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

V. T. Long; M. S. Packianather

doi:doi:10.11648/j.ijtet.20150101.11

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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

V. T. Long, M. S. Packianather

Published in International Journal of Transportation Engineering and Technology (Volume 1, Issue 1)

Received: 29 January 2016 Accepted: 8 February 2016 Published: 26 February 2016

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Abstract

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.

Published in	International Journal of Transportation Engineering and Technology (Volume 1, Issue 1)
DOI	10.11648/j.ijtet.20150101.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Hybrid Electric Vehicles, Multidisciplinary Design Optimization, Basic Bees Algorithm, Pheromone-Based Bees Algorithm, Intelligent Optimization, HEV Control Strategy, Honda Insight 2000

References

[1]	ASSANIS, D., DELAGRAMMATIKAS, G., FELLINI, R., FILIPI, Z., LIEDTKE, J., MICHELENA, N., PAPALAMBROS, P., REYES, D., ROSENBAUM, D., SALES, A. AND SASENA, M., An optimization approach to hybrid electric propulsion system design, SAE Paper, (1996), 961660.
[2]	KELLY K. J., RAJAGOPALAN A., Benchmarking of OEM Hybrid Electric Vehicles at NREL, National Renewable Energy Laboratory, Golden, Colorado, USA (2001).
[3]	KELLY K. J., ZOLOT M., GLINSKY G. and HIERONYMUS A. (2001), “Test Results and Modeling of the Honda Insight Using ADVISOR”, SAE Future Transportation Technologies Conference, NREL/CP-540-31085.
[4]	MOORE, T. C. AND LOVINS A. B., Vehicle Design Strategy to Meet and Exceed PNGV Goals, SAE 951906, (1995), T95-27.
[5]	MARKEL, T., BROOKER, A., HENDRICKS, T., JOHNSON, V., KELLY, K., KRAMER, B., O’KEEFE, M., SPRIK, S. AND WIPKE, K.. ADVISOR: a systems analysis tool for advanced vehicle modelling, Journal of Power Sources, (2002), 110, 255–266.
[6]	MONTAZERI-GH, M. AND POURSAMAD, A.. Appliacation of Genetic Algorithm for Simultaneous Optimization of HEV Component Sizing and Control Strategy, Int. J. Alternative Propultion, (2006), 1, 1, 63-78.
[7]	National Renewable Energy Laboratory. Documentation, ADVISOR software 3.2 (2001).
[8]	LONG V. T., NHAN N. V. Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles, International Journal of Automotive Technology, (2012), 13, 7, 1177–1183.
[9]	LONG V. T., Application of Bees Algorithm for simultaneous optimisation of HEV key component sizes and control strategy, The 2nd international conference on automotive technology, engine and alternative fuels, ISBN: 978-604-73-1496-6, (2012), 37–43.
[10]	PACKIANATHER M. S., LANDY M., PHAM D. T., Enhancing the speed of the Bees Algorithm using Pheromone-based Recruitment, 7th IEEE International Conference on Industrial Informatics (INDIN 2009), (2009), 789-794.
[11]	PHAM, D. T., GHANBARZADEH, A., KOÇ, E., OTRI, S., RAHIM, S. AND ZAIDI, M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, (2005).
[12]	PHAM, D. T., GHANBARZADEH, A., KOÇ, E., OTRI, S., RAHIM, S. AND ZAIDI, M., The Bees Algorithm - A Novel Tool for Complex Optimisation Problems, Proceedings of IPROMS Conference, (2006), 454-461.
[13]	PU, J. H., YIN, C.-L. AND ZHANG, J.-W. Fuzzy torque control strategy for parallel hybrid electronic vehicles, Int. J. Automotive Technology, (2005), 6, 5, 529-536.
[14]	SALMASI F. R., Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends, IEEE Transaction on Vehicular Techonology, (2007), 56, 5, 2393-2404.
[15]	WU, J., ZHANG, C.-H. AND CUI, N.-X. PSO Algorithm-Based Parameter Optimization for HEV Powertrain and Its Control Strategy, Int. J. Automotive Technology,(2008), 9, 1, 53-69.
[16]	YENIAY, O. Penalty Function Methods for Constrained Optimization with Genetic Algorithms, Math. and Comp. Applications, (2005), 10, 1, 45-56.

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APA Style

V. T. Long, M. S. Packianather. (2016). 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, 1(1), 1-9. https://doi.org/10.11648/j.ijtet.20150101.11

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ACS Style

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. Int. J. Transp. Eng. Technol. 2016, 1(1), 1-9. doi: 10.11648/j.ijtet.20150101.11

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AMA Style

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. Int J Transp Eng Technol. 2016;1(1):1-9. doi: 10.11648/j.ijtet.20150101.11

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@article{10.11648/j.ijtet.20150101.11,
  author = {V. T. Long and M. S. Packianather},
  title = {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},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijtet.20150101.11},
  url = {https://doi.org/10.11648/j.ijtet.20150101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20150101.11},
  abstract = {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.},
 year = {2016}
}

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TY - JOUR
T1 - 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
AU - V. T. Long
AU - M. S. Packianather
Y1 - 2016/02/26
PY - 2016
N1 - https://doi.org/10.11648/j.ijtet.20150101.11
DO - 10.11648/j.ijtet.20150101.11
T2 - International Journal of Transportation Engineering and Technology
JF - International Journal of Transportation Engineering and Technology
JO - International Journal of Transportation Engineering and Technology
SP - 1
EP - 9
PB - Science Publishing Group
SN - 2575-1751
UR - https://doi.org/10.11648/j.ijtet.20150101.11
AB - 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.
VL - 1
IS - 1
ER -

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Author Information

V. T. Long

Mechatronics Department, Nha Trang University, Khanh Hoa, Vietnam
M. S. Packianather

School of Engineering, Cardiff University, Cardiff, UK

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APA Style

V. T. Long, M. S. Packianather. (2016). 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, 1(1), 1-9. https://doi.org/10.11648/j.ijtet.20150101.11

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ACS Style

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. Int. J. Transp. Eng. Technol. 2016, 1(1), 1-9. doi: 10.11648/j.ijtet.20150101.11

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AMA Style

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. Int J Transp Eng Technol. 2016;1(1):1-9. doi: 10.11648/j.ijtet.20150101.11

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@article{10.11648/j.ijtet.20150101.11,
  author = {V. T. Long and M. S. Packianather},
  title = {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},
  journal = {International Journal of Transportation Engineering and Technology},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijtet.20150101.11},
  url = {https://doi.org/10.11648/j.ijtet.20150101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20150101.11},
  abstract = {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.},
 year = {2016}
}

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