Application of a Pheromone-Based Bees Algorithm as an Optimizer Within a Multidisciplinary Design Optimization System for Powertrain Component Sizing and Control Parameters for Hybrid E-Vehicles
International Journal of Transportation Engineering and Technology
Volume 1, Issue 1, December 2015, Pages: 1-9
Received: Jan. 29, 2016;
Accepted: Feb. 8, 2016;
Published: Feb. 26, 2016
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V. T. Long, Mechatronics Department, Nha Trang University, Khanh Hoa, Vietnam
M. S. Packianather, School of Engineering, Cardiff University, Cardiff, UK
This paper presents a Multidisciplinary Design Optimization (MDO) to optimize key component sizes and control strategy for a hybrid electric vehicle, Honda Insight 2000. A pheromone-based Bees Algorithm (PBA), where the food foraging behavior of honey bees combined with evolutionary computation, is used as an optimizer within a MDO system. The PBA uses pheromones, chemical substances secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species. The values of the key component size and control strategy parameters are adjusted according to PBA to obtain the minimization of Fuel Consumption (FC) while dynamic performances have to satisfy the Partnership for a New Generation of Vehicles (PNGV) constraints. In this research, ADVISOR software has been used as the simulation tool, where driving cycles, FTP and HWFET are employed to evaluate FC and dynamic performances. Following a description of the MDO system, the paper shows the results obtained for only the control strategy parameter optimization and the simultaneous optimization of key component sizes and control strategy parameters for the Honda Insight 2000. The results demonstrate the effectiveness of PBA when it is used as the optimizer within a MDO system for determining the optimal parameters of component sizes and control strategy resulting in the reduction of FC and improvement of vehicle performances. In this research, the new version, PBA, showed an improvement of about 20-25% over the Basic Bees Algorithm (BBA) in convergence speed with the nearly same results of optimization targets.
V. T. Long,
M. S. Packianather,
Application of a Pheromone-Based Bees Algorithm as an Optimizer Within a Multidisciplinary Design Optimization System for Powertrain Component Sizing and Control Parameters for Hybrid E-Vehicles, International Journal of Transportation Engineering and Technology.
Vol. 1, No. 1,
2015, pp. 1-9.
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