文献类型：会议论文

英文题名：Multi-Objective Optimization of HEV Transmission System Parameters Based on Immune Genetic Algorithm

作者：Tan Guangxing[1];Lin Cong[2];Bai Yuhe[1];Chen Zan[1]

机构：[1]Guangxi Univ Sci & Technol, Sch Elect & Informat Engn, Liuzhou 545006, Peoples R China;[2]Guangdong Ocean Univ, Coll Informat, Zhanjiang 524088, Peoples R China

会议论文集：IEEE International Conference on Communication Problem-Solving (ICCP)

会议日期：OCT 16-18, 2015

会议地点：Guilin, PEOPLES R CHINA

语种：英文

外文关键词：Multiobjective optimization - Genetic algorithms - Fuels - Parameter estimation - Vehicle transmissions - Fuel economy - Battery management systems - Charging (batteries)

外文摘要：In consideration of transmission system parameters impact on fuel economy and exhaust emissions of hybrid electric vehicle ( HEV), a multi-objective optimization scheme, immune genetic algorithm, is proposed in this paper for optimization of both transmission system parameters and control parameters of HEV. Therefore we establish a multi-objective optimal model where we consider transmission system parameters as variables, minimizing fuel consumption and exhaust emissions ( CO, HC and NOx) as optimization objectives, dynamic performance and balance in battery state of charge as constraint conditions. Meanwhile, we transform the multiple-objective functions into single-objective ones by weighting coefficients to realize optimization via immune genetic algorithm. Thus a combined optimization and simulation model is established by using real coding method and calling functions on ADVISOR background. Simulation results show that the proposed algorithm can effectively reduce fuel consumption and exhaust emissions of the vehicle.