Abstract
The coordination between the powertrain and control strategy has significant impacts on the operating performance of hybrid electric vehicles (HEVs). A comprehensive methodology based on Particle Swarm Optimization (PSO) is presented in this paper to achieve parameter optimization for both the powertrain and the control strategy, with the aim of reducing fuel consumption, exhaust emissions, and manufacturing costs of the HEV. The original multi-objective optimization problem is converted into a single-objective problem with a goal-attainment method, and the principal parameters of powertrain and control strategy are set as the optimized variables by PSO, with the dynamic performance index of HEVs being defined as the constraint condition. Computer simulations were carried out, which showed that the PSO scheme gives preferable results in comparison to the ADVISOR method. Therefore, fuel consumption and exhaust emissions of HEVs can be effectively reduced without sacrificing dynamic performance of HEVs.
Similar content being viewed by others
References
Antonio, P., Lucio, I., Vincen, G. and Alfredo, V. (2001). Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms. IEEE/ASME Int. Conf. Advanced Intelligent Mechatronics Proc.. Como, Italy, 434–439.
Chu, L., Li, Y. and Wang, Q. N. (2000). Study on the parametric optimization for a parallel hybrid electric vehicle powertrain. SAE Paper No. 2000-01-3109.
Delprat, S., Guerra, T. M., Paganelli, G., Lauber J. and Delhom M. (2001). Control strategy optimization for an hybrid parallel powertrain. Proc. American Control Conf., Arlington, USA, 1315–1320.
Ehsani, M., Rahman, K. M. and Toliyat, H. A. (1997). Propulsion system design of electric and hybrid vehicles. IEEE Trans. Industrial Electronics 44, 1, 19–27.
Ehsani, M., Gao, Y. and Butler, K. L. (1999). Application of electrically peaking hybrid (ELPH) propulsion system to a full-size passenger car with simulated design verification. IEEE Trans. Vehicular Technology 48, 6, 1779–1787.
Galdi, V., Ippoloto, L., Piccolo, A. and Vaccaro, A. (2001). A genetic-based methodology for hybrid electric vehicles sizing. Soft Computing, 6, 451–457.
Gembicki, F. W. and Haimes, Y. Y. (1975). Approach to performance and sensitivity multiobjective optimization: The goal attainment method. IEEE Trans. Automat. Control 20, 6, 769–771.
Johnson, V. H., Wipke, K. B. and Rausen, D. J. (2000). HEV control strategy for real-time optimization of fuel economy and emissions. SAE Paper No. 2000-01-1543.
Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. IEEE Int. Conf. Neutral Networks, Piscataway, USA, 1942–1948.
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.
Wipke, K., Markel, T. and Nelson, D. (2001). Optimizing energy management strategy and degree of Hybridization for a hydrogen fuel cell SUV. Proc. 18th Int. Electric Vehicle Symp., Berlin, Germany.
Zhu, Zh. L., Zhang, J. W. and Yin, CH. L. (2005). Optimization approach for hybrid electric vehicle powertrain design. Chinese J. Mechanical Engineering 15, 1, 30–36.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, J., Zhang, C.H. & Cui, N.X. PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. Int.J Automot. Technol. 9, 53–59 (2008). https://doi.org/10.1007/s12239-008-0007-8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-008-0007-8