Abstract
This paper presented the application of genetic algorithm (GA) and simulated annealing (SA) for parameter optimization of parallel hybrid electric vehicle (PHEV). The proper selection of optimal size of vehicle’s power train components not only improves its performance but also increases the fuel efficiency and hence enhances the cost-effectiveness. The parameter optimization of PHEV to achieve two long-range goals such as reduction of fuel consumption and toxic emission is a very challenging and interesting problem. The problem becomes a more realistic and difficult one due to the inclusion of lots of design variables, nonlinear constraints, etc. The performances of the above mentioned two optimization techniques have been compared and the simulation results demonstrate the superiority of GA over SA to solve the challenging constrained optimization problem in PHEV.
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Dey, S., Kamlu, S., Mishra, S.K. (2016). Optimization of Fuel Consumption and Emission for Hybrid Electric Vehicle. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_65
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DOI: https://doi.org/10.1007/978-81-322-2656-7_65
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