© The Institution of Engineering and Technology
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.
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-
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.
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-
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.
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-
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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.
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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.
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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.
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