liu, J., Peng, H.: ‘Modeling and control of a power-split hybrid vehicle’, IEEE Trans. Control Syst. Technol., 2008, 16, pp. 12421251.
    19. 19)
      • 19. Johannesson, L., Asbogard, M., Bo, E.: ‘Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming’, IEEE Trans. Intell. Transp. Syst., 2007, 8, pp. 7183.
    20. 20)
      • 20. Lin, C.C., Peng, H., Grizzle, J.W., et al: ‘Power management strategy for a parallel hybrid electric truck’, IEEE Trans. Control. Syst. Technol., 2003, 11, pp. 839849.
    21. 21)
      • 21. Ngo, V., Hofman, T., Steinbuch, M., et al: ‘Optimal control of the gearshift command for hybrid electric vehicles’, IEEE Trans. Veh. Technol., 2012, 61, pp. 35313543.

Related content

This is a required field
Please enter a valid email address