Abstract
This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Assanis, D., Delagrammatikas, G., Fellini, R., Filipi, Z., Liedtke, J., Michelena, N., Papalambros, P., Reyes, D., Rosenbaum, D., Sales, A. and Sasena, M. (1996). An optimization approach to hybrid electric propulsion system design. SAE Paper No. 961660.
Dosthosseini, R., Kouzani, A. Z. and Sheikholeslam, F. (2011). Direct method for optimal power management in hybrid electric vehicles. Int. J. Automotive Technology 12,6, 943–950.
Han, Z., Yuan, Z., Guangyu, T., Quanshi, C. and Yaobin, C. (2004). Optimal energy management strategy for hybrid electric vehicles. SAE Paper No. 2004-01-0576.
Karaboga, D. and Akay, B. (2009). A comparative study of artificial bee colony algorithm. Elsevier Inc, Applied Mathematics and Computation, 214, 108–132.
Kim, N., Cha, S. and Peng, H. (2011). Optimal control of hybrid electric vehicles based on pontryagin’s minimum principle. IEEE Trans. Control System Technology 19,5, 1279–1287.
Lin, C. C., Peng, H., Grizzle, J. W. and Kang, J. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Trans. Control Sys. Technol. 11,6, 839–849.
Liu, J. and Peng, H. (2006). Control optimization for a power-split hybrid vehicle. American Control Conf., Minneapolis, Minnesota, 466–471.
Markel, T., Brooker, A., Hendricks, T., Johnson, V., Kelly, K., Kramer, B., O’Keefe, M., Sprik, S. and Wipke, K. (2002). ADVISOR: A systems analysis tool for advanced vehicle modeling. J. Power Sources, 110, 255–266.
Montazeri-Gh, M. and Poursamad, A. (2006). Appliacation of genetic algorithm for simultaneous optimization of HEV component sizing and control strategy. Int. J. Alternative Propultion 1,1, 63–78.
Moore, T. C. and Lovins, A. B. (1995). Vehicle design strategy to meet and exceed PNGV goals. SAE Paper No. 951906, RMI Publication, T95-27.
National Renewable Energy Laboratory (2001). Documentation. ADVISOR Software 3.2.
Namwook, K., Sukwon, C. and Huei, P. (2011). Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle. Control Systems Technology, IEEE Trans., 19, 1279–1287.
Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. and Zaidi, M. (2005). The Bees Algorithm. Technical Note. Manufacturing Engineering Centre. Cardiff University. UK.
Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. and Zaidi, M. (2006). The bees algorithm — A novel tool for complex optimisation problems. Proc. IPROMS Conf., 454–461.
Pu, J. H., Yin, C.-L. and Zhang, J.-W. (2005). Fuzzy torque control strategy for parallel hybrid electronic vehicles. Int. J. Automotive Technology 6,5, 529–536.
Srdjan, M. L. and Ali, E. (2004). Effects of drivetrain hybridization on fuel economy and dynamic performance of parallel hybrid electric vehicles. IEEE Trans. Vehicular Technology 53,2, 385–389.
Suh, B., Frank, A., Chung, Y. J., Lee, E. Y., Chang, Y. H. and Han, S. B. (2011). Powertrain system optimization for a heavy-duty hybrid electric bus. Int. J. Automotive Technology 12,1, 131–139.
Vadim, F. (1996). Global Methods in Optimal Control Theory. Marcel Dekker. New York. 140.
Wu, J., Zhang, C.-H. and Cui, N.-X. (2008). PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. Int. J. Automotive Technology 9,1, 53–69.
Yeniay, O. (2005). Penalty function methods for constrained optimization with genetic algorithms. Math. and Comp. Applications 10,1, 45–56.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Long, V.T., Nhan, N.V. Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles. Int.J Automot. Technol. 13, 1177–1183 (2012). https://doi.org/10.1007/s12239-012-0121-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-012-0121-5