access icon free Genetic-based bacteria foraging to optimise energy management of hybrid electric vehicles

© The Institution of Engineering and Technology
Received 20/03/2013, Accepted 02/10/2013, Revised 18/09/2013, Published 25/02/2014

This study deals with energy management in hybrid electric vehicles. This study first formulates energy management as an optimisation problem. Novelty of this study is use of a population-based hybrid algorithm, genetic-based bacteria foraging, to the problem of energy management. This hybrid algorithm hybridises merits of both genetic algorithm and bacteria foraging optimisation. Encouraging simulation results show that there is a reduction in fuel consumption, whereas retaining technical and commercial efficiency of the electric vehicle.

Inspec keywords: hybrid electric vehicles; energy management systems; genetic algorithms

Other keywords: energy management; optimisation problem; genetic algorithm; genetic-based bacteria foraging; bacteria foraging optimisation; hybrid electric vehicles; fuel consumption; population-based hybrid algorithm

Subjects: Optimisation techniques; Transportation

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 10. Yildiz, A.R., Ozturk, F.: ‘Hybrid Taguchi-Harmony search approach for shape optimization, recent advances in harmony search algorithm-studies in computational intelligence’ (Springer, 2010).
    22. 22)
      • 29. Zhang, P., Du, C., Yan, F., Kang, J.: ‘Energy efficiency of a Ni-MH battery used in hybrid electric vehicles’. Int. Conf. on Electric Information and Control Engineering (ICEICE), 2011, pp. 4996–4999.
    23. 23)
      • 21. Passino, K.M.: ‘Biomimicry of bacterial foraging for distributed optimization and control’, IEEE Control Syst., 2002, 22, (3), pp. 52–67.
    24. 24)
      • 3. Tate, E.D., Boyd, S.P.: ‘Finding ultimate limits of performance for hybrid electric vehicles’. Proc. SAE Future Transportation Technology Conf., Costa Mesa, CA, August 2000. SAE Paper 2000-01-3099.
    25. 25)
      • 8. Paganelli, G., Ercole, G., Brahma, A., Guezennec, Y., Rizzoni, G.: ‘General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles’, JSAE Rev., 2001, 22, (4), pp. 511–518.
    26. 26)
      • 24. Michalewicz, Z.: ‘Genetic Algorithms + Data Structures = Evolution Programs’ (Springer-Verlag, New York, 1999).
    27. 27)
      • 26. Smith, J.N.M.: ‘The food searching behaviour of two European thrushes. II. The adaptiveness of the search patterns’, Behavior, 1974, 49, (1–2), pp. 1–61.
    28. 28)
      • 9. Yildiz, A.R.: ‘Hybrid Taguchi-Harmony search algorithm for solving engineering optimization problems’, Int. J. Ind. Eng Theory Appl. Pract., 2008, 15, (3), pp. 286–293.
    29. 29)
      • 22. Liu, Y., Passino, K.M.: ‘Biomimicry of social foraging bacteria for distributed optimization: models, principles, and emergent behaviors’, J. Optim. Theory Appl., 2002, 115, (3), pp. 603–628.
    30. 30)
      • 13. Ali, R., Yildiz, A.R.: ‘An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry’, J. Mater. Process. Technol., 2009, 50, (4), pp. 224–228.
    31. 31)
      • 18. Fang, L., Qin, S., Xu, G., Li, T., Zhu, K.: ‘Simultaneous optimization for hybrid electric vehicle parameters based on multi-objective genetic algorithms’, Energies, 2011, 4, pp. 532–544.
    32. 32)
