Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018 pp 361-368 | Cite as
Simultaneous Perturbation Stochastic Approximation Optimization for Energy Management Strategy of HEV
Abstract
This paper addresses optimization for hybrid electric vehicle (HEV). This project is using a single agent method to optimize the power losses under a specific driving cycle which is simultaneous perturbation stochastic approximation (SPSA) based method. For optimization process, four gain are added in four main parts of the HEV system. Those main parts are engine, motor, generator and battery. These four gain is controlled the output for each component to give the minimum power losses. The design method is applied to free model of HEV by using Simulink/MATLAB software while M-File/MATLAB is used to apply the SPSA method. The result from design method achieved a minimum reduction of power losses compared to original system. Thus, the comparison of result has been done to show the different before and after optimization.
Keywords
Simultaneous perturbation stochastic approximation (SPSA) OptimizationReferences
- 1.Ahmad, M.A.: Switching controller design for hybrid electric vehicles. SICE J. 7(5), 273–282 (2014)CrossRefGoogle Scholar
- 2.Wang, A.A.F.Q.: Plug-in HEV with CVT: configuration, control, and its concurrent multi-objective optimization by evolutionary algorithm. Int. J. Autom. Technol. 15(1), 103–115 (2014)CrossRefGoogle Scholar
- 3.Spall, J.C.: Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Autom. Control. 37(3), (1992)MathSciNetCrossRefGoogle Scholar
- 4.Bansal, H.O.: A review of optimal energy management strategies for hybrid electric vehicle. Int. J. Veh. Technol, (2014)Google Scholar
- 5.Ahmad, M.A.: Model free tuning of variable state of charge target of hybrid electric vehicles. The International Federation of Automatic Control, (2013)Google Scholar
- 6.Spall, J.C.: An overview of simultaneous perturbation method for efficient optimization, John Hopkins APL Technical Digest, 19(4), (1998)Google Scholar
- 7.Prokhorov, D.: Toyota Prius HEV neuro control, In: Proceedings of International Joint Conference on Neural Networks (2007)Google Scholar