Multi-Objective Optimization for Parameters of Energy Management Strategy of HEV Based on Improved NSGA-II

Abstract:

Hybrid Electric Vehicle (HEV) provides fairly high fuel economy with lower emissions compared to conventional vehicles. To enhance HEV performance in terms of fuel economy and emissions, subject to the satisfaction of driving performance, multi-objective optimization for parameters of energy management strategy is inevitable. Considering the defect of the method which transfers multi-objective optimization problem into that of single-objective and the shortage of the Pareto-optimum based nondominated sorting genetic algorithm II (NSGA-II), the NSGA-II has been improved and then applied to the optimization in this paper. The simulation results show that each run of the algorithm can produce many Pareto-optimal solutions and the satisfactory solution can be selected by decision-maker according to the requirement. The results also demonstrate the effectiveness of the approach.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 912-917
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.912
• Online since: August 2010
• Authors: Fei Hu, Zhi Guo Zhao
• Keywords: Energy Management Strategy of HEV, Improved NSGA-II, Multi-Objective Optimization (MOP)

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