      • 11. Ali, R., Yildiz, A.R.: ‘A new design optimization framework based on immune algorithm and Taguchi method’, Comput. Ind., 2009, 60, (8), pp. 613–620.
    33. 33)
      • 15. Ali, R., Yildiz, A.R., Ozturk, F.: ‘Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation’, J. Eng. Manuf., 2006, 220, (12), pp. 2041–2053.
    34. 34)
      • 25. Michalewicz, Z.: ‘Genetic Algorithms + Data Structures = Evolution Programs’ (Springer-Verlag, Berlin Heideberg, 1996).
    35. 35)
      • 30. Samanta, C.K., Padhy, S.K., Panigrahi, S.P., Panigrahi, B.K.: ‘Hybrid swarm intelligence methods for energy management in hybrid electric vehicles’, IET Electr. Syst. Transp., 2013, 3, (1), pp. 22–29.
    36. 36)
      • 28. Gendron, R.P., Staddon, J.E.R.: ‘Searching for cryptic prey: the effect of search rate’, Ame. Naturalist, 1983, 121, (2), pp. 172–186.
    37. 37)
      • 19. Won, J.-S., Langari, R., Ehsani, M.: ‘An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT’, IEEE Trans. Control Syst. Technol., 2005, 13, (2), pp. 313–320.
    38. 38)
      • 4. Delprat, S., Lauber, J., Guerra, T.M., Rimaux, J.: ‘Control of a parallel hybrid powertrain: optimal control’, IEEE Trans. Veh. Technol., 2004, 53, (3), pp. 872–881.
    39. 39)
      • 6. Lin, C.-C., Peng, H., Grizzle, J.W., Kang, J.M.: ‘Power management strategy for a parallel hybrid electric truck’, IEEE Trans. Control Syst. Technol., 2003, 11, (6), pp. 839–849.
    40. 40)
      • 7. Scordia, J., Desbois-Renaudin, M., Trigui, R., Jeanneret, B., Badin, F.: ‘Global optimization of energy management laws in hybrid vehicles using dynamic programming’, Int. J. Veh. Des., 2005, 39, (4), pp. 349–367.
    41. 41)
      • 16. Yildiz, A.R., Solanki, K.N.: ‘Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach’ in press, 2011.
    42. 42)
      • 23. http://www.softcomputing.net/bfoa-chapter.pdf.
    43. 43)
      • 14. Ali, R., Yildiz, A.R., Ozturk, N., Kaya, N., Ozturk, F.: ‘Hybrid multi-objective shape design optimization using Taguchi's method and genetic algorithm’, Struct. Multidiscip. Optim., 2007, 34, (4), pp. 277–365.
    44. 44)
      • 5. Sciarretta, A., Back, M., Guzzella, L.: ‘Optimal control of parallel hybrid electric vehicles’, IEEE Trans. Control Syst. Technol., 2004, 12, (3), pp. 352–362.
    45. 45)
      • 27. Giraldeau, L.A., Beauchamp, G.: ‘Food exploitation: searching for the optimal joining policy’, Trends Ecol. Evol., 1999, 14, (3), pp. 102–106.
    46. 46)
      • 12. Ali, R., Yildiz, A.R.: ‘A novel particle swarm optimization approach for product design and manufacturing’, Int. J. Adv. Manuf. Technol., 2009, 40, (5–6), pp. 617–628.
    47. 47)
      • 2. Schouten, N.J., Salman, M.A., Kheir, N.A.: ‘Fuzzy logic control for parallel hybrid vehicles’, IEEE Trans. Control Syst. Technol., 2002, 10, (3), pp. 460–468.
    48. 48)
      • 1. Baumann, B.M., Washington, G., Glenn, B.C., Rizzoni, G.: ‘Mechatronic design and control of hybrid electric vehicles’, IEEE/ASME Trans. Mechatron., 2000, 5, (1), pp. 58–72.
    49. 49)
      • 17. Ali, R., Yildiz, A.R., Kaya, N., Orhan, B., Alankus, O.B., Ozturk, F.: ‘Optimal design of vehicle components using topology design and optimization’, Int. J. Veh. Des., 2004, 34, (4), pp. 387–398.
    50. 50)
      • 20. http://www.volkspage.net/technik/ssp/ssp/SSP_230.pdf.
